Improving understanding of how water use efficiency (WUE), evapotranspiration (ET), and gross primary productivity (GPP) (CO2 exchange) vary across agricultural systems can help farmers better prepare for an uncertain future due to climate change by assessing water requirements for a crop as a function of current environmental conditions. This study: (a) quantified field-scale plant–water–carbon dynamics for silage maize (Zea mays L.) and alfalfa (Medicago sativa L.) crops – two dominant forage crops in southern Ontario, Canada; and (b) identified differences in plant carbon–water dynamics between these two crops, relating these differences to vegetation-driven ecosystem controls. Climate and soil properties were similar between the two study sites, and water availability was not limiting, suggesting that the overall temporal differences in carbon–water relations were driven by vegetation differences, mainly crop choice and management practices. Alfalfa had greater seasonal GPP, ET, and WUE than maize, due to a longer growing season. Differences in daily WUE between maize and alfalfa were driven by differences in GPP rather than ET. Multiple harvests reduced leaf-aging effects and promoted periods of rapid growth in alfalfa. In contrast, late seedling emergence and self-shading reduced GPP in maize. Under a warmer future climate, crop selection (i.e., perennial vs. annual), harvest regimes, and changes in growing season length should be considered when trying to manage for increased WUE. However, longer duration studies to validate these results are required to better address the impacts of climatic variability—especially antecedent conditions—to better inform future crop choices within a climate change context.

Abstract In the Canadian prairies, eutrophication is a widespread issue, with agriculture representing a major anthropogenic nutrient source in many watersheds. However, efforts to mitigate agricultural nutrient export are challenged by the lack of coordinated monitoring programs and the unique hydrological characteristics of the prairies, notably, the dominance of snowmelt in both water flows and nutrient loads, variable runoff, variable contributing area and the issues of understanding how scale affects nutrient concentrations and prevalence of dissolved nutrient transport (over total nutrients). Efforts are being made to integrate these characteristics in process-based water quality models, but the models are often complex and are not yet ready for use by watershed managers for prioritizing implementation of beneficial management practices (BMPs). In this study, a screening and scoping approach based on nutrient export coefficient modeling was used to prioritize BMPs for the 55,700 km2 Qu’Appelle Watershed, Saskatchewan. By integrating land use information, in-stream monitoring data, stakeholder input and nutrient export coefficient modeling, the study assessed potential efficiencies of six BMPs involving fertilizer, manure, grazing, crop and wetland management in nutrient load reductions for nine tributaries of the watershed. Uncertainty around the effectiveness of the BMPs was assessed. Field-level export coefficients were adjusted with nutrient delivery ratios for estimating watershed-level exports. Of the BMPs examined, in general, wetland restoration had the greatest potential to reduce both nitrogen and phosphorus loads in most tributaries, followed by fertilizer management. The importance of wetland restoration was supported by positive, significant, linear correlations between nutrient delivery ratios and drainage intensity in the tributaries (nitrogen: R 2 = 0.67; phosphorus: R 2 = 0.82). Notably, the relative ranking of BMP efficiencies varied with tributaries, as a result of differing landscape characteristics, land uses and nutrient inputs. In conclusion, the approach developed here acknowledges uncertainty, but provides a means to guide management decisions within the context of an adaptive management approach, where BMP implementation is partnered with monitoring and assessment to revise ongoing plans and ensures selected practices are meeting goals for nutrient abatement.

Best Management Practices (BMPs) incentive programs have been introduced to protect agricultural land and reduce nutrient runoff in watersheds. However, their voluntary nature has not led to the expected high participation rates. We examine influencing factors and underlying drivers that are associated with BMP adoption and farmer preferences for specific BMPs. Data are collected through an online survey in Ontario, Canada in 2019. A binary logit model is estimated to explain current participation in BMP schemes and a multinomial logit model to predict preferences for future BMP uptake. Results show that a mix of farmer and farm characteristics and environmental attitudes explain both current participation in BMP schemes and the likelihood of adopting a future BMP. Farmers tend to endorse a BMP if they currently implement that BMP. The findings furthermore suggest that increasing farmers' environmental awareness and sharing positive BMP experiences with other farmers may help expand future BMP adoption in Ontario. • We examine underlying drivers of farmer BMP adoption and preferences in Canada. • We inspect both current participation and future choices using logit models. • Farmers fairly concerned about water pollution are more likely to adopt BMPs. • Farmers tend to endorse a BMP if they currently implement that BMP. • Demographic characteristics are not significant predictors of future adoption.

The wildfire regime in Canada’s boreal region is changing; extended fire seasons are characterized by more frequent large fires (≥200 ha) burning greater areas of land, whilst climate-mediated drying is increasing the vulnerability of peatlands to deep burning. Proactive management strategies, such as fuel modification treatments, are necessary to reduce fire danger at the wildland-human interface (WHI). Novel approaches to fuel management are especially needed in peatlands where deep smouldering combustion is a challenge to suppression efforts and releases harmful emissions. Here, we integrate surface compression within conventional stand treatments to examine the potential for reducing smouldering of near-surface moss and peat. A linear model (adj. R2=0.62, p=2.2e-16) revealed that ground cover (F(2,101)=60.97, p<0.001) and compression (F(1,101)=56.46, p<0.001) had the greatest effects on smouldering potential, while stand treatment did not have a significant effect (F(3,101)=0.44, p=0.727). On average, compressed Sphagnum and feather moss plots showed 57.1% and 58.7% lower smouldering potential, respectively, when compared to uncompressed analogs. While practical evaluation is warranted to better understand the evolving effectiveness of this strategy, these findings demonstrate that a compression treatment can be successfully incorporated within both managed and unmanaged peatlands to reduce fire danger at the WHI.

Indigenous households are 90 times more likely to be without running water than non-Indigenous households in Canada. Current primary indicators of water quality and security for Indigenous Peoples are based on federal boil water advisories, which do not disaggregate at household levels to identify who is most at risk within or between communities. A mixed methods approach was used to assess the level of water insecurity and perceptions of water access by gender and age for a sample of households in Six Nations of the Grand River First Nations in Ontario, Canada. A household survey captured water security using the Household Water InSecurity Experiences (HWISE) scale and Likert-type responses to perceptions of water access, contextualized using semi-structured individual and group interviews. From 2019 to 2020, 66 households participated in the survey, 18 individuals participated in semi-structured individual interviews, and 7 individuals participated in 3 semi-structured group interviews. The survey sample demonstrated high levels of household water insecurity (57.5%, n = 38). Interviews revealed that women were more dissatisfied with their drinking water situations due to quality, source, and cost, though they shared water sharing as a coping strategy. Women faced more physical and mental barriers accessing water for their households, due to their roles as caretakers of their family and knowledge protectors for their communities. Generational divides were found in interviews about what qualified as "good water," with older participants understanding it as relating to traditional water sourcing, and younger participants wanting clean, accessible tap water. Taken together, the participants demonstrated a frustration with the sub-standard drinking water on reserve.

In Canada, Indigenous populations have an increased prevalence of psychiatric disorders and distress. Mental health mobile applications can provide effective, easy-to-access, and low-cost support. Examining grey literature and academic sources, this review found three mobile apps that support mental health for Indigenous communities in Canada. Implications and future directions are discussed.

Advances in scientific domains are led to an increase in the complexity of the experiments. To address this growing complexity, scientists from different domains require to work collaboratively. Scientific Workflow Management Systems (SWfMSs) are popular tools for data-intensive experiments. To the best of our knowledge, very few of the existing SWfMSs support collaboration, and it is not efficient in many cases. Researchers share a single version of the workflow in existing collaborative data analysis systems, which increases the chance of interference as the number of collaborators grows. Moreover, for effective collaboration, contributors require a clear view of the project's status, the information that existing SWfMSs do not provide. Another significant problem is most scientists are not capable of adding collaborative tools to existing SWfMSs, and they need software engineers to take on this responsibility. Even for software engineers such tasks could be challenging and time consuming. In this paper, we attempted to address this crucial issue in scientific workflow composition and doing so in a collaborative setting. Hence, we propose a tool to facilitate collaborative workflow composition. This tool provides branching and versioning, which are standard version control system features to allow multiple researchers to contribute to the project asynchronously. We also suggest some visualizations and a variety of reports to increase group awareness and help the scientists to realize the project's status and issues. As a proof of concept, we developed an API to capture the provenance data and provide collaborative tools. This API is developed as an example for software engineers to help them understand how to integrate collaborative tools into any SWfMS. We collect provenance information during workflow composition and then employ it to track workflow versions using the proposed collaborative tool. Prior to implementing the visualizations, we surveyed to discover how much the proposed visualizations could contribute to group awareness. Moreover, in the survey we investigated to what extent the proposed version control system could help address shortcomings in collaborative experiments. The survey participants provided us with valuable feedback. In future, we will use the survey responses to enhance the proposed version control system and visualizations.

Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers’ opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.

Exploring the source code of a software system is a prevailing task that is frequently done by contributors to a system. Practitioners often use call graphs to aid in understanding the source code of an inadequately documented software system. Call graphs, when visualized, show caller and callee relationships between functions. A static call graph provides an overall structure of a software system and dynamic call graphs generated from dynamic execution logs can be used to trace program behaviour for a particular scenario. Unfortunately a call graph of an entire system can be very complicated and hard to understand. Hierarchically abstracting a call graph can be used to summarize an entire system’s structure and more easily comprehending function calls. In this work, we mine concepts from source code entities (functions) to generate a concept cluster tree with improved naming of cluster nodes to complement existing studies and facilitate more effective program comprehension for developers. We apply three different information retrieval techniques (TFIDF, LDA, and LSI) on function names and function name variants to label the nodes of a concept cluster tree generated by clustering execution paths. From our experiment in comparing automatic labelling with manual labeling by participants for 12 use cases, we found that among the techniques on average, TFIDF performs better with 64% matching. LDA and LSI had 37% and 23% matching respectively. In addition, using the words in function name variants performed at least 5% better in participant ratings for all three techniques on average for the use cases.

Testing software is considered to be one of the most crucial phases in software development life cycle. Software bug fixing requires a significant amount of time and effort. A rich body of recent research explored ways to predict bugs in software artifacts using machine learning based techniques. For a reliable and trustworthy prediction, it is crucial to also consider the explainability aspects of such machine learning models. In this paper, we show how the feature transformation techniques can significantly improve the prediction accuracy and build confidence in building bug prediction models. We propose a novel approach for improved bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains the machine learning model using the transformed dataset. In our experiment with real-life bug datasets, the random forest and k-nearest neighbor classifier models that leveraged feature transformation showed 4.25% improvement in recall values on an average of over 8 software systems when compared to the models built on original data.

Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve their code-level problems. Usually, they include example code segments with their questions to explain the programming issues. When users of SO attempt to answer the questions, they prefer to reproduce the issues reported in questions using the given code segments. However, such code segments could not always reproduce the issues due to several unmet challenges (e.g., too short code segment) that might prevent questions from receiving appropriate and prompt solutions. A previous study produced a catalog of potential challenges that hinder the reproducibility of issues reported at SO questions. However, it is unknown how the practitioners (i.e., developers) perceive the challenge catalog. Understanding the developers’ perspective is inevitable to introduce interactive tool support that promotes reproducibility. We thus attempt to understand developers’ perspectives by surveying 53 users of SO. In particular, we attempt to – (1) see developers’ viewpoints on the agreement to those challenges, (2) find the potential impact of those challenges, (3) see how developers address them, and (4) determine and prioritize tool support needs. Survey results show that about 90% of participants agree to the already exposed challenges. However, they report some additional challenges (e.g., error log missing) that might prevent reproducibility. According to the participants, too short code segment and absence of required Class/Interface/Method from code segments severely prevent reproducibility, followed by missing important part of code. To promote reproducibility, participants strongly recommend introducing tool support that interacts with question submitters with suggestions for improving the code segments if the given code segments fail to reproduce the issues.

Software bug prediction is one of the promising research areas in software engineering. Software developers must allocate a reasonable amount of time and resources to test and debug the developed software extensively to improve software quality. However, it is not always possible to test software thoroughly with limited time and resources to develop high quality software. Sometimes software companies release software products in a hurry to make profit in a competitive environment. As a result the released software might have software defects and can affect the reputation of those software companies. Ideally, any software application that is already in the market should not contain bugs. If it does, depending on its severity, it might cause a great cost. Although a significant amount of work has been done to automate different parts of testing to detect bugs, fixing a bug after it is discovered is still a costly task that developers need to do. Sometimes these bug fixing changes introduce new bugs in the system. Researchers estimated that 80% of the total cost of a software system is spent on fixing bugs [8]. They show that the software faults and failures costs the US economy $59.5 billion a year [9].

AbstractLet M be a two-dimensional table with each cell weighted by a nonzero positive number. A StreamTable visualization of M represents the columns as non-overlapping vertical streams and the rows as horizontal stripes such that the intersection between a stream and a stripe is a rectangle with area equal to the weight of the corresponding cell. To avoid large wiggle of the streams, it is desirable to keep the consecutive cells in a stream to be adjacent. Let B be the smallest axis-aligned bounding box containing the StreamTable. Then the difference between the area of B and the sum of the weights is referred to as the excess area. We attempt to optimize various StreamTable aesthetics (e.g., minimizing excess area, or maximizing cell adjacencies in streams). If the row permutation is fixed and the row heights are given, then we give an O(rc)-time algorithm to optimizes these aesthetics, where r and c are the number of rows and columns, respectively. If the row permutation is fixed but the row heights can be chosen, then we discuss a technique to compute an aesthetic (but not necessarily optimal) StreamTable by solving a quadratically-constrained quadratic program, followed by iterative improvements. If the row heights are restricted to be integers, then we prove the problem to be NP-hard. If the row permutations can be chosen, then we show that it is NP-hard to find a row permutation that optimizes the area or adjacency aesthetics. KeywordsGeometric AlgorithmsTable CartogramStreamgraphs

Software development is largely dependent on libraries to reuse existing functionalities instead of reinventing the wheel. Software developers often need to find analogical libraries (libraries similar to ones they are already familiar with) as an analogical library may offer improved or additional features. Developers also need to search for analogical libraries across programming languages when developing applications in different languages or for different platforms. However, manually searching for analogical libraries is a time-consuming and difficult task. This paper presents a technique, called XLibRec, that recommends analogical libraries across different programming languages. XLibRec collects Stack Overflow question titles containing library names, library usage information from Stack Overflow posts, and library descriptions from a third party website, Libraries.io. We generate word-vectors for each information and calculate a weight-based cosine similarity score from them to recommend analogical libraries. We performed an extensive evaluation using a large number of analogical libraries across four different programming languages. Results from our evaluation show that the proposed technique can recommend cross-language analogical libraries with great accuracy. The precision for the Top-3 recommendations ranges from 62-81% and has achieved 8-45% higher precision than the state-of-the-art technique.

A software release note is one of the essential documents in the software development life cycle. The software release contains a set of information, e.g., bug fixes and security fixes. Release notes are used in different phases, e.g., requirement engineering, software testing and release management. Different types of practitioners (e.g., project managers and clients) get benefited from the release notes to understand the overview of the latest release. As a result, several studies have been done about release notes production and usage in practice. However, two significant problems (e.g., duplication and inconsistency in release notes contents) exist in producing well-written & well-structured release notes and organizing appropriate information regarding different targeted users' needs. For that reason, practitioners face difficulties in writing and reading the release notes using existing tools. To mitigate these problems, we execute two different studies in our paper. First, we execute an exploratory study by analyzing 3,347 release notes of 21 GitHub repositories to understand the documented contents of the release notes. As a result, we find relevant key artifacts, e.g., issues (29%), pull-requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%) in the release note contents. Second, we conduct a survey study with 32 professionals to understand the key information that is included in release notes regarding users' roles. For example, project managers are more interested in learning about new features than less critical bug fixes. Our study can guide future research directions to help practitioners produce the release notes with relevant content and improve the documentation quality.

Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.

The susceptible-infected-recovered (SIR) model is perhaps the most basic epidemiological model for the evolution of disease spread within a population. Because of its direct representation of fundamental physical quantities, a true solution to an SIR model possesses a number of qualitative properties, such as conservation of the total population or positivity or monotonicity of its constituent populations, that may only be guaranteed to hold numerically under step-size restrictions on the solver. Operator-splitting methods with order greater than two require backward sub-steps in each operator, and the effects of these backward sub-steps on the step-size restrictions for guarantees of qualitative correctness of numerical solutions are not well studied. In this study, we analyze the impact of backward steps on step-size restrictions for guaranteed qualitative properties by applying third- and fourth-order operator-splitting methods to the SIR epidemic model. We find that it is possible to provide step-size restrictions that guarantee qualitative property preservation of the numerical solution despite the negative sub-steps, but care must be taken in the choice of the method. Results such as this open the door for the design and application of high-order operator-splitting methods to other mathematical models in general for which qualitative property preservation is important.

This study was conducted at an oil sands operation in the Athabasca Oil Sands Region (AOSR), northeastern Alberta, Canada. The mine comprises open pit excavation of bituminous sands at two sites (Mildred Lake, ML, and Aurora North, AN), with a single hot-water extraction circuit connecting extraction plants at each mine. Water samples were collected and analyzed regularly over an eight-year period to establish inventories of site-wide water isotope signatures including seasonal and interannual changes in the recycle water circuit, and to permit future application of an isotope balance model to constrain poorly quantified processes such as evaporation losses, dewatering of tailings, and tailings pond connectivity of the recycle water circuit. Sampling of precipitation inputs over an 8-year period was used to constrain a local meteoric water line for the area. Differences in evaporative isotopic enrichment of tailings ponds at ML and AN are attributed to use of Athabasca River makeup water at the former site versus basal dewatering sources at the latter, with similar atmospheric controls at both. A conceptual model is developed summarizing temporal variations in water balance and isotopic signatures within the recycle water circuit, including accurate simulation of the unique isotopic enrichment of cooling tower blowdown. This study provides foundational evidence for application of stable isotope mass balance to monitor and improve industrial water use efficiency and management. • Detailed summary of stable isotope variations at oil sands mine sites. • New dataset for precipitation, makeup water, and mine circuits. • Updated regressions defining local meteoric water line for district. • Contrasts isotopic variations for nearby mine sites with distinct sources. • Previously unpublished effects of cooling tower blowdown.

The lower Athabasca River was used as a test case using total suspended sediment, chloride and vanadium as the model variables. Upstream model boundary conditions included water from the tributary Clearwater River (right stream tube) and the upper Athabasca River extending upstream of the tributary mouth (left stream tube). This model will be extended to include the Peace-Athabasca Delta (PAD), to determine the implications of mining outfall discharges on a large region of the Athabasca – PAD region. A novel, quasi-two-dimensional surface water-quality modelling approach is presented in which the model domain can be discretised in two dimensions, but a one-dimension solver can still be applied to capture water flow between the discretisation units (segments). The approach requires a river reach to be divided into two stream tubes, along the left and right river sides, with flows exchanging through the segments longitudinally and also laterally between adjacent segments along the two streams. The new method allows the transverse mixing of tributary and outfall water of different constituent concentrations to be simulated along the course of the river. Additional diffuse loading of dissolved vanadium could be determined from the model’s substance balance. A scenario was then simulated in which the transport and fate of vanadium in a floodplain lake and a secondary channel was determined. • Quasi-2D modelling approach proves to be viable for transverse mixing. • Quasi-2D approach allows secondary channels and side lakes to be modelled. • Quasi-2D approach is appropriate to scale up to entire lower Athabasca River reach. • The approach allowed a diffuse loading of dissolved vanadium to be quantified.

This paper synthesizes Canada's environmental valuation literature over the last six decades. Focusing on primary valuation benefit estimates, we link multiple research outputs from the same data collection effort to obtain an accurate measure of unique studies. We identify a total of 269 unique valuation studies conducted in Canada between 1964 and 2019. The number of valuation studies conducted per year has not increased since 1975 and the median data collection year is 1996. Stated preference (SP) methods are the most popular valuation approaches being used in more than 50% of studies and this share has increased to over 80% within the last decade. We discuss numerous gaps in our knowledge for certain environmental resources and regions, in particular Canada's three Northern territories. The paper provides information on the state of environmental valuation research in Canada and identifies future research needs.

A discrete choice experiment was conducted on the non-use value of avoiding climate impacts to coral reefs. • The Northwestern Hawaiian Islands coral reefs were utilized as a case study site. • Decreasing coral cover and fish numbers causes large welfare losses. • Declines to coral health and fish species diversity lead to moderate welfare losses. • Choice behaviour is compared between US mainland and Hawaiian residents. Global climate change is leading to rapid deteriorations of the health and productivity of coral reefs. There is limited research on the associated human welfare implications, particularly in terms of the non-use values that people hold for coral reefs. We examine climate related changes in non-use values of coral health, coral cover, water clarity, fish numbers, fish species diversity and presence of turtles. Using a discrete choice experiment conducted among 1,369 Hawaiian and US mainland residents, we find that climate change induced declines in coral cover and fish numbers result in large welfare losses; whereas, declines in coral health and fish species diversity lead to moderate welfare losses. Deterioration in water clarity results in large welfare losses for US mainland residents but relatively smaller losses for Hawaiian residents. On aggregate, differences in welfare estimates for the US mainland and Hawaii sample are minor. However, we find significant differences in the underlying determinants of willingness-to-pay for partial climate change mitigation including income and beliefs in the need to mitigate climate change. The paper concludes with some recommendations for policy on the basis of these findings.

DNA metabarcoding can provide a high-throughput and rapid method for characterizing responses of communities to environmental stressors. However, within bulk samples, DNA metabarcoding hardly distinguishes live from the dead organisms. Here, both DNA and RNA metabarcoding were applied and compared in experimental freshwater mesocosms conducted for assessment of ecotoxicological responses of zooplankton communities to remediation treatment until 38 days post oil-spill. Furthermore, a novel indicator of normalized vitality (NV), sequence counts of RNA metabarcoding normalized by that of DNA metabarcoding, was developed for assessment of ecological responses. DNA and RNA metabarcoding detected similar taxa richness and rank of relative abundances. Both DNA and RNA metabarcoding demonstrated slight shifts in measured α-diversities in response to treatments. NV presented relatively greater magnitudes of differential responses of community compositions to treatments compared to DNA or RNA metabarcoding. NV declined from the start of the experiment (3 days pre-spill) to the end (38 days post-spill). NV also differed between Rotifer and Arthropoda, possibly due to differential life histories and sizes of organisms. NV could be a useful indicator for characterizing ecological responses to anthropogenic influence; however, the biology of target organisms and subsequent RNA production need to be considered. • RNA normalized by DNA metabarcoding functions as normalized vitality. • Normalized vitality reflected temporal dynamics of zooplankton communities. • Normalized vitality revealed greater community differences between treatments. • Rotifer had greatest normalized vitality compared to Arthropoda.

Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.

Abstract Wastewater-based surveillance (WBS) has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Quantities of viral fragments of SARS-CoV-2 in wastewater are related to numbers of clinical cases of COVID-19 reported within the corresponding sewershed. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) or sequencing. A multiplex RT-qPCR assay to detect and estimate the prevalence of multiple VOCs, including Omicron/Alpha, Beta, Gamma, and Delta, in wastewater RNA extracts was developed and validated. The probe-based multiplex assay, named “N200” focuses on amino acids 199-202, a region of the N gene that contains several mutations that are associated with variants of SARS- CoV-2 within a single amplicon. Each of the probes in the N200 assay are specific to the targeted mutations and worked equally well in single- and multi-plex modes. To estimate prevalence of each VOC, the abundance of the targeted mutation was compared with a non- mutated region within the same amplified region. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from six sewersheds in Ontario, Canada collected between December 1, 2021, and January 4, 2022. Using the N200 assay, the replacement of the Delta variant along with the introduction and rapid dominance of the Omicron variant were monitored in near real-time, as they occurred nearly simultaneously at all six locations. The N200 assay is robust and efficient for wastewater surveillance can be adopted into VOC monitoring programs or replace more laborious assays currently being used to monitor SARS- CoV-2 and its VOCs.

Abstract Wastewater monitoring of SARS-CoV-2 allows for early detection and monitoring of COVID-19 burden in communities and can track specific variants of concern. Targeted assays enabled relative proportions of SARS-CoV-2 Omicron and Delta variants to be determined across 30 municipalities covering >75% of the province of Alberta (pop. 4.5M) in Canada, from November 2021 to January 2022. Larger cities like Calgary and Edmonton exhibited a more rapid emergence of Omicron relative to smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a more remote northern city with a large fly-in worker population. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden prior to the observed increase in newly diagnosed clinical cases throughout Alberta, which peaked two weeks later. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of emerging pathogens including SARS-CoV-2 variants.

Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.

Permafrost plays an important role in the hydrology of arctic/subarctic regions. However, permafrost thaw/degradation has been observed over recent decades in the Northern Hemisphere and is projected to accelerate. Hence, understanding the evolution of permafrost areas is urgently needed. Land surface models (LSMs) are well-suited for predicting permafrost dynamics due to their physical basis and large-scale applicability. However, LSM application is challenging because of the large number of model parameters and the complex memory of state variables. Significant interactions among the underlying processes and the paucity of observations of thermal/hydraulic regimes add further difficulty. This study addresses the challenges of LSM application by evaluating the uncertainty due to meteorological forcing, assessing the sensitivity of simulated permafrost dynamics to LSM parameters, and highlighting issues of parameter identifiability. Modelling experiments are implemented using the MESH-CLASS framework. The VARS sensitivity analysis and traditional threshold-based identifiability analysis are used to assess various aspects of permafrost dynamics for three regions within the Mackenzie River Basin. The study shows that the modeller may face significant trade-offs when choosing a forcing dataset as some datasets enable the representation of some aspects of permafrost dynamics, while being inadequate for others. The results also emphasize the high sensitivity of various aspects of permafrost simulation to parameters controlling surface insulation and soil texture; a detailed list of influential parameters is presented. Identifiability analysis reveals that many of the most influential parameters for permafrost simulation are unidentifiable. These conclusions will hopefully inform future efforts in data collection and model parametrization.

Abstract. Northern peatlands cover approximately four million km2, and about half of these peatlands are estimated to contain permafrost and periglacial landforms, like palsas and peat plateaux. In northeastern Canada, peatland permafrost is predicted to be spatially concentrated in the western interior of Labrador and largely absent along the Labrador Sea and Gulf of St. Lawrence coastline. However, the paucity of observations of peatland permafrost in the interior coupled with ongoing use of perennially frozen peatlands along the coast by Labrador Inuit and Innu cast doubt on the reliability of existing maps of peatland permafrost distribution in the region. In this study, we develop a multi-stage consensus-based inventory of peatland permafrost complexes in coastal Labrador and adjacent parts of Quebec using high-resolution satellite imagery and validate it with extensive field visits and low-altitude aerial photography and videography. A total of 1885 wetland complexes were inventoried, of which 1023 were interpreted as likely containing peatland permafrost. Likely peatland permafrost complexes were mostly found in lowlands within 40 km of the coastline where mean annual air temperatures of up to +1.2 °C are recorded. Evaluation of the geographic distribution of peatland permafrost complexes reveals a clear gradient from the outer coasts, where peatland permafrost is more abundant, to inland peatlands, where permafrost is generally absent. This coastal gradient may be attributed to a combination of climatic and geomorphological influences which lead to lower insolation, thinner snowpacks, and more frost-susceptible materials along the coast. The results of this study also suggest that existing maps of permafrost distribution for southeastern Labrador require adjustment to better reflect the abundance of peatland permafrost complexes which are located to the south of the regional sporadic discontinuous permafrost limit. This study constitutes the first dedicated peatland permafrost inventory for Labrador, and our results provide an important baseline for future mapping, modelling, and climate change adaptation strategy development in the region.

Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parametrisations to model human dam operations, the representation of reservoir regulation is often still lacking in Earth System Models. Here we implement and evaluate a widely used reservoir parametrisation in the global river routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs, and which is currently being coupled to an Earth System Model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing to simulations ignoring inland waters, and simulations with reservoirs represented as natural lakes, using (i) local simulations for 26 individual reservoirs driven by observed inflows, and (ii) global-scale simulations driven by runoff from the Community Land Model. The local simulations show a clear added value of the reservoir parametrisation, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-scale application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but compared to the natural lake parametrisation, an overall similar performance is found. This lack of impact could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that the inclusion of dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test water management parametrisations, as well as to improve runoff simulations for advancing the representation of anthropogenic interference with the terrestrial water cycle in Earth System Models.

Abstract. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned area detection algorithm between 2001–2019 across Alaska and Canada at 500 meters (m) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned area estimates. Using this new burned area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely-sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37 million hectares (Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 +/- 27.96 (+/- 1 standard deviation) Teragrams of carbon (C) per year, with a mean combustion rate of 3.13 +/- 1.17 kilograms C m-2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger fire years and later season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion data sets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local to continental-scale applications of boreal fire science.

Abstract. Ice thickness across lake ice is influenced mainly by the presence of snow and its distribution, as it directly impacts the rate of lake ice growth. The spatial distribution of snow depth over lake ice varies and is driven by wind redistribution and snowpack metamorphism, creating variability in the lake ice thickness. The accuracy and consistency of snow depth measurement data on lake ice are challenging and sparse to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models. Such information is required to improve the knowledge and understanding of snow depth distribution over lake ice. This study maps snow depth distribution over lake ice using ground-penetrating radar (GPR) two-way travel-time (TWT) with ~9 cm spatial resolution along transects totalling ~44 km over four freshwater lakes in Canada’s sub-arctic. The accuracy of the snow depth retrieval is assessed using in situ snow depth observations (n =2,430). On average, the snow depth derived from GPR TWTs for the early winter season is estimated with a root mean square error (RMSE) of 1.58 cm and a mean bias error of -0.01 cm. For the late winter season on a deeper snowpack, the accuracy is estimated with RMSE of 2.86 cm and a mean bias error of 0.41 cm. The GPR-derived snow depths are interpolated to create 1 m spatial resolution snow depth maps. Overall, this study improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information, which is essential for the lake ice modelling community.

Due to their relatively large production and few restrictions on uses, novel substitutes for historically used per and poly-fluoroalkyl substances (PFAS) are being used and accumulating in the environment. However, due to a lack of information on their toxicological properties their hazards and risks are hard to estimate. Before fertilization, oocytes of two salmonid species, Arctic Char (Salvelinus alpinus) and Rainbow Trout (Oncorhynchus mykiss), were exposed to three PFAS substances used as substitutes for traditional PFAS, PFBA, PFBS or GenX or two archetypical, historically used, longer-chain PFAS, PFOA and PFOS. Exposed oocytes were subsequently fertilized, incubated and were sampled during several developmental stages, until swim-up. All five PFAS were accumulated into egg yolks with similar absorption rates, and their concentrations in egg yolks were less than respective concentrations in/on egg chorions. Rapid elimination of the five PFAS was observed during the first 3 days after fertilization. Thereafter, amounts of PFOS and PFOA were stable until swim-up, while PFBA, PFBS and GenX were further eliminated during development from one month after the fertilization to swim-up. In these two salmonid species, PFBA, PFBS and GenX were eliminated faster than were PFOS or PFOA.

The microbiome of the gut is vital for homeostasis of hosts with its ability to detoxify and activate toxicants, as well as signal to the immune and nervous systems. However, in the field of environmental toxicology, the gut microbiome has only recently been identified as a measurable indicator for exposure to environmental pollutants. Antidepressants found in effluents of wastewater treatment plants and surface waters have been shown to exhibit antibacterial-like properties in vitro, where some bacteria are known to express homologous proteins that bind antidepressants in vertebrates. Therefore, it has been hypothesized that exposure to antidepressant drugs might affect gut microbiota of aquatic organisms. In this study, the common antidepressant, fluoxetine, was investigated to determine whether it can modulate the gut microbiome of adult fathead minnows. A 28-day, sub-chronic, static renewal exposure was performed with nominal fluoxetine concentrations of 0.01, 10 or 100 μg/L. Using 16S rRNA amplicon sequencing, shifts among the gut-associated microbiota were observed in individuals exposed to the greatest concentration, with greater effects observed in females. These changes were associated with a decrease in relative proportions of commensal bacteria, which can be important for health of fish including bacteria essential for fatty acid oxidation, and an increase in relative proportions of pathogenic bacteria associated with inflammation. Results demonstrate, for the first time, how antidepressants found in some aquatic environments can influence gut microbiota of fishes.

There are no standardized protocols for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater to date, especially for population normalization. Here, a pipeline was developed, applied, and assessed to quantify SARS-CoV-2 and key variants of concern (VOCs) RNA in wastewater at Saskatoon, Canada. Normalization approaches using recovery ratio and extraction efficiency, wastewater parameters, or population indicators were assessed by comparing to daily numbers of new cases. Viral load was positively correlated with daily new cases reported in the sewershed. Wastewater surveillance (WS) had a lead time of approximately 7 days, which indicated surges in the number of new cases. WS revealed the variant α and δ driving the third and fourth wave, respectively. The adjustment with the recovery ratio and extraction efficiency improved the correlation between viral load and daily new cases. Normalization of viral concentration to concentrations of the artificial sweetener acesulfame K improved the trend of viral load during the Christmas and New Year holidays when populations were dynamic and variable. Acesulfame K performed better than pepper mild mottle virus, creatinine, and ammonia for population normalization. Hence, quality controls to characterize recovery ratios and extraction efficiencies and population normalization with acesulfame are promising for precise WS programs supporting decision-making in public health.

The neurotoxic alkaloid β-N-methyl-amino-l-alanine (BMAA) and related isomers, including N-(2-aminoethyl glycine) (AEG), β-amino-N-methyl alanine (BAMA), and 2,4-diaminobutyric acid (DAB), have been reported previously in cyanobacterial samples. However, there are conflicting reports regarding their occurrence in surface waters. In this study, we evaluated the impact of amending lake water samples with trichloroacetic acid (0.1 M TCA) on the detection of BMAA isomers, compared with pre-existing protocols. A sensitive instrumental method was enlisted for the survey, with limits of detection in the range of 5-10 ng L-1. Higher detection rates and significantly greater levels (paired Wilcoxon's signed-rank tests, p < 0.001) of BMAA isomers were observed in TCA-amended samples (method B) compared to samples without TCA (method A). The overall range of B/A ratios was 0.67-8.25 for AEG (up to +725%) and 0.69-15.5 for DAB (up to +1450%), with absolute concentration increases in TCA-amended samples of up to +15,000 ng L-1 for AEG and +650 ng L-1 for DAB. We also documented the trends in the occurrence of BMAA isomers for a large breadth of field-collected lakes from Brazil, Canada, France, Mexico, and the United Kingdom. Data gathered during this overarching campaign (overall, n = 390 within 45 lake sampling sites) indicated frequent detections of AEG and DAB isomers, with detection rates of 30% and 43% and maximum levels of 19,000 ng L-1 and 1100 ng L-1, respectively. In contrast, BAMA was found in less than 8% of the water samples, and BMAA was not found in any sample. These results support the analyses of free-living cyanobacteria, wherein BMAA was often reported at concentrations of 2-4 orders of magnitude lower than AEG and DAB. Seasonal measurements conducted at two bloom-impacted lakes indicated limited correlations of BMAA isomers with total microcystins or chlorophyll-a, which deserves further investigation.

Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluorometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014–2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration, including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance metrics; accuracy (lowest error) and reliability (% fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35%), whereas that using environmental factors was most reliable (89% within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30% decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation–emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.

Stable Fe isotopes have only recently been measured in freshwater systems, mainly in meromictic lakes. Here we report the δ56Fe of dissolved, particulate, and sediment Fe in two small dimictic boreal shield headwater lakes: manipulated eutrophic Lake 227, with annual cyanobacterial blooms, and unmanipulated oligotrophic Lake 442. Within the lakes, the range in δ56Fe is large (ca. -0.9 to +1.8‰), spanning more than half the entire range of natural Earth surface samples. Two layers in the water column with distinctive δ56Fe of dissolved (dis) and particulate (spm) Fe were observed, despite differences in trophic states. In the epilimnia of both lakes, a large Δ56Fedis-spm fractionation of 0.4-1‰ between dissolved and particulate Fe was only observed during cyanobacterial blooms in Lake 227, possibly regulated by selective biological uptake of isotopically light Fe by cyanobacteria. In the anoxic layers in both lakes, upward flux from sediments dominates the dissolved Fe pool with an apparent Δ56Fedis-spm fractionation of -2.2 to -0.6‰. Large Δ56Fedis-spm and previously published metagenome sequence data suggest active Fe cycling processes in anoxic layers, such as microaerophilic Fe(II) oxidation or photoferrotrophy, could regulate biogeochemical cycling. Large fractionation of stable Fe isotopes in these lakes provides a potential tool to probe Fe cycling and the acquisition of Fe by cyanobacteria, with relevance for understanding biogeochemical cycling of Earth's early ferruginous oceans.

With ongoing global warming and permafrost thawing, weathering processes will change on the Yukon River, with risks for water quality and ecosystem sustainability. Here, we explore the relationship between weathering processes and permafrost cover using elemental concentration and strontium and lithium isotopic data in the dissolved load of 102 samples collected during the summer across most major tributaries of the Yukon River. The Yukon River basin is dominated by silicate weathering with a high contribution from young volcanic rock units. In glaciated mountainous zones, we observe higher carbonate weathering contribution, low Li/Na ratios and low δ 7 Li values (<15‰). In these areas, the high denudation rate and high supply of fresh minerals associated with alpine glaciers favor congruent silicate weathering, and sulfide oxidation accelerates carbonate weathering. In floodplains covered by continuous permafrost, we observe a high carbonate weathering contribution, relatively high Li/Na ratios, and low δ 7 Li values (∼18‰). We argue that the minimal water–rock interactions in this setting inhibit silicate weathering and favor congruent weathering of easily weatherable minerals (i.e., carbonates). Conversely, in areas with discontinuous or sporadic permafrost, we observe a dominance of silicate weathering, with higher and more variable Li/Na ratios and high δ 7 Li values (11–33‰). In this setting, longer water–rock interactions combined with the high supply of fresh minerals from mountain zones favor more incongruent weathering. The unique history of Pleistocene glaciations on the Yukon River basin also influences weathering processes. Many areas of the basin were never glaciated during the Pleistocene, and rivers draining those regions have higher δ 7 Li values suggesting more incongruent weathering associated with deeper flow paths and longer water residence time in the regolith. Our work underlines that water–rock interactions, including active layer weathering and groundwater inputs, are highly dependent on climate conditions and glacial processes across the Yukon River basin, with key implications for future water quality in this warming basin.

In cold water (temperatures between water's freezing point and the temperature of maximum density), near-surface heating (from the sun) generates dense water which in turn induces vertical currents. If there is a near-surface current, the resulting convective instabilities efficiently move momentum from the current to regions lower in the water column. Then, there is an induced momentum flux across the plume boundary leading to a complicated series of three-dimensional interactions resulting in turbulence. How might this process be affected by factors such as water clarity and current speed?

Resource development and climate change are increasing concerns regarding safe water for Indigenous people in Canada. A research study was completed to characterize the consumption of water and beverages prepared with water and identify the perception of water consumption in Indigenous communities from the Northwest Territories and Yukon, Canada. As part of a larger research program, data for this study were available from a 24-hour recall dietary survey ( n = 162), a health messages survey ( n = 150), and an exposure factor survey ( n = 63). A focus group was conducted with Elders in an on-the-land camp setting. The consumption of water-based beverages in winter was 0.9 L/day on average, mainly consisting of tea and coffee. Of the 81% of respondents who reported consuming water-based beverages in the previous 24 hours of the survey, 33% drank more bottled water than tap water. About 2% of respondents consumed water from the land (during the winter season). Chlorine smell was the main limiting factor reported to the consumption of tap water. Results from the focus group indicated that Indigenous knowledge might impact both the perception and consumption of water. These findings aim to support public health efforts to enable people to make water their drink of choice.

Identification of integrated models is still hindered by submodels’ uncertainty propagation. In this article, a novel identifiability and identification framework is applied to screen and establish reasonable hypotheses of an integrated instream (WASP) and catchment water quality (VENSIM) model. Using the framework, the models were linked, and critical parameters and processes identified. First, an ensemble of catchment nutrient loads was simulated with randomized parameter settings of the catchment processes (e.g. nutrient decay rates). A second Monte Carlo analysis was then staged with randomized loadings and parameter values mimicking insteam processes (e.g. algae growth). The most significant parameters and their processes were identified. This coupling of models for a two-step global sensitivity analysis is a novel approach to integrated catchment-scale water quality model identification. Catchment processes were, overall, more significant to the river’s water quality than the instream processes of this Prairie river system investigated (Qu’Appelle River).

• Model benchmarking was performed using four different meteorological forcing data. • Calculation of water balance revealed the dominant hydrological processes. • Hydrological conditions under future climatic conditions were assessed. • Uncertainty in future flow projections were quantified. Climate change introduces substantial uncertainty in water resources planning and management. This is particularly the case for the river systems in the high latitudes of the Northern Hemisphere that are more vulnerable to global change. The situation becomes more challenging when there is a limited hydrological understanding of the basin. In this study, we assessed the impacts of future climate on the hydrology of the Saint John River Basin (SJRB), which is an important transboundary coastal river basin in northeastern North America. We also additionally performed model benchmarking for the SJRB using four different meteorological forcing datasets. Using the best performing forcing data and model parameters, we studied the water balance of the basin. Our results show that meteorological forcing data play a pivotal role in model performance and therefore can introduce a large degree of uncertainty in hydrological modelling. The analysis of the water balance highlights that runoff and evapotranspiration account for about 99% of the total basin precipitation, with each constituting approximately 50%. The simulation of future flows projects higher winter discharges, but summer flows are estimated to decrease in the 2041–2070 and 2071–2100 periods compared to the baseline period (1991–2020). However, the evaluation of model errors indicates higher confidence in the result that future winter flows will increase, but lower confidence in the results that future summer flows will decrease.

• Comprehensive and extended review on probabilistic methods for hydroclimatic extremes. • Synthesis of methods used in analyses of extremes in precipitation, streamflow and temperature. • Over 20 probability distribution estimation methods in 25 comparative studies reviewed. • Identification of most promising contemporary probabilistic methods. Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.

The Grand River (GR) extends throughout the majority of Southern Ontario with its final outlet at Lake Erie and accommodates thirty wastewater treatment plants (WWTP) with varied filtration processes. Many WWTPs are unable to effectively eliminate several contaminants of concern (CECs) from final released effluent, leading to measurable concentrations in surface waters and ultimately chronically exposing aquatic species to mixed CECs. Exposures to CECs have reported impacts on oxidative stress, measurable through reactive oxygen species (ROS) and the antioxidant defense response. This research focuses on the effects of WWTP effluent on four Etheostoma (Darter) species endemic to the GR. Objectives of this study examined if any oxidative stress markers are present in darter brains downstream from the effluent release point compared to an upstream reference site relative to the Waterloo, ON WWTP across two separate years (Fall 2020 and 2021). This was assessed using transcriptional and enzyme analysis of antioxidant enzymes (SOD, GPX, CAT) and an enzyme involved in serotonin synthesis. In fall 2020, significant differences in transcript expression of markers were found between sites and sexes in greenside darters (GSD) with SOD and CAT showing increased expression downstream. Changes in transcript expression aligned with antioxidative enzyme activity where interactive effects with sex-related differences were observed in fish collected the Fall of 2020. In contrast, transcription markers measured in Fall 2021 were increased upstream compared to downstream species. Continued investigation on the impacts of pharmaceutical exposures in non-target organisms is crucial to further the knowledge of WWTP effluent impacts.

A strong atmospheric river made landfall in southwestern British Columbia, Canada on 14th November 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterized in terms of the associated two-day precipitation, the event is approximately a one in 50-100 year event, and its probability has been increased by a best estimate of 50% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events has been increased by human-induced climate change by a best estimate of 2 to 4. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Extreme temperature is a major threat to urban populations; thus, it is crucial to understand future changes to plan adaptation and mitigation strategies. We assess historical and CMIP6 projected trends of minimum and maximum temperatures for the 18 most populated Canadian cities. Temperatures increase (on average 0.3°C/decade) in all cities during the historical period (1979–2014), with Prairie cities exhibiting lower rates (0.06°C/decade). Toronto (0.5°C/decade) and Montreal (0.7°C/decade) show high increasing trends in the observation period. Higher-elevation cities, among those with the same population, show slower increasing temperature rates compared to the coastal ones. Projections for cities in the Prairies show 12% more summer days compared to the other regions. The number of heat waves (HWs) increases for all cities, in both the historical and future periods; yet alarming increases are projected for Vancouver, Victoria, and Halifax from no HWs in the historical period to approximately 4 HWs/year on average, towards the end of 2100 for the SSP5–8.5. The cold waves reduce considerably for all cities in the historical period at a rate of 2 CWs/decade on average and are projected to further reduce by 50% compared to the observed period. • CMIP6 simulations for extreme temperature estimation of the largest Canadian cities. • Prairies' cities exhibit a lower rate of temperature increase compared to the cities in Great lakes in observation period. • Cities in Prairies are projected to have 12% more summer days than the rest of the cities. • The number of heat waves increases significantly, especially for Vancouver, Victoria, and Halifax. • Cold waves are expected to decrease by 50% in future.

• The probable impacts of future climate on ice-jam floods are discussed. • Practical suggestions for modelling ice-jam floods under both past and future climates are provided. • Research opportunities that could lead to further improvements in ice-jam flood modelling and prediction are presented. Ice-jam floods (IJFs) are a key concern in cold-region environments, where seasonal effects of river ice formation and break-up can have substantial impacts on flooding processes. Different statistical, machine learning, and process-based models have been developed to simulate IJF events in order to improve our understanding of river ice processes, to quantify potential flood magnitudes and backwater levels, and to undertake risk analysis under a changing climate. Assessment of IJF risks under future climate is limited due to constraints related to model input data. However, given the broad economic and environmental significance of IJFs and their sensitivity to a changing climate, robust modelling frameworks that can incorporate future climatic changes, and produce reliable scenarios of future IJF risks are needed. In this review paper, we discuss the probable impacts of future climate on IJFs and provide suggestions on modelling IJFs under both past and future climates. We also make recommendations around existing approaches and highlight some data and research opportunities, that could lead to further improvements in IJF modelling and prediction.

With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more–advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT–2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by ∼2 on average in the shallow near–shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by ∼6 on average in the deepest profundal off–shore zone.

Abstract Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dewpoint temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.

Water quality models are an emerging tool in water management to understand and inform decisions related to eutrophication. This study tested flow scenario effects on the water quality of Buffalo Pound Lake—a eutrophic reservoir supplying water for approximately 25% of Saskatchewan’s population. The model CE-QUAL-W2 was applied to assess the impact of inter-basin water diversion after the impounded lake received high inflows from local runoff. Three water diversion scenarios were tested: continuous flow, immediate release after nutrient loading increased, and a timed release initiated when water levels returned to normal operating range. Each scenario was tested at three different transfer flow rates. The transfers had a dilution effect but did not affect the timing of the nutrient peaks in the upstream portion of the lake. In the lake’s downstream section, nutrients peaked at similar concentrations as the base model, but peaks arrived earlier in the season and attenuated rapidly. Results showed greater variation among scenarios in wet years compared to dry years. Dependent on the timing and quantity of water transferred, some but not all water quality parameters are predicted to improve along with the water diversion flows over the period tested. The results suggest that it is optimal to transfer water while local watershed runoff is minimal.

Abstract Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

Ice jams are impacted by several climatic factors that are likely to change under a future warming climate. Due to the complexity of river ice phenology, projection of future ice jams is challenging. However, it is important to be able to project future ice jam behavior. Additionally, ice jam research is limited by the shortage of long-term monitoring data. In this paper, a novel framework for projecting future ice jam behavior is developed and implemented for ice jams in a data-sparse region, the Slave River Delta, NWT, Canada, situated in the Mackenzie River Basin (MRB). This framework employs both historical records and future hydro-meteorological data, acquired from climate and hydrological models, to drive the river ice models and quantify climate-induced influences on ice jams. Ice jam behavior analysis is based on three outputs of the framework: potential of river ice jamming, ice jam initiation date, and the stage frequency distribution of backwater elevation induced by ice jams. Trends of later ice jam initiation and decreased possibility of ice jam formation are projected, but ice jamming events in the Slave River Delta are likely to be more severe and cause higher backwater levels.

Management strategies aimed at reducing nutrient enrichment of surface waters may be hampered by nutrient legacies that have accumulated in the landscape. Here, we apply the Net Anthropogenic Phosphorus Input (NAPI) model to reconstruct the historical phosphorus (P) input trajectories for the province of Ontario, which encompasses the Canadian portion of the drainage basin of the Laurentian Great Lakes (LGL). NAPI considers P inputs from detergent, human and livestock waste, fertilizer inputs, and P outputs by crop uptake. During the entire time period considered, from 1961 to 2016, Ontario experienced positive annual NAPI values. Despite a generally downward NAPI trend since the late 1970s, the lower LGL, especially Lake Erie, continue to be plagued by algal blooms. When comparing NAPI results and river monitoring data for the period 2003 to 2013, P discharged by Canadian rivers into Lake Erie only accounts for 12.5% of the NAPI supplied to the watersheds' agricultural areas. Thus, over 85% of the agricultural NAPI is retained in the watersheds where it contributes to a growing P legacy, primarily as soil P. The slow release of legacy P therefore represents a long-term risk to the recovery of the lake. To help mitigate this risk, we present a methodology to spatially map out the source areas with the greatest potential of erosional export of legacy soil P to surface waters. These areas should be prioritized in soil conservation efforts.

In the higher latitudes of the northern hemisphere, ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Since ice-jam formation mechanism is stochastic and depends on numerous unpredictable hydraulic and river ice factors, ice-jam associated flood forecasting is a very challenging task. A stochastic modelling framework was developed to forecast real-time ice jam flood severity along the transborder (New Brunswick/Maine) Saint John River of North America during the spring breakup 2021. Modélisation environnementale communautaire—surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast. A Monte-Carlo analysis (MOCA) framework was applied to simulate hundreds of possible ice-jam scenarios for the model domain from Fort Kent to Grand Falls using a hydrodynamic river ice model, RIVICE. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.

Rapid shrub expansion has been observed across the Arctic, driving a need for regional-scale estimates of shrub biomass and shrub-mediated ecosystem processes such as rainfall interception. Synthetic-Aperture Radar (SAR) data have been shown sensitive to vegetation canopy characteristics across many ecosystems, thereby potentially providing an accurate and cost-effective tool to quantify shrub canopy cover. This study evaluated the sensitivity of L-band Advanced Land Observing Satellite 2 (ALOS-2) data to the aboveground biomass and Leaf Area Index (LAI) of dwarf birch and alder in the Trail Valley Creek watershed, Northwest Territories, Canada. The σ° VH /σ° VV ratio showed strong sensitivity to both LAI (R 2 = 0.72 with respect to in-situ measurements) and wet aboveground biomass (R 2 = 0.63) of dwarf birch. Our ALOS-2-derived maps revealed high variability of birch shrub LAI and biomass across spatial scales. The LAI map was fed into the sparse Gash model to estimate shrub rainfall interception, an important but under-studied component of the Arctic water balance. Results suggest that on average across the watershed, 17 ± 3% of incoming rainfall was intercepted by dwarf birch (during summer 2018), highlighting the importance of shrub rainfall interception for the regional water balance. These findings demonstrate the unexploited potential of L-band SAR observations from satellites for quantifying the impact of shrub expansion on Arctic ecosystem processes. • L-band SAR is a skillful predictor for tundra shrub biomass and leaf area index. • High spatial variation in tundra shrub cover captured by L-band SAR. • Distributed rainfall interception by shrub mapped across the watershed. • Amount of interception closely linked to shrub leaf area index.

• A comprehensive review and analysis of IMERG validation studies from 2016 to 2019. • There is robust representation of spatio-temporal patterns of precipitation. • Discrepancies can be found in extreme and light precipitation, and the winter season. • The 30-min scale has not yet been sufficiently evaluated. • Using IMERG in hydrological simulation results to high variance in their performance. Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.

Abstract Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge ( Eriophorum vaginatum ) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ~0.97 tussocks per m2 over the past ~38 years on Alaska’s North Slope from ~1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. -0.8 tussocks per m2 and -85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m2 and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlights the need for better representation of this species in TBMs.

ABSTRACT Simple and robust hydrological modelling is critical for peat studies as water content (θ) and water table depth (d WT) are key controls on many biogeochemical processes. We show that near-surface θ can be a good predictor of θ at any depth and/or d WT in peat. This was achieved by further developing the formulae of an existing model and applying it for Mer Bleue bog (Ontario, Canada) and a permafrost peat plateau at Scotty Creek (Northwest Territories, Canada). Simulated θ dynamics at various depths in hummocks and hollows at both sites matched observations with R2 , Willmott’s index of agreement (d), and normalized Nash-Sutcliffe efficiency coefficient (NNSE), reaching 0.97, 0.95, and 0.86, respectively. Simulated bog WT dynamics matched observations with R2 , d, and NNSE reaching 0.67, 0.87, and 0.72. Our approach circumvents the difficulties of measuring subsurface hydrology and reveals a perspective for large spatial scale estimation of θ and d WT in peat.

Peatlands have acted as net CO2 sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes, where peatlands are abundant, can disturb their CO2 sink function. Here we show that sensitivity of peatland net CO2 exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO2 sink responses. We use multiannual net CO2 exchange observations from 20 northern peatlands to show that warmer early summers are linked to increased net CO2 uptake, while warmer late summers lead to decreased net CO2 uptake. Thus, net CO2 sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. Our results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO2 uptake over the coming few decades.

ABSTRACT Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation. GRAPHICAL ABSTRACT

Subsurface pipe failures in cold regions are generally believed to be exacerbated by differential strain in shallow soils induced by seasonal freeze and thaw cycles. The transient stress–strain fields resulting from soil water phase change may influence the occurrence of local buried pipe breaks including those related to urban water mains. This work proposes that freezing-induced frost loading results in uneven stress–strain distributions along the buried water mains placing them at risk of bending, breaking, and/or leaking. A coupled thermal-hydraulic-mechanical (THM) model was developed to illustrate the interactions among moisture, temperature, and stress–strain fields within variably saturated freezing soils. Several typical cases involving highly frost-susceptible and lower frost-susceptible soils underlying roadbeds were examined. Results show that the magnitude of frost-induced compressive stress and strain changes between different frost-susceptible soils can vary significantly. Such substantial differences in stress–strain fields would increase the breakage risk of water mains buried within different types of soils. Furthermore, even water mains buried within soils with low frost-susceptibility are at risk when additional sources of soil water exist and are available to migrate to the freezing front. To reduce the risk of damage to buried pipe-like infrastructure, such as municipal water mains, from soil freezing phenomena, the selected backfill material should have fairly consistent frost susceptibility or a broad zone of transition should be considered between materials with significantly different frost susceptibility. In addition, buried pipes should be kept as far away from external sources of subsurface water as possible considering the potential for the water source to exacerbate the level of risk to the pipe.

Wetlands are important ecosystems—they provide vital hydrological and ecological services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extents and hydrological processes is important for valuing wetlands' function. The purpose of this study is to dynamically represent the spatial extents and hydrological processes of wetlands and investigate their feedback to regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP land surface model with two major modifications: (a) modifying the subgrid saturation fraction for spatial wetland extent and (b) incorporating a dynamic wetland storage to simulate hydrological processes. This scheme was evaluated at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while suppressing sensible heat and runoff in the wetland scheme. Finally, the dynamic wetland scheme was applied in the Weather Research and Forecasting (WRF) model. The wetlands scheme not only modifies the surface energy balance but also interacts with the lower atmosphere, shallowing the planetary boundary layer height and promoting cloud formation. A cooling effect of 1–3°C in summer temperature is evident where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for >10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling and wetland conservation, especially in mitigating extreme heatwaves under climate change.

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.

Access to and availability of food harvested from the land (called traditional food, country food, or wild food) are critical to food security and food sovereignty of Indigenous People. These foods can be particularly difficult to access for those living in urban environments. We ask: what policies are involved in the regulation of traditional/country foods and how do these policies affect access to traditional/country food for Indigenous Peoples living in urban centers? Which policies act as barriers? This paper provides a comparative policy analysis of wild food policies across Ontario, the Northwest Territories (NWT), and the Yukon Territory, Canada, by examining and making comparisons between various pieces of legislation, such as fish and wildlife acts, hunting regulations, food premises legislation, and meat inspection regulations. We provide examples of how some programs serving Indigenous Peoples have managed to provide wild foods, using creative ways to operate within the existing system. While there is overwhelming evidence that traditional/country food plays a critical role for the health and well-being of Indigenous Peoples within Canada, Indigenous food systems are often undermined by provincial and territorial wild food policies. Provinces like Ontario with more restrictive policies may be able to learn from the policies in the Territories. We found that on a system level, there are significant constraints on the accessibility of wild foods in urban spaces because the regulatory food environment is designed to manage a colonial market-based system that devalues Indigenous values of sharing and reciprocity and Indigenous food systems, particularly for traditional/country foods. Dismantling the barriers to traditional/country food access in that system can be an important way forward.

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.

Mercury concentrations ([Hg]) in fish reflect complex biogeochemical and ecological interactions that occur at a range of spatial and biological scales. Elucidating these interactions is crucial to understanding and predicting fish [Hg], particularly at northern latitudes, where environmental perturbations are having profound effects on land-water-animal interactions, and where fish are a critical subsistence food source. Using data from eleven subarctic lakes that span an area of ~60,000 km2 in the Dehcho Region of Northwest Territories (Canada), we investigated how trophic ecology and growth rates of fish, lake water chemistry, and catchment characteristics interact to affect [Hg] in Northern Pike (Esox lucius), a predatory fish of widespread subsistence and commercial importance. Results from linear regression and piecewise structural equation models showed that 83% of among-lake variability in Northern Pike [Hg] was explained by fish growth rates (negative) and concentrations of methyl Hg ([MeHg]) in benthic invertebrates (positive). These variables were in turn influenced by concentrations of dissolved organic carbon, MeHg (water), and total Hg (sediment) in lakes, which were ultimately driven by catchment characteristics. Lakes in relatively larger catchments and with more temperate/subpolar needleleaf and mixed forests had higher [Hg] in Northern Pike. Our results provide a plausible mechanistic understanding of how interacting processes at scales ranging from whole catchments to individual organisms influence fish [Hg], and give insight into factors that could be considered for prioritizing lakes for monitoring in subarctic regions.

Adapting to climate change as a consequence of increasing greenhouse gas (GHG) emissions is of paramount importance in the near future. Therefore, recognition of spatial and temporal variations of atmospheric carbon dioxide (CO 2 ) concentration both globally and regionally is critical. The goal of this study was to analyze spatio-temporal patterns of atmospheric CO 2 concentration (XCO 2 ) for Iran over the period from 2003 to 2020 to shed light on the role of various biotic and abiotic controls. First, by using atmospheric XCO 2 data obtained from the SCIAMACHY and GOSAT satellite instruments, a series of spatio-temporal XCO 2 distribution maps were developed. Second, to understand of the potential causes underlying the spatio-temporal distributions in XCO 2 , the correlations between monthly XCO 2 and vegetation abundance, air temperature, precipitation, and fossil fuel CO 2 emissions were examined. The spatio-temporal patterns in XCO 2 indicated an increasing gradient of XCO 2 from north to south and from west to east in Iran, with the highest XCO 2 in the central, southern and southeastern parts of the country. The findings revealed that XCO 2 was negatively correlated with vegetation abundance and precipitation, and positively correlated with air temperature in different months from 2003 to 2020. Among the different explanatory variables, vegetation abundance explained most of the spatial variation in XCO 2 . Furthermore, in spring (April and May), which has the highest amount of vegetation abundance and precipitation, biotic controls had a substantial impact on the diffusion and absorption of XCO 2 in the northern and northwestern parts of Iran. Our results suggest that CO 2 is moved from the center of Iran to the outer parts of the country in summer (July–September) and vice-versa in winter (January–March). Our findings provide policy- and decision makers with crucial information regarding the spatio-temporal dynamics in XCO 2 to reduce and, ultimately, halt its increase. • Over the spatial distribution of XCO 2 , biotic controls such as vegetation abundance were found to be the primary controlling factor especially in spring. • The results revealed a significant positive correlation between XCO 2 and CO 2 emissions only in temporal correlation but not in the spatial correlation. • The spatio-temporal distribution maps show the maximum XCO 2 in south and southeast of Iran, while the highest net increase of XCO 2 appeared in the west and north of Iran which are densely populated.

Studies of tree water source partitioning have primarily focused on the growing season. However, little is yet known about the source of transpiration before, during, and after snowmelt when trees rehydrate and recommence transpiration in the spring. This study investigates tree water use during spring snowmelt following tree's winter stem shrinkage. We document the source of transpiration of three boreal forest tree species—Pinus banksiana, Picea mariana, and Larix laricina—by combining observations of weekly isotopic signatures (δ18O and δ2H) of xylem, soil water, rainfall and snowmelt with measurements of soil moisture dynamics, snow depth and high-resolution temporal measurements of stem radius changes and sap flow. Our data shows that the onset of stem rehydration and transpiration overlaps with snowmelt for evergreens. During rehydration and transpiration onset, xylem water at the canopy reflected a constant pre-melt isotopic signature likely showing late fall conditions. As snowmelt infiltrates the soil and recharges the soil matrix, soil water shows a rapid isotopic shift to depleted-snowmelt water values. While there was an overlap between snowmelt and transpiration timing, xylem and soil water isotopic values did not overlap during transpiration onset. Our data showed 1–2-week delay in the shift in xylem water from pre-melt to clear snowmelt-depleted water signatures in evergreen species. This delay appears to be controlled by tree water transit time that was in the order of 9–18 days. Our study shows that snowmelt is a key source for stem rehydration and transpiration in the boreal forest during spring onset.

Concentrations of total mercury were measured in blood and hair samples collected as part of a human biomonitoring project conducted in First Nations communities of the Mackenzie Valley, Northwest Territories, Canada. Hair (n = 443) and blood (n = 276) samples were obtained from six communities in the Dehcho region and three communities in the Sahtú region of the Mackenzie Valley. The aim of this paper was to calculate hair to blood mercury ratios (for matched samples) and determine if: 1) ratios differed significantly between the two regions; 2) ratios differed from the 250:1 ratio proposed by the WHO; and, 3) point estimates of hair to blood mercury ratios could be used to estimate blood mercury concentrations. In addition, this paper aims to determine if there were seasonal patterns in hair mercury concentrations in these regions and if so, if patterns were related to among-season variability in fish consumption. The majority of mercury levels in hair and blood were below relevant health-based guidance values. The geometric mean hair (most recent segment) to blood mercury ratio (stratified by region) was 619:1 for the Dehcho region and 1220:1 for the Sahtú region. Mean log-transformed hair to blood mercury ratios were statistically significantly different between the two regions. Hair to blood ratios calculated in this study were far higher (2-5 times higher) than those typically reported in the literature and there was a large amount of inter-individual variation in calculated ratios (range: 114:1 to 4290:1). Using the 250:1 ratio derived by the World Health Organisation to estimate blood mercury concentrations from hair mercury concentrations would substantially over-estimate blood mercury concentrations in the studied regions. However, geometric mean site-specific hair to blood mercury ratios can provide estimates of measures of central tendency for blood mercury concentrations from hair mercury concentrations at a population level. Mercury concentrations were determined in segments of long hair samples to examine exposure of participants to mercury over the past year. Hair segments were assigned to six time periods and the highest hair mercury concentrations were generally observed in hair segments that aligned with September/October and November/December, whereas the lowest hair mercury concentrations were aligned with March/April and May/June. Mean log-transformed hair mercury concentrations were statistically significantly different between time periods. Between time periods (e.g., September/October vs. March/April), the geometric mean mercury concentration in hair differed by up to 0.22 μg/g, and the upper margins of mercury exposure (e.g., 95th percentile of hair mercury) varied by up to 0.86 μg/g. Results from self-reported fish consumption frequency questionnaires (subset of participants; n = 170) showed total fish intake peaked in late summer, decreased during the winter, and then increased during the spring. Visual assessment of results indicated that mean hair mercury concentrations followed this same seasonal pattern. Results from mixed effects models, however, indicated that variability in hair mercury concentrations among time periods was not best explained by total fish consumption frequency. Instead, seasonal trends in hair mercury concentrations may be more related to the consumption of specific fish species (rather than total wild-harvested fish in general). Future work should examine whether seasonal changes in the consumption of specific fish species are associated with seasonal changes in hair mercury concentrations.

A dietary transition away from traditional foods and toward a diet of the predominantly unhealthy market is a public health and sociocultural concern throughout Indigenous communities in Canada, including those in the sub-Arctic and remote regions of Dehcho and Sahtú of the Northwest Territories, Canada. The main aim of the present study is to describe dietary intakes for macronutrients and micronutrients in traditional and market food from the Mackenzie Valley study. We also show the trends of contributions and differences of dietary intakes over time from 1994 data collected and reported by the Centre for Indigenous People's Nutrition and Environment (CINE) in 1996. Based on 24-h dietary recall data, the study uses descriptive statistics to describe the observed dietary intake of the Dene First Nations communities in the Dehcho and Sahtú regions of the NWT. Indigenous people in Canada, like the sub-Arctic regions of Dehcho and Sahtú of the NWT, continue to consume traditional foods, although as a small percentage of their total dietary intake. The observed dietary intake calls for action to ensure that traditional food remains a staple as it is critical for the wellbeing of Dene in the Dehcho and Sahtú regions and across the territory.

At the edge of alpine and Arctic ecosystems all over the world, a transition zone exists beyond which it is either infeasible or unfavorable for trees to exist, colloquially identified as the treeline. We explore the possibility of a thermodynamic basis behind this demarcation in vegetation by considering ecosystems as open systems driven by thermodynamic advantage-defined by vegetation's ability to dissipate heat from the earth's surface to the air above the canopy. To deduce whether forests would be more thermodynamically advantageous than existing ecosystems beyond treelines, we construct and examine counterfactual scenarios in which trees exist beyond a treeline instead of the existing alpine meadow or Arctic tundra. Meteorological data from the Italian Alps, United States Rocky Mountains, and Western Canadian Taiga-Tundra are used as forcing for model computation of ecosystem work and temperature gradients at sites on both sides of each treeline with and without trees. Model results indicate that the alpine sites do not support trees beyond the treeline, as their presence would result in excessive CO[Formula: see text] loss and extended periods of snowpack due to temperature inversions (i.e., positive temperature gradient from the earth surface to the atmosphere). Further, both Arctic and alpine sites exhibit negative work resulting in positive feedback between vegetation heat dissipation and temperature gradient, thereby extending the duration of temperature inversions. These conditions demonstrate thermodynamic infeasibility associated with the counterfactual scenario of trees existing beyond a treeline. Thus, we conclude that, in addition to resource constraints, a treeline is an outcome of an ecosystem's ability to self-organize towards the most advantageous vegetation structure facilitated by thermodynamic feasibility.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

Both unconventional and conventional oil and gas production have led to instances of brine contamination of near-surface environments from spills of saline produced waters. Strontium isotope ratios ( 87 Sr/ 86 Sr) have been used as a sensitive tracer of sources of brine contamination in surface waters and shallow aquifers in areas where oil and gas production are limited to only a few reservoirs and produced water sources are well-defined. Recent expansion of conventional and unconventional oil and gas production to additional tight formations within sedimentary basins has resulted in production of formation waters from multiple oil and gas reservoirs that may have similar chemical and isotopic ratios, including 87 Sr/ 86 Sr. This study evaluates the utility of 87 Sr/ 86 Sr, the most widely available tracer dataset beyond major ion chemistry and water stable isotopes, as a tracer of brine contamination related to conventional and unconventional oil and gas production in the Williston, Appalachian and Permian basins. Multiple stacked oil and gas reservoirs within each basin have overlapping formation water 87 Sr/ 86 Sr, based on a non-parametric statistical test. For example, in the Appalachian Basin, produced waters from unconventional gas production in the Middle Devonian Marcellus and Upper Ordovician Utica shales have overlapping 87 Sr/ 86 Sr. In the Permian Basin, produced waters from the unconventional Pennsylvanian-Permian Wolfcamp Shale and conventional and unconventional Pennsylvanian Cisco/Canyon/Strawn formations have similar 87 Sr/ 86 Sr. In the Williston Basin produced waters from Late Devonian to Early Mississippian Bakken Formation unconventional oil production have overlapping 87 Sr/ 86 Sr with produced waters associated with minor production of conventional oil from the Middle Devonian Winnipegosis. Improved spatial characterization of 87 Sr/ 86 Sr and other isotopic signatures of produced waters from various oil/gas reservoirs are needed to constrain geographic and depth variability of produced waters in hydrocarbon producing regions. This is particularly important, as unconventional oil and gas production expands in areas of existing conventional oil and gas production, where delineating sources of saline produced waters in cases of accidental surface spills or subsurface leakage will become a greater challenge. Sr isotopes alone may not be able to distinguish produced waters in areas with overlapping production from reservoirs with similar isotopic signatures. • Sr isotopes may not be effective tracers where stacked reservoirs are present. • More Sr isotope data required to understand spatial/depth variability. • Multiple tracers may be needed to identify sources of contamination.

• Eight rainfall models are compared as input for a simplified continuous hydrologic model. • The comparison is performed by investigating the simulated runoff properties. • Results suggest that all rainfall models lead to realistic runoff time series. • Four models will be further optimized to be adapted for data-scarce applications. Continuous hydrologic modelling is a natural evolution of the event-based design approach in modern hydrology. It improves the rainfall-runoff transformation and provides the practitioner with more effective hydrological output information for risk assessment. However, this approach is still not widely adopted, mainly because the choice of the most appropriate rainfall simulation model (which is the core of continuous frameworks) for the specific aim of risk analysis has not been sufficiently investigated. In this paper, we test eight rainfall models by evaluating the performances of the simulated rainfall time series when used as input for a simplified continuous rainfall-runoff model, the COSMO4SUB, which is particularly designed for small and ungauged basins. The comparison confirms the capability of all models to provide realistic flood events and allows identifying the models to be further improved and tailored for data-scarce hydrological risk applications. The suggested framework is transferable to any catchment while different hydrologic and rainfall models can be used.

• Rainfall erosivity for Yellow River basin increased significantly at both event and seasonal scale during 1971–2020. • Storms shifted towards longer durations and higher precipitation amounts. • Extreme precipitation within the basin occurred more frequently and intensely. • The increasing trend became more pronounced in the last two decades. Hourly precipitation data from 1971 to 2020, collected from 98 stations distributed across the Yellow River basin, were analyzed to detect changes in characteristics on rainfall and rainfall erosivity for all storms and storms with extreme erosivity (greater than 90 th percentile). Results showed that over the past 50 years, rainfall erosivity at both event and seasonal scales over the whole basin increased significantly ( p < 0.05) with rates of 5.46% and 6.86% decade -1 , respectively, compared to the 1981–2010 average values. Approximate 80% of 98 stations showed increasing trends and 20% of stations had statistically significant trends ( p < 0.1). The increase of rainfall erosivity resulted from the significant increasing trends of average storm precipitation ( p < 0.1), duration ( p < 0.1), rainfall energy ( p < 0.05) and maximum 1-h intensity ( p < 0.05). In addition, the total extreme erosivity showed significant upward trends at a relative rate of 6.05% decade -1 ( p < 0.05). Extreme erosivity storms occurred more frequently and with higher rainfall energy during the study period ( p < 0.05). Trends for seasonal total and extreme erosivity were also estimated based on daily rainfall data, and the changing magnitudes were similar to those based on hourly rainfall data, which suggested daily rainfall can be applied to detect interannual and long-term variations of rainfall erosivity in the absence of rainfall data with higher resolution. It was suggested that soil and water conservation strategies and vegetation projects conducted within the Yellow River basin should be continued and enhanced in the future.

• A first comprehensive and systematic review on the research of extreme precipitation in China. • Variation and regional characteristics of extreme precipitation under non-stationary conditions due to climate change and human activities. • Supports and basis for engineering application and further research on extreme precipitation and flood in China. Recent years have witnessed global massive property losses and casualties caused by extreme precipitation and its subsequent natural disasters, including floods and landslides. China is one of the countries deeply affected by these casualties. If the statistical characteristics and laws of extreme precipitation could be clearly grasped, then the negative impacts triggered by it may be minimized. China is a vast country and diverse in climate and terrain, hence different regions may be suitable for different analyses and research methods. Therefore, it is necessary to clarify the research progress, methods and current status of extreme precipitation across the country. This paper attempts to provide a comprehensive review of techniques and methods used in extreme precipitation research and engineering practice and their applications. The literature is reviewed focusing on seven aspects: (1) annual maxima method (AM), (2) peaks over threshold method (POT), (3) probable maximum precipitation (PMP), (4) non-stationary analysis of precipitation extremes, (5) intensity-duration-frequency curves (IDF), (6) uncertainty in extreme precipitation frequency analysis, and (7) spatial variability of extreme precipitation. Research on extreme precipitation in China is generally based or centered on the above seven aspects. The current study aims to provide ideas for further research on extreme precipitation frequency analysis and its response to climate change and human activities.

<abstract><p>This paper proposes a new very simply explicitly invertible function to approximate the standard normal cumulative distribution function (CDF). The new function was fit to the standard normal CDF using both MATLAB's Global Optimization Toolbox and the BARON software package. The results of three separate fits are presented in this paper. Each fit was performed across the range $ 0 \leq z \leq 7 $ and achieved a maximum absolute error (MAE) superior to the best MAE reported for previously published very simply explicitly invertible approximations of the standard normal CDF. The best MAE reported from this study is 2.73e–05, which is nearly a factor of five better than the best MAE reported for other published very simply explicitly invertible approximations.</p></abstract>

Globally, extreme temperatures have severe impacts on the economy, human health, food and water security, and ecosystems. Mortality rates have been increased due to heatwaves in several regions. Specifically, megacities have high impacts with the increasing temperature and ever-expanding urban areas; it is important to understand extreme temperature changes in terms of duration, magnitude, and frequency for future risk management and disaster mitigation. Here we framed a novel Semi-Parametric quantile mapping method to bias-correct the CMIP6 minimum and maximum temperature projections for 199 megacities worldwide. The changes in maximum and minimum temperature are quantified in terms of climate indices (ETCCDI and HDWI) for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Cities in northern Asia and northern North America (Kazan, Samara, Heihe, Montréal, Edmonton, and Moscow) are warming at a higher rate compared to the other regions. There is an increasing and decreasing trend for the warm and cold extremes respectively. Heatwaves increase exponentially in the future with the increase in warming, that is, from SSP1-2.6 to SSP5-8.5. Among the CMIP6 models, a huge variability is observed, and this further increases as the warming increases. All climate indices have steep slopes for the far future (2066–2100) compared to the near future (2031–2065). Yet the variability among CMIP6 models in near future is high compared to the far future for cold indices.

Molybdenum disulfide (MoS2) is a promising material for applications in sensors, energy storage, energy conversion devices, solar cells, and fuel cells. Because many of those applications require conductive materials, we recently developed a method for preparing a conductive form of MoS2 (c-MoS2) using dilute aqueous hydrogen peroxide in a simple and safe way. Here, we investigate modulating the chemical and mechanical surface properties of c-MoS2 thin films using diazonium chemistry. In addition to a direct passivation strategy of c-MoS2 with diazonium salts for electron-withdrawing groups, we also propose a novel in situ synthetic pathway for modification with electron-donating groups. The obtained results are examined by Raman spectroscopy and X-ray photoelectron spectroscopy. The degree of surface passivation of pristine and functionalized c-MoS2 films was tested by exposing them to aqueous solutions of different metal cations (Fe2+, Zn2+, Cu2+, and Co2+) and detecting the chemiresistive response. While pristine films were found to interact with several of the cations, modified films did not. We propose that a surface charge transfer mechanism is responsible for the chemiresistive response of the pristine films, while both modification routes succeeded at complete surface passivation. Functionalization was also found to lower the coefficient of friction for semiconducting 2H-MoS2, while all conductive materials (modified or not) also had lower coefficients of friction. This opens up a pathway to a palette of dry lubricant materials with improved chemical stability and tunable conductivity. Thus, both in situ and direct diazonium chemistries are powerful tools for tuning chemical and mechanical properties of conductive MoS2 for new devices and lubricants based on conductive MoS2.

Molybdenum disulfide (MoS 2 ) has long been used in catalysis and is a promising material for energy conversion devices.

Prevalence of high levels of metal ions in natural and drinking water is a growing problem to both ecosystems and human health. Several methods are broadly used for heavy metal monitoring in water resources, but most of them are laboratory-based. Here, we describe a method that simplifies the measurement process by enabling passive aliquoting and preconcentration of heavy metals. We use superabsorbent polymer beads that can take up hundreds of times their volume to aliquot the sample and preconcentrate the ionic species present in them by 2 orders of magnitude. We then use commercially available colorimetric dyes that are sensitive only at high concentrations to reveal a visible range change in the bead color that can be measured optically using a camera. Using this approach, we have detected the concentration of copper(II) ions in water as low as 5.4 ppb. We demonstrate that this method can also be used for drinking water and tap water samples to assess concentrations of copper and iron. This solid-state method significantly simplifies the analytical procedure and provides extremely low detection levels of heavy metals, eliminating the need for expensive equipment and hence could be useful in remote settings.

With the growing consumption of caffeine-containing beverages, detection of caffeine has become an important biomedical, bioanalytical, and environmental topic. We herein isolated four high-quality aptamers for caffeine with dissociation constants ranging from 2.2 to 14.6 μM as characterized using isothermal titration calorimetry. Different binding patterns were obtained for the three single demethylated analogues: theobromine, theophylline, and paraxanthine, highlighting the effect of the molecular symmetry of the arrangement of the three methyl groups in caffeine. A structure-switching fluorescent sensor was designed showing a detection limit of 1.2 μM caffeine, which reflected the labeled caffeine concentration within 6.1% difference for eight commercial beverages. In 20% human serum, a detection limit of 4.0 μM caffeine was achieved. With the four aptamer sensors forming an array, caffeine and the three analogues were well separated from nine other closely related molecules.

Abstract Monitoring the real-time status of food products using pH sensors is important to determine if pathogens are present and growing, which in turn affects food quality. A promising material for pH sensors is ruthenium dioxide (RuO2) due to its chemical stability and excellent performance. Furthermore, graphene oxide (GO) provides an electrode with large surface area and good electrical properties. Here, in situ sol-gel deposition of RuO2 nanoparticles on the surface of GO as a facile, cost-effective, and environmentally friendly approach is used for the fabrication of a flexible pH sensor. As-synthesized GO-RuO2 nanocomposites with a low volume were applied on the surface of screen printed carbon paste. The obtained GO-RuO2 nanocomposite pH sensor achieved high pH sensitivity (55.5 mV/pH) in the pH range of 4-10, up to 4 times higher compared to the unmodified carbon electrode. The increased sensitivity is due to the positive role of RuO2 nanoparticles densely anchored across the GO sheets. It also shows low drift (0.36 mV/hr) and low hysteretic width. Considering this novel method and material with the cost-effective green synthesis approach, as well as excellent pH sensing properties, GO-RuO2 can be considered as a promising material for production of high-performance electrochemical pH sensors.

Hydrogen peroxide (H2O2) is an intermediate molecule generated in numerous peroxidase assays used to measure concentrations of biomolecules such as glucose, galactose, and lactate. Here, we develop a solid-state reagent-free chemiresistive H2O2 sensor, which can measure H2O2 over a wide measuring range of 0.5–1000 ppm (0.015–29.4 mM). The sensor was fabricated using a network of functionalized single-walled carbon nanotubes (SWCNTs) as a sensitive layer and a xurographically patterned gold leaf as a contact electrode. The SWCNTs were functionalized with crystal violet to impart selective detection of H2O2. The crystal violet was self-assembled on the SWCNT film and subsequently polymerized via cyclic voltammetry to improve its retention on the sensing layer. The functionalized sensor exhibited good selectivity against common interferents such as uric acid, urea, glucose, and galactose. In addition, the sensor was used to measure in situ H2O2 generated during peroxidase assays performed using enzymes like glucose oxidase. The sensor was tested in standard buffer solutions for both enzymes. The glucose oxidase assay was also demonstrated in spiked pooled human plasma samples. The glucose oxidase-coated sensor exhibited a glucose detection range of 2–20 mM in standard buffer and blood plasma solutions, with a good recovery rate (∼95–107%) for glucose measurements in blood plasma.

Over the last three decades, numerous aptamer-based biosensors have been reported. The basis of these sensors is the selective binding of target analytes by aptamers. In the last few years, a number of papers have been published questioning the binding ability of some popular aptamers such as those documented for As(III), ampicillin, chloramphenicol, isocarbophos, phorate and dopamine. In this article, these papers are reviewed, and the binding assays are described, which may provide possible reasons for obtaining false positive aptamers. Additionally, relevant aptamer selection methods and typical characterization steps are described. It is found that for small molecular targets, using an immobilized library might result in better aptamers. Furthermore, the importance of carefully designed controls to ensure the quality of binding assays is discussed, especially in the case of mutated nonbinding aptamers. Only then, with fully validated aptamers, can subsequent biosensor design bring about meaningful results. • The first critical review of the literature on aptamers that were proven to be non-binding sequences. • Five different aptamers for various small molecules reviewed. • Possible reasons for the generation of such non-binding aptamer sequences proposed and methods to avoid them described.

Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N =905$ </tex-math></inline-formula> ) database of colocated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm {rs}}$ </tex-math></inline-formula> ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{\mathrm {rs}}$ </tex-math></inline-formula> spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 73$ </tex-math></inline-formula> % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

Estimating soil moisture (SM) over the circumpolar boreal forest would have numerous applications including wildfire risk detection, and weather prediction. Evaluation of satellite derived SM retrievals in boreal ecoregions is hindered by available in situ SM observation networks. To address this, an SM monitoring network was established in a boreal forest region in Saskatchewan, Canada. The network is unique as there are no other SM network of similar size in the boreal forest. The network consisted of 17 SM stations within a single Soil Moisture Active Passive (SMAP) satellite observation pixel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$33\times 33$ </tex-math></inline-formula> km). We present an analysis of the sensitivity and accuracy of SMAP SM products in a boreal forest environment over a two-year period in 2018 and 2019. Results show current SMAP radiometer-based L2 SM products have higher correlation with the in situ lower mineral layer SM than with the top organic layer, although the overall correlation is low. Correlations between in situ mineral layer SM and SMAP brightness-temperature (TB) products are higher than those observed with the SMAP SM product, suggesting current SMAP SM retrieval from the TB using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model introduces large uncertainties in the SM estimation, possibly from uncertain vegetation and surface parameters in the retrieval model. Results show SM can be retrieved using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model with reasonable accuracy over the boreal forest provided the vegetation and soil parameters are optimized. The SM retrieval using a dual channel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> model, which utilize both horizontally and vertically polarized SMAP TB, performs better than that with a single channel algorithm (SCA), using optimized parameters.

Abstract Non-invasive contactless simultaneous sensing and heating of individual droplets would allow droplet microfluidics to empower a wide range of applications. However, it is challenging to realize simultaneous sensing and heating of individual droplets as the resonance frequency of the droplet fluid, which is decided by its permittivity, must be known so that energy is only supplied at this frequency for droplet heating with one resonator. To tailor the energy transfer in real-life heating applications, the droplet has to be sensed first to identify its corresponding resonance frequency, which is used to dynamically tune the frequency for supplying the required energy for heating this particular droplet. To achieve this goal, two resonators are needed, with one for sensing and one for heating. Integrating multiple resonators into one typical microfluidic device limits placement of the resonators to be as close as possible, which would raise the concern of crosstalk between them. The crosstalk would result in inaccurate sensing and heating. This study focuses on numerically and experimentally investigating the effect of influencing parameters on the crosstalk between two adjacent resonators with the ultimate goal of providing guidance for multiplexing the resonators in a typical microfluidic device. ANSYS HFSS is used to perform the electromagnetic analysis based on the finite element method. Experimental studies are conducted on a microfluidic chip integrated with two resonators to validate the numerical results. An optimal distance between two resonators is suggested, with the recommendation for the resonator size and heating power towards simultaneous sensing and heating of individual droplets.

Abstract. Data for small to mid-sized watersheds are seldom publicly available, but may be representative of diverse types of hydrological contexts when assessing patterns. These types of data may also prove valuable for informing numerical experimentation and practical modelling. This paper presents data collected in the Alder Creek watershed, located within the Grand River basin in Ontario, Canada. The Alder Creek watershed provides source water from the aquifers of the Waterloo Moraine for multiple well fields that supply the cities of Kitchener and Waterloo. Recharge rates and human impacts on streamflow are important topics for the watershed, and many numerical models of the area have been constructed. In order to support these types of analyses, field equipment was deployed within the watershed between 2013 and 2018 to monitor groundwater levels, stream stage, soil moisture, soil temperature, rainfall, and other weather parameters. The available data are described, complementary information is presented, and examples of possible analyses are cited and illustrated. The data presented and described in this paper are available at https://doi.org/10.20383/101.0178 (Wiebe et al., 2019).

• A novel analytical-numerical scheme for calculating temperature profiles in porous media with temperature-dependent thermal properties during the freezing process; • The hybrid analytical-numerical method can deal with different types of nonlinear soil freezing functions; • Neumann's two-layer solution underestimates the penetration rate and depth of the freezing front; • The profiles of temperature, equivalent thermal conductivity and diffusivity, conductive heat flux, and dynamics of the freezing front were significantly impacted by the shape of the unfrozen water content curve and the magnitude of soil grain thermal conductivity. The freeze-thaw cycle associated with climatic seasonality is a common phenomenon in cold regions affecting a wide range of subsurface processes. Due to the complex and highly nonlinear nature of the associated hydrologic processes, transient freeze-thaw dynamics are conventionally quantified in a numerical way. Here we present a hybrid analytical-numerical scheme for solving one-dimensional soil (or porous media) temperature profiles when the soil profile is subjected to unidirectional freezing (or thawing) conditions. This scheme divides the partially-frozen soil into multi-layers, each with constant thermal parameters and fixed-temperature boundaries. Temperature profiles within each layer were obtained by solving multiple moving-boundary problems. The proposed hybrid analytical-numerical scheme was tested into a freezing test of silty clay in a permafrost region on the Qinghai-Tibetan Plateau, and its solution was in good agreement with the finite element numerical solution. Results show that the proposed multi-layer method adapted well to the changes in unfrozen water content and thermal properties of soil over a wide range of subzero temperatures. By contrast, the freezing front's migration rate and penetration depth calculated by Neumann's classical solution, which only considers two zones (frozen and unfrozen), was found to be underestimated. As for our proposed multi-layer solution, by dividing the subsurface domain into many layers with smaller proportion ratios (thinner layers close to the freezing front), there was a slower penetration rate of the freezing front resulting in shallower penetration depth. The predicted profiles of temperature, thermal conductivity and diffusivity, heat flux, and dynamics of the freezing front were significantly impacted by the shape of the soil freezing curves and the magnitude of soil grain thermal conductivity, especially for the accuracy of long-term predictions.

Microbial activity persists in cold region agricultural soils during the fall, winter, and spring (i.e., non-growing season) and frozen condition, with peak activity during thaw events. Climate change is expected to change the frequency of freeze-thaw cycles (FTC) and extreme temperature events (i.e, altered timing, extreme heat/cold events) in temperate cold regions, which may hasten microbial consumption of fall-amended fertilizers, decreasing potency come the growing season. We conducted a high-resolution temporal examination of the impacts of freeze-thaw and nutrient stress on microbial communities in agricultural soils across both soil depth and time. Four soil columns were incubated under a climate model of a non-growing season including precipitation, temperature, and thermal gradient with depth over 60 days. Two columns were amended with fertilizer, and two incubated as unamended soil. The impacts of repeated FTC and nutrient stress on bacterial, archaeal, and fungal soil community members were determined, providing a deeply sampled longitudinal view of soil microbial response to non-growing season conditions. Geochemical changes from flow-through leachate and amplicon sequencing of 16S and ITS rRNA genes were used to assess community response. Despite nitrification observed in fertilized columns, there were no significant microbial diversity, core community, or nitrogen cycling population trends in response to nutrient stress. FTC impacts were observable as an increase in alpha diversity during FTC. Community compositions shifted across a longer time frame than individual FTC, with bulk changes to the community in each phase of the experiment. Our results demonstrate microbial community composition remains relatively stable for archaea, bacteria, and fungi through a non-growing season, independent of nutrient availability. This observation contrasts canonical thinking that FTC have significant and prolonged effects on microbial communities. In contrast to permafrost and other soils experiencing rare FTC, in temperate agricultural soils regularly experiencing such perturbations, the response to freeze-thaw and fertilizer stress may be muted by a more resilient community or be controlled at the level of gene expression rather than population turn-over. These results clarify the impacts of winter FTC on fertilizer consumption, with implications for agricultural best practices and modeling of biogeochemical cycling in agroecosystems.

Cold regions are warming faster than the rest of the planet, with the greatest warming occurring during the winter and shoulder seasons. Warmer winters are further predicted to result in more frequent soil freezing and thawing events. Freeze-thaw cycles affect biogeochemical soil processes and alter carbon and nutrient export from soils, hence impacting receiving ground and surface waters. Cold region agricultural management should therefore consider the possible effects on water quality of changing soil freeze-thaw dynamics under future climate conditions. In this study, soil column experiments were conducted to assess the leaching of fertilizer nitrogen (N) from an agricultural soil during the non-growing season. Identical time series temperature and precipitation were imposed to four parallel soil columns, two of which had received fertilizer amendments, the two others not. A 15-30-15 N-P-K fertilizer (5.8% ammonium and 9.2% urea) was used for fertilizer amendments. Leachates from the soil columns were collected and analyzed for major cations and anions. The results show that thawing following freezing caused significant export of chloride (Cl − ), sulfate (SO 4 2− ) and nitrate (NO 3 − ) from the fertilizer-amended soils. Simple plug flow reactor model calculations indicated that the high NO 3 − concentrations produced during the fertilized soil thawing events were due to nitrification of fertilizer N in the upper oxidized portion of the soil. The very low concentrations of NO 3 − and ammonium in the non-fertilized soils leachates implied that the freeze-thaw cycles had little impact on the mineralization of soil organic N. The findings, while preliminary, indicate that unwanted N enrichment of aquifers and rivers in agricultural areas caused by fall application of N fertilizers may be exacerbated by changing freeze-thaw activity.

Future warming of the Arctic not only threatens to destabilize the enormous pool of organic carbon accumulated in permafrost soils but may also mobilize elements such as calcium (Ca) or silicon (Si). While for Greenlandic soils, it was recently shown that both elements may have a strong effect on carbon dioxide (CO 2 ) production with Ca strongly decreasing and Si increasing CO 2 production, little is known about the effects of Si and Ca on carbon cycle processes in soils from Siberia, the Canadian Shield, or Alaska. In this study, we incubated five different soils (rich organic soil from the Canadian Shield and from Siberia (one from the top and one from the deeper soil layer) and one acidic and one non-acidic soil from Alaska) for 6 months under both drained and waterlogged conditions and at different Ca and amorphous Si (ASi) concentrations. Our results show a strong decrease in soil CO 2 production for all soils under both drained and waterlogged conditions with increasing Ca concentrations. The ASi effect was not clear across the different soils used, with soil CO 2 production increasing, decreasing, or not being significantly affected depending on the soil type and if the soils were initially drained or waterlogged. We found no methane production in any of the soils regardless of treatment. Taking into account the predicted change in Si and Ca availability under a future warmer Arctic climate, the associated fertilization effects would imply potentially lower greenhouse gas production from Siberia and slightly increased greenhouse gas emissions from the Canadian Shield. Including Ca as a controlling factor for Arctic soil CO 2 production rates may, therefore, reduces uncertainties in modeling future scenarios on how Arctic regions may respond to climate change.

The rapid growth in microplastic pollution research is influencing funding priorities, environmental policy, and public perceptions of risks to water quality and environmental and human health. Ensuring that environmental microplastics research data are findable, accessible, interoperable, and reusable (FAIR) is essential to inform policy and mitigation strategies. We present a bibliographic analysis of data sharing practices in the environmental microplastics research community, highlighting the state of openness of microplastics data. A stratified (by year) random subset of 785 of 6,608 microplastics articles indexed in Web of Science indicates that, since 2006, less than a third (28.5%) contained a data sharing statement. These statements further show that most often, the data were provided in the articles’ supplementary material (38.8%) and only 13.8% via a data repository. Of the 279 microplastics datasets found in online data repositories, 20.4% presented only metadata with access to the data requiring additional approval. Although increasing, the rate of microplastic data sharing still lags behind that of publication of peer-reviewed articles on environmental microplastics. About a quarter of the repository data originated from North America (12.8%) and Europe (13.4%). Marine and estuarine environments are the most frequently sampled systems (26.2%); sediments (18.8%) and water (15.3%) are the predominant media. Of the available datasets accessible, 15.4% and 18.2% do not have adequate metadata to determine the sampling location and media type, respectively. We discuss five recommendations to strengthen data sharing practices in the environmental microplastic research community.

Exploitation of bitumen-rich deposits in the Alberta Oil Sands Region (AOSR) by large-scale mining and processing activities has generated widespread concern about the potential for dispersal of harmful contaminants to aquatic ecosystems via fluvial and atmospheric pathways. The release of mercury has received attention because it is a potent neurotoxin for wildlife and humans. However, knowledge of baseline mercury concentration prior to disturbance is required to evaluate the extent to which oil sands development has contributed mercury to aquatic ecosystems. Here, we use stratigraphic analysis of total mercury concentration ([THg]) in radiometrically dated sediment cores from nine floodplain lakes in the AOSR and downstream Peace-Athabasca Delta (PAD) and two upland lakes in the PAD region to establish pre-1900 baseline [THg] and evaluate if [THg] has become enriched via fluvial and atmospheric pathways since oil sands mining and processing began in 1967. Concentrations of THg in sediment cores from the study lakes range from 0.022–0.096 mg/kg (dry wt.) and are below the Canadian interim sediment quality guidelines for freshwater (0.17 mg/kg). Results demonstrate no enrichment of [THg] above pre-1900 baseline via fluvial pathways at floodplain lakes in the AOSR or PAD. Enrichment of [THg] was detected via atmospheric pathways at upland lakes in the PAD region, but this occurred prior to oil sands development and aligns with long-range transport of emissions from coal combustion and other anthropogenic sources across the northern hemisphere recognized in many other lake sediment records. The inventory of anthropogenic [THg] in the upland lakes in the AOSR is less than at the Experimental Lakes Area of northwestern Ontario (Canada), widely regarded as a “pristine” area. The absence of enrichment of [THg] in lake sediment via fluvial pathways is a critical finding for stakeholders, and we recommend that monitoring at the floodplain lakes be used to inform stewardship as oil sands operators prepare to discharge treated oil sands process waters directly into the Athabasca River upstream of the PAD.

Much of the Arctic is experiencing rapid change in the productivity and recruitment of tall, deciduous shrubs. It is well established that shrub expansion can alter tundra ecosystem composition and function; however, less is known about the degree to which variability in the physical structure of shrub patches might mediate these changes. There is also limited information as to how different physical attributes of shrub patches may covary and how they differ with topography. Here, we address these knowledge gaps by measuring the physical structure, abiotic conditions, and understory plant community composition at sampling plots within undisturbed green alder patches at a taiga–tundra ecotone site in the Northwest Territories, Canada. We found surprisingly few associations between most structural variables and abiotic conditions at the plot scale, with the notable exceptions of canopy complexity and snow depth. Importantly, neither patch structure nor abiotic conditions were associated with the vegetation community at the plot scale when among-patch variation was accounted for. However, among-patch variation in plant community composition was significant and represented a gradient in the richness of tundra specialists and Sphagnum moss abundance. This gradient was strongly associated with mean patch snow depth, which was likely controlled at least in part by mean patch canopy complexity. Overall, natural variability in green alder patch structure had less of an association with abiotic conditions than expected, suggesting future changes in physical structure at undisturbed sites may have limited environmental impact at the plot scale. However, at the patch scale, increases in snow depth, likely related to canopy complexity, were negatively associated with tundra specialist richness, potentially due to phenological limitations associated with shortened growing seasons. In summary, our data suggest emergent properties exist at the patch scale that are not apparent at the plot scale such that plot-scale measurements do not represent variation in understory community composition across the landscape. The results presented here will inform future work addressing spatial variability in shrub impacts on ecosystem function and increase our understanding of understory community variation within alder patch habitats at the taiga–tundra ecotone.

As the global population increases, the expansion of road networks has led to the destruction and disturbance of terrestrial and aquatic habitats. Road-related stressors have significant effects on both lotic and lentic habitats. While there are several systematic reviews that evaluate the effects of roads on lotic environments, there are none that consider their effects on lentic habitats only. We conducted a literature review to achieve two objectives: (1) to summarize the effects of roads on the physical, chemical, and biological properties of lentic environments; and (2) to identify biases and gaps in our current knowledge of the effects of roads on lentic habitats, so that we could find promising areas for future research. Our review found 172 papers published between 1970 and 2020. The most frequently studied stressors associated with roads included road salt and heavy metal contamination (67 and 43 papers, respectively), habitat fragmentation (37 papers), and landscape change (14 papers). These stressors can lead to alterations in conductivity and chloride levels, changes in lake stratification patterns, increases in heavy metal concentrations in water and organisms, and significant mortality as amphibians disperse across roadways. We also identified a variety of other stressors that may be understudied based on their frequency of appearance in our search results, including polycyclic aromatic hydrocarbons, road dust, increased accessibility, hydrological changes, noise pollution, dust suppressants, sedimentation, invasive species introductions, and water withdrawal. Our review indicated that there are strong geographic biases in published studies, with 57.0% examining North American sites and 30.2% examining European sites. Furthermore, there were taxonomic biases in the published literature, with most studies focusing on amphibians (41.7%), fish (15.6%), and macroinvertebrates (14.6%), while few considered zooplankton (8.3%), diatoms (7.3%), amoebas (5.2%), water birds (3.1%), reptiles (2.1%), and macrophytes (1.0%). Based on our review, we have identified promising areas for future research for each of the major stressors related to roadways. However, we speculate that rectifying the geographic and taxonomic bias of our current knowledge could significantly advance our understanding of the impacts of roads on lentic environments, thereby better informing environmental management of these important habitats.

ABSTRACT Winter storms in eastern Canada can bring heavy precipitation, including large amounts of freezing rain. The resulting ice accumulation on structures such as trees and power lines can lead to widespread power outages and damage to infrastructure. The objective of this study is to provide a better understanding of the processes that led to extreme freezing rain events over New Brunswick (NB), Canada, during past events and how they may change in the future. To accomplish this, freezing rain events that affected the power network over NB were identified and analysed using high-resolution convection-permitting simulations. These simulations were produced from 2000 to 2013 climate data and using the pseudo global warming (WRF-PGW) approach, assuming warmer climate conditions. Our results show that through the process of cold air damming, the Appalachians enhance the development of strong temperature inversions, leading to an increase in the amount of freezing rain in central and southern NB. The occurrence of freezing rain events generally decreases by 40% in southern and eastern NB, while the occurrence of long-duration events (>6 h) increases slightly in northwestern NB in the WRF-PGW simulation. Overall, key local orographic effects that influence atmospheric conditions favorable for freezing precipitation were identified. This knowledge will enable us to better anticipate the impact of climate change on similar storms.

ABSTRACT A devastating storm struck southern Manitoba, Canada on 10–13 October 2019, producing a large region of mainly sticky and wet snow. Accumulations reached 75 cm, wind gusts exceeded 100 km h−1, and surface temperature (T) remained near 0°C (−1°C ≤ T ≤ 1°C) for up to 88 h. It produced the largest October snowfall and was the earliest to produce at least 20 cm since 1872 in Winnipeg. These factors led to unparalleled damage and power restoration challenges for Manitoba Hydro and, with leaves still largely on vegetation, the most damaging storm to Winnipeg’s trees ever recorded. The storm’s track was uncommon, and produced elevated convection related to buoyancy-driven instability and conditional symmetric instability (CSI), with a moist absolutely unstable layer (MAUL) near 500 hPa. Instabilities were released via lift through lower-tropospheric warm advection and frontogenesis, differential cyclonic vorticity advection, and jet streak dynamics. Precipitation bands, elevated convection, and lake effect snow bands enhanced local snowfall. Snow adhering to structures was not always wet but, when present, it sometimes occurred because of incomplete freezing of particles partially melted aloft in a near-surface (<100 m deep) inversion. Although other storms over the historical record have produced a similar combination of severe precipitation, temperature and wind conditions, none have done this for such a long period.

Abstract Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we aim to understand better how LSMs function under different spin-up states, which facilitates addressing the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modélisation Environmentale Communautaire—Surface and Hydrology (MESH) framework and its embedded Canadian land surface scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5–1.5 m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200–1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

A traditional engineering-based approach to hydro-economic modelling is to connect a partial equilibrium economic assessment, e.g., changes in sectoral production, to a detailed water resources system model. Since the 1990s, another approach emerged where water data are incorporated into a macro-economic model, e.g., a computable general equilibrium or input-output model, to estimate both direct and indirect economic impacts. This study builds on these different approaches and compares the outcomes from three models in the transboundary Saskatchewan River Basin in Canada. The economic impacts of drought and socioeconomic development are estimated using an engineering-based model, a macro-economic model, and a model that integrates a water resources model and a macro-economic model. Findings indicate that although the integrated model is more challenging to develop, its results seem most relevant for water allocation, owing to capturing both regional and sectoral economic interdependencies and key features of the water resources system in more detail. • We compare three hydro-economic modelling approaches in a transboundary river basin. • Their applicability is examined under drought and economic development scenarios. • Usefulness of integrating water management and macroeconomic models is demonstrated. • Ignoring linkages between basins and sectors affects the model simulation results. • This may mislead water allocation decision-making in transboundary river basins.

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Lower Nelson River Basin, Manitoba, Canada Hydroelectricity makes up almost 97% of electricity generated in Manitoba, of which over 70% of its generation capacity is installed along the Lower Nelson River (LNR). In this study, 19 climate projections representing ~ 87% of climatic variability over Hudson Bay Drainage Basin are applied to coupled hydrologic-operations models to estimate water supply and hydropower generation potential changes under future climates. Future inflow to the forebay of the main hydropower generating stations along LNR is expected to increase in spring and summer but decrease in winter and fall. Consequently, hydropower generation potential is projected to increase for spring, the historical flood season, which may lead to reduced reservoir inflow retention efficiency. In extremely dry climatic simulations, winter seasons see a reduction in reservoir inflow and hydropower generation potential, up to 35% and 37% in 2021–2050 and 2041–2070, respectively. Projected changes in reservoir inflow and hydropower generation potential continue to diverge over time, with dry scenarios becoming drier and wet becoming wetter, yielding high basin climate sensitivity and uncertainty with system supply and generation potential. Despite the presence of statistically significant individual trends and changes, there is a low agreement within the climate ensemble. Analysis of system robustness shows adjustment of the operations along LNR should be considered over time to better leverage changing seasonal water supply. • Unique dynamic coupling of climate-hydrologic-operations models. • Projected reservoir inflow and hydropower generation potential for LNRB. • No significant change or trend in mean or median values due to uncertainty. • Wet seasons are getting wetter, dry seasons are getting drier. • Increase in uncertainty and extremes under future climates poses operational challenge.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1 million square kilometer study domain. The study comprises 13 models covering a wide range of model types from Machine Learning based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways: either by aggregating model outputs and the reference to basin-level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models – while losing less performance in spatial and spatio-temporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale. This is especially true for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The Machine Learning based model was not included here as is not setup to simulate AET, SSM, and SWE. (6) All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download data and model outputs.

The Great Lakes (GL) in North America are among the largest freshwater resources on the planet facing serious eutrophication problems as a result of excessive nutrient loadings due to population and economic growth. More than a third of Canada's GDP is generated in and around the GL. Hence, the economic interests affected by pollution and pollution control are high. New policies to reduce pollution are often insufficiently informed due to the lack of integrated models and methods that provide decision-makers insight into the direct and indirect economic impacts of their policies. This study fills this knowledge gap and estimates the impacts of different total phosphorus (TP) restriction policy scenarios across the GL. A first of its kind multi-regional hydro-economic model is built for the Canadian GL, extended to include TP emissions from point and non-point sources. This optimization model is furthermore extended with a pollution abatement cost function that allows sectors to also take technical measures to meet the imposed pollution reduction targets. The latter is a promising new avenue for extending existing hydro-economic input-output modeling frameworks. The results show decision-makers the least cost-way to achieve different TP emission reduction targets. The estimated cost to reduce TP emissions by 40% in all GL amounts to a total annual cost of 3 billion Canadian dollars or 0.15% of Canada's GDP. The cost structure changes substantially as policy targets become more stringent, increasing the share of indirect costs and affecting not only the economic activities around the GL, but the economy of Canada as a whole due to the tightly interwoven economic structure.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper discusses the scientific and technical challenges of the large-scale modelling of cold region systems and reports recent modelling developments, focussing on MESH, the Canadian community hydrological land surface scheme. New cold region process representations include improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole pond storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multistage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. These illustrate the current capabilities and limitations of cold region modelling, and the extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

Increasing incidences of eutrophication and groundwater quality impairment from agricultural nitrogen pollution are threatening humans and ecosystem health. Minimal improvements in water quality have been achieved despite billions of dollars invested in conservation measures worldwide. Such apparent failures can be attributed in part to legacy nitrogen that has accumulated over decades of agricultural intensification and that can lead to time lags in water quality improvement. Here, we identify the key knowledge gaps related to landscape nitrogen legacies and propose approaches to manage and improve water quality, given the presence of these legacies.

Intensification of agriculture and increased insecticide use have been implicated in global losses of farmland biodiversity and ecosystem services. We hypothesized that increased insecticide applications (proportion of area treated with insecticides) in Canada's expansive agricultural landscapes are due, in part, to shifts toward more simplified landscapes. To assess this relationship, we analyzed data from the Canadian Census of Agriculture spanning 20 years including five census periods (1996-2016) and across 225 census units within the four major agricultural regions of Pacific, Prairie, Central, and Atlantic Canada. Generalized mixed effects models were used to evaluate if changes in landscape simplification - defined as the proportion of farmland in crops (cereals, oilseeds, pulses and fruit/vegetables) - alongside other farming and climatic variables, influenced insecticide applications over time. Bayesian spatial-temporal models were further used to estimate the strength of the relationship with landscape simplification over time. We found that landscape simplification increased in 89% and insecticide applications increased in 70% of the Census Division spatial units during the 1996-2016 period. Nationally, significant increases in landscape simplification were observed in the two most agriculturally intensive regions of Prairie (from 55% to 63%) and Central (from 51% to 60%) Canada. For both regions, landscape simplification was a strong and significant predictor of higher insecticide applications, even after accounting for other factors such as climate, farm economics, farm size and land use practices (e.g., area in cash crops and tillage). If current trends continue, we estimated that insecticide applications will increase another 10%-20% by 2036 as a result of landscape simplification alone. To avoid increased reliance on toxic insecticides, agri-environmental policies need to consider that losing diverse natural habitat can increase insect pest pressure and resistance with negative environmental consequences extending beyond the field.

Abstract. Significant challenges from changes in climate and land use face sustainable water use in the Canadian Prairies ecozone. The region has experienced significant warming since the mid-20th century, and continued warming of an additional 2 ∘C by 2050 is expected. This paper aims to enhance understanding of climate controls on Prairie basin hydrology through numerical model experiments. It approaches this by developing a basin-classification-based virtual modelling framework for a portion of the Prairie region and applying the modelling framework to investigate the hydrological sensitivity of one Prairie basin class (High Elevation Grasslands) to changes in climate. High Elevation Grasslands dominate much of central and southern Alberta and parts of south-western Saskatchewan, with outliers in eastern Saskatchewan and western Manitoba. The experiments revealed that High Elevation Grassland snowpacks are highly sensitive to changes in climate but that this varies geographically. Spring maximum snow water equivalent in grasslands decreases 8 % ∘C−1 of warming. Climate scenario simulations indicated that a 2 ∘C increase in temperature requires at least an increase of 20 % in mean annual precipitation for there to be enough additional snowfall to compensate for enhanced melt losses. The sensitivity in runoff is less linear and varies substantially across the study domain: simulations using 6 ∘C of warming, and a 30 % increase in mean annual precipitation yields simulated decreases in annual runoff of 40 % in climates of the western Prairie but 55 % increases in climates of eastern portions. These results can be used to identify those areas of the region that are most sensitive to climate change and highlight focus areas for monitoring and adaptation. The results also demonstrate how a basin classification-based virtual modelling framework can be applied to evaluate regional-scale impacts of climate change with relatively high spatial resolution in a robust, effective and efficient manner.

Using data from five long-term field sites measuring soil moisture, we show the limitations of using soil moisture observations alone to constrain modelled hydrological fluxes. We test a land surface model, Modélisation Environnementale communautaire-Surface Hydrology/Canadian Land Surface Scheme, with two configurations: one where the soil hydraulic properties are determined using a pedotransfer function (the texture-based calibration) and one where they are assigned directly (the hydraulic properties-based calibration). The hydraulic properties-based calibration outperforms the texture-based calibration in terms of reproducing changes in soil moisture storage within a 1.6 m deep profile at each site, but both perform reasonably well, especially in the summer months. When the models are constrained using observations of changes in soil moisture, the predicted hydrological fluxes are subject to very large uncertainties associated with equifinality. The uncertainty is larger for the hydraulic properties-based calibration, even though the performance was better. We argue that since the pedotransfer functions constrain the model parameters in the texture-based calibrations in an unrealistic way, the texture-based calibration underestimates the uncertainty in the fluxes. We recommend that reproducing observed cumulative changes in soil moisture storage should be considered a necessary but insufficient criterion of model success. Additional sources of information are needed to reduce uncertainties, and these could include improved estimation of the soil hydraulic properties and direct observations of fluxes, particularly evapotranspiration.

Mountain regions are an important regulator in the global water cycle through their disproportionate water contribution. Often referred to as the “Water Towers of the World”, mountains contribute 40%–60% of the world's annual surface flow. Shade is a common feature in mountains, where complex terrain cycles land surfaces in and out of shadows over daily and seasonal scales, which can impact water use. This study investigated the turbulent water and carbon dioxide (CO2) fluxes during the snow‐free period in a subalpine wetland in the Canadian Rocky Mountains, from 7 June to 10 September 2018. Shading had a significant and substantial effect on water and CO2 fluxes at our site. When considering data from the entire study period, each hourly increase of shade per day reduced evapotranspiration (ET) and gross primary production (GPP) by 0.42 mm and 0.77 g C m−2, equivalent to 17% and 15% per day, respectively. However, the variability in shading changed throughout the study, it was stable to start and increased towards the end. Only during the peak growing season, the site experienced days with both stable and increasing shade. During this time, we found that shade, caused by the local complex terrain, reduced ET and potentially increased GPP, likely due to enhanced diffuse radiation. The overall result was greater water use efficiency during periods of increased shading in the peak growing season. These findings suggest that shaded subalpine wetlands can store large volumes of water for late season runoff and are productive through short growing seasons.

Flow regimes are critical for determining physical and biological processes in rivers, and their classification and regionalization traditionally seeks to link patterns of flow to physiographic, climate and other information. There are many approaches to, and rationales for, catchment classification, with those focused on streamflow often seeking to relate a particular response characteristic to a physical property or climatic driver. Rationales include such topics as Prediction in Ungauged Basins (PUB), and providing guidance for model selection in poorly understood hydrological systems. The Annual Daily Hydrograph (ADH) is a first-order easily visualized integrated expression of catchment function, and over many years the average ADH is a distinct hydrological signature that differentiate catchments from each other. In this study, we use t-SNE, a state-of-the-art technique of dimensionality reduction, to classify 17110 ADHs for 304 reference catchments in mountainous Western North America. t-SNE is chosen over other conventional methods of dimensionality reduction (e.g. PCA) as it presents greater separability of ADHs, which are projected on a 2D map where the similarities are evaluated according to their map distance. We then utilize a Deep Learning encoder to upgrade the non-parametric t-SNE to a parametric approach, enhancing its capability to address ’unseen’ samples. Results showed that t-SNE successfully clustered ADHs of similar flow regimes on the 2D map and allowed more accurate classification with KNN. In addition, many compact clusters on the 2D map in the coastal Pacific Northwest suggest information redundancy in the local streamflow network. The t-SNE map provides an intuitive way to visualize the similarity of high-dimensional data of ADHs, groups catchments with like characteristics, and avoids the reliance on subjective hydrometric indicators. This article is protected by copyright. All rights reserved.

Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

• The precipitation increase can offset the impact of warming on mountain snow hydrology. • The offsetting role of precipitation is effective at the high elevations and high latitudes. • The projected precipitation elasticity of annual runoff increases as latitude decreases. • The projected precipitation elasticity of peak snowpack increases as latitude increases. • Elasticities indicated that runoff changes are primarily attributed to precipitation change. Whether or not the impact of warming on mountain snow and runoff can be offset by precipitation increases has not been well examined, but it is crucially important for future downstream water supply. Using the physically based Cold Regions Hydrological Modelling Platform (CRHM), elasticity (percent change in runoff divided by change in a climate forcing) and the sensitivity of snow regimes to perturbations were investigated in three well-instrumented mountain research basins spanning the northern North American Cordillera. Hourly meteorological observations were perturbed using air temperature and precipitation changes and were then used to force hydrological models for each basin. In all three basins, lower temperature sensitivities of annual runoff volume ( ≤ 6% °C −1 ) and higher sensitivities of peak snowpack (−17% °C −1 ) showed that annual runoff was far less sensitive to temperature than the snow regime. Higher and lower precipitation elasticities of annual runoff (1.5 – 2.1) and peak snowpack (0.7 – 1.1) indicated that the runoff change is primarily attributed to precipitation change and, secondarily, to warming. A low discrepancy between observed and simulated precipitation elasticities showed that the model results are reliable, and one can conduct sensitivity analysis. The air temperature elasticities, however, must be interpreted with care as the projected warmings range beyond the observed temperatures and, hence, it is not possible to test their reliability. Simulations using multiple elevations showed that the timing of peak snowpack was most sensitive to temperature. For the range of warming expected from North American climate model simulations, the impacts of warming on annual runoff, but not on peak snowpack, can be offset by the size of precipitation increases projected for the near-future period 2041–2070. To offset the impact of 2 °C warming on annual runoff, precipitation would need to increase by less than 5% in all three basins. To offset the impact of 2 °C warming on peak snowpack, however, precipitation would need to increase by 12% in Wolf Creek in Yukon Territory, 18% in Marmot Creek in the Canadian Rockies, and an amount greater than the maximum projected at Reynolds Mountain in Idaho. The role of increased precipitation as a compensator for the impact of warming on snowpack is more effective at the highest elevations and higher latitudes. Increased precipitation leads to resilient and strongly coupled snow and runoff regimes, contrasting sharply with the sensitive and weakly coupled regimes at low elevations and in temperate climate zones.

• A novel physically based glacier hydrological model has been developed in CRHM. • The model considers processes such as blowing snow and sublimation, avalanches, firnification, glacier mass balance and energy-budget of snow/ice. • The model was driven with both in-situ and reanalysis data and evaluated with respect to albedo, mass balance, and runoff. • The hydrology of two partially glacierized catchments was simulated without any calibration of streamflow parameters. • The long term increases in discharge are due to increased glacier ice melt. A comprehensive glacier hydrology model was developed within the Cold Regions Hydrological Modelling platform (CRHM) to include modules representing wind flow over complex terrain, blowing snow redistribution and sublimation by wind, snow redistribution by avalanches, solar irradiance to sloping surfaces, surface sublimation, glacier mass balance and runoff, meltwater and streamflow routing. The physically based glacier hydrology model created from these modules in CRHM was applied to simulate the hydrology of the instrumented, glacierized and rapidly deglaciating Peyto and Athabasca glacier research basins in the Canadian Rockies without calibration of parameters from streamflow. It was tested against observed albedo, point and aggregated glacier mass balance, and streamflow and found to successfully simulate surface albedo, snow redistribution, snow and glacier accumulation and ablation, mass balance and streamflow discharge, both when driven by in-situ observations and reanalysis forcing data. Long term modelling results indicate that the increases in discharge from the 1960s to the present are due to increased glacier ice melt contributions, despite declining precipitation and snow melt.

The retreat of mountain glaciers affects mountain hazards and hydrology, and new methods are needed to rapidly map glacier retreat at planetary scales. We automatically map 14,329 glaciers (30,063 km 2 ) in British Columbia and Alberta, Canada, from 1984 to 2020 using satellite image archives from the Landsat 4, 5, 7 and 8 missions and reveal an acceleration in area loss that commenced in 2011. Glacier fragmentation, disappearance, and proglacial lake development also accelerated, as did the retreat of glaciers to higher elevations. Our annually-resolved method relies on the existence of previously published and manually validated glacier inventories from the mid-1980s and mid-2000's. Our methods performed well for clean ice glaciers, had occasional errors when proglacial lakes were present, and consistently underestimated the area of debris-covered glaciers. Clean ice glacier area loss accelerated sevenfold between the early [1984–2010] and late [2011−2020] epochs. This acceleration yielded rates of area shrinkage of −49 ± 7 km 2 a −1 [early] and − 340 ± 40 km 2 a −1 [late] with accelerated losses (32-fold increase) for small glaciers on Vancouver Island over the last decade. Glacier fragmentation accelerated from 26 ± 5.6 fragments a −1 to 88 ± 39 fragments a −1 . About 1141 glaciers fell below our 0.05 km 2 detection limit and so disappeared from our database, representing a loss of 8%. Proglacial lake area growth accelerated from 9.2 ± 1.1 km 2 a −1 to 49 ± 4.5 km 2 a −1 . We also observed an acceleration in the upwards migration of median glacier elevations for clean ice glaciers from 0.31 ± 0.08 m a −1 to 4.7 ± 0.7 m a −1 . Our workflow demonstrates the advantages of annual resolution glacier inventories and contributes towards the implementation of planetary mapping of glaciers and glacier attributes at annual resolution. • We produced annually-resolved [1984–2020], western Canadian glacier inventory. • Glaciers retreat, fragmentation and disappearance accelerated over last decade. • Our workflow could yield planetary-scale, annual glacier inventories.

Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrised or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analysing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (1) training a machine-learning (ML) error model using the input data of the process-based model and other available variables, (2) estimation of local explanations (i.e., contributions of each variable to an individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analysing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ML model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation-emitted long wave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.

Stream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. This study presents a unique example of an indirectly lake-headed stream, that is, a lake that only has transient subsurface hydrologic connections to the stream and no surface connections. The interaction of groundwater and lake water, and the subsurface connectivity between the lake and the headwater spring determine the resulting stream temperature. Radiation dominated the non-advective fluxes in the stream energy balance. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.

Subalpine regions of the Canadian Rocky Mountains are expected to experience continued changes in hydrometeorological processes due to anthropogenically mediated climate warming. As a result, fresh water supplies are at risk as snowmelt periods occur earlier in the year, and glaciers contribute less annual meltwater, resulting in longer growing seasons and greater reliance on rainfall to generate runoff. In such environments, wetlands are potentially important components that control runoff processes, but due to their location and harsh climates their hydrology is not well studied. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with MixSIAR, a Bayesian mixing model, to understand relative source water contributions and mixing within Burstall Wetland, a subalpine wetland (1900 m a.s.l.), and the larger Burstall Valley. These results were combined with climate data from the Burstall Valley to understand hydrometeorological controls on Burstall Wetland source water dynamics over spatiotemporal timescales. Our results show that the seasonal isotopic patterns within Burstall Wetland reflect greater reliance on snowmelt in spring and rainfall in the peak and post-growing season periods. We found a substantial degree of mixing between precipitation (rain and snow) and stored waters in the landscape, especially during the pre-growing season. These findings suggest that longer growing seasons in subalpine snow-dominated landscapes put wetlands at risk of significant water loss and increased evaporation rates potentially leading to periods of reduced runoff during the peak- growing season and in extreme cases, wetland dry out.

Wetlands in Montane and Subalpine Subregions are increasingly recognized as important hydrologic features that support ecosystem function. However, it is currently not clear how climate trends will impact wetland hydrological processes (e.g., evaporative fluxes) across spatiotemporal scales. Therefore, identifying the factors that influence wetland hydrologic response to climate change is an important step in understanding the sensitivity of these ecosystems to environmental change. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with climate data, to determine the spatiotemporal variability in isotopic signatures of wetland source waters and understand the influence of evaporative fluxes on wetlands in the Kananaskis Valley. Our results show that the primary runoff generation mechanism changes throughout the growing season resulting in considerable mixing in wetland surface waters. We found that evaporative fluxes increased with decreasing elevation and that isotopic values became further removed from meteoric water lines during the late peak- and into the post-growing seasons. These findings suggest that a change in the water balance in favor of enhanced evaporation (due to a warmer and longer summer season than present) will not only lead to greater water loss from the wetlands themselves but may also reduce the water inputs from their catchments.

Beavers are a keystone species known to strategically impound streamflow by building dams. Beaver colonization involves upstream ponding; after abandonment, the dams degrade, and the ponds slowly drain. This ponding-draining cycle likely modifies peatland nutrient availability, which is an important control on vegetation distribution and productivity. We compared soil mineral nutrient supply patterns in a beaver-dammed peatland in the Canadian Rocky Mountains over the growing and senescence study seasons during 2020. We used a nested design, comparing nutrient supply with ion-exchange probes among a full beaver pond (FBP with deep and shallow ponding), a drained beaver pond (DBP at its centre and margin) and unimpacted fen (UF at hummock and hollow hydrologic zones). Overall, FBP had lower soil total inorganic nitrogen (TIN) and nitrate (NO3), and higher ammonium (NH4) and phosphorus (PO4) supplies compared to UF. Interestingly, beaver pond drainage tended to restore the nutrient supply to its original status. The patterns we found in nutrient supply were consistent between the growing and senescence seasons. The key drivers of nutrient dynamics were water table level and soil temperature at 5 cm depth (TSoil); however, the controls affected each of the nutrients differently. Deepening of the water table level and higher TSoil non-linearly increased TIN/NO3 but decreased NH4 and PO4. We suggest that the variations in peatland nutrient availabilities in response to the beaver’s ponding-draining cycle may support downstream ecosystem heterogeneity and plant community composition diversity at a longer time scale.

Abstract. Lakes are key ecosystems within the global biogeosphere. However, the environmental controls on the biological productivity of lakes – including surface temperature, ice phenology, nutrient loads, and mixing regime – are increasingly altered by climate warming and land-use changes. To better characterize global trends in lake productivity, we assembled a dataset on chlorophyll-a concentrations as well as associated water quality parameters and surface solar radiation for temperate and cold-temperate lakes experiencing seasonal ice cover. We developed a method to identify periods of rapid net increase of in situ chlorophyll-a concentrations from time series data and applied it to data collected between 1964 and 2019 across 343 lakes located north of 40∘. The data show that the spring chlorophyll-a increase periods have been occurring earlier in the year, potentially extending the growing season and increasing the annual productivity of northern lakes. The dataset on chlorophyll-a increase rates and timing can be used to analyze trends and patterns in lake productivity across the northern hemisphere or at smaller, regional scales. We illustrate some trends extracted from the dataset and encourage other researchers to use the open dataset for their own research questions. The PCI dataset and additional data files can be openly accessed at the Federated Research Data Repository at https://doi.org/10.20383/102.0488 (Adams et al., 2021).

• Snow, glaciers, wetlands, frozen ground and permafrost needed in hydrological models. • Water quality export by coupling biochemical transformations to cold regions processes. • Hydrological sensitivity to land use depends on cold regions processes. • Strong cold regions hydrological sensitivity to climate warming. Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM’s capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations.

Hydrological conditions in cold regions have been shown to be sensitive to climate change. However, a detailed understanding of how regional climate and basin landscape conditions independently influence the current hydrology and its climate sensitivity is currently lacking. This study, therefore, compares the climate sensitivity of the hydrology of two basins with contrasted landscape and meteorological characteristics typical of eastern Canada: a forested boreal climate basin (Montmorency) versus an agricultural hemiboreal climate basin (Acadie). The physically based Cold Regions Hydrological Modelling (CRHM) platform was used to simulate the current and future hydrological processes. Both basin landscape and regional climate drove differences in hydrological sensitivities to climate change. Projected peak SWE were highly sensitive to warming, particularly for milder baseline climate conditions and moderately influenced by differences in landscape conditions. Landscape conditions mediated a wide range of differing hydrological processes and streamflow responses to climate change. The effective precipitation was more sensitive to warming in the forested basin than in the agricultural one, due to reductions in forest canopy interception losses with warming. Under present climate, precipitation and discharge were found to be more synchronized in the greater relief and slopes of the forested basin, whereas under climate change, they are more synchronized in the agricultural basin due to reduced infiltration and storage capacities. Flow through and over agricultural soils translated the increase in water availability under a warmer and wetter climate into higher peak discharges, whereas the porous forest soils dampened the response of peak discharge to increased available water. These findings help diagnose the mechanisms controlling hydrological response to climate change in cold regions forested and agricultural basins.

• A real-time ice-jam risk assessment system was developed to better regulate reservoir discharges. • The modelling system improves ice-jam flood predictions considering the influence of reservoir regulation. • Machine learning with deterministic modelling provides more accurate ice-jam flood predictions of regulated rivers. • The modelling system was successfully verified for the Sanhuhekou bend reach regulated by the Sanshenggong reservoir. To effectively alleviate ice-jam flood disasters, it is necessary to carry out hazard assessments and predictions of ice-jam flooding influenced by the operational scheme of a reservoir. However, traditional hydrologic flood routing techniques cannot effectively address the huge uncertainties caused by the many factors that lead to ice-jam flooding. In this paper, a hazard assessment system for regulating flood discharge schemes is developed; it is composed of a machine learning (ML) model, Long Short-Term Memory (LSTM), and a river-ice dynamic model (RIVICE) within a probabilistic method. The modelling system is to aid in the challenge of predicting ice-jam flooding downstream of reservoirs. The LSTM model forecasts the downstream flow under the operational discharge scheme and, combined with the RIVICE model, the backwater level profile of ice jams can be forecasted. Furthermore, a set of backwater level profiles can be provided by probabilistic modelling, and the probability of ice-jam flood inundation can be calculated by comparing backwater levels with the elevation of the river bank; this information can be used to warn of the hazard induced by operational discharges to better aid in the preparedness and mitigation of ice-jam floods. This system was tested successfully for the ice-cover breakup period in the spring of 2008 and 2018 along the Sanhuhekou bend reach of the Yellow River in China.

Brominated disinfection by-products (Br-DBPs) can form during chlorination of drinking water in treatment plants (DWTP). Regulations exist for a small subset of Br-DBPs; However, hundreds of unregulated Br-DBPs have been detected and limited information exists on their occurrence, concentrations, and seasonal trends. Here, a data-independent precursor isolation and characteristic fragment (DIPIC-Frag) method was optimized to screen chlorinated waters for Br-DBPs. There were 553 Br-DBPs detected with m/z values ranging from 170.884 to 497.0278 and chromatographic retention times from 2.4 to 26.2 min. With MS 2 information, structures for 40 of the 54 most abundant Br-DBPs were predicted. The method was then applied to a year-long study in which raw, clear well, and finished water were analyzed monthly. The 54 most abundant unregulated Br-DBPs were subjected to trend analysis. Br-DBPs with higher oxygen-to-carbon (O/C) and bromine-to-carbon (Br/C) ratios increased as water moved from the clear well to the finished stage, which indicated the dynamic formation of Br-DBPs. Monthly trends of unregulated Br-DBPs were compared to raw water parameters such as natural organic matter, temperature, and total bromine, but no correlations were observed. It was found that total concentrations of bromine (TBr) in finished water (0.04–0.12 mg/L) were consistently and significantly greater than in raw water (0.013–0.038 mg/L, P < 0.001), suggesting the introduction of bromine during the disinfection process. Concentrations of TBr in treatment units, rather than raw water, were significantly correlated to 34 of the Br-DBPs at α = 0.05. This study provides the first evidence that monthly trends of unregulated Br-DBPs can be associated with the concentration of TBr in treated waters. - Ultrahigh resolution mass spectrometry was used to identify novel brominated disinfection byproducts in a Canadian water treatment facility. - Several hundred novel brominated compounds were identified of which 54 were assigned chemical structures. - Seasonal variation in the generated DBPs were assessed over 11 months of sampling. - Increases in total bromine in drinking water was noted with progress thru the treatment process.

• Maps of organic layer thickness and fuel load were developed using machine learning. • Tree species was the most important variable in the final random forest model. • Error in our final models was close to the natural variability we expected to find. • The resultant maps will help improve fuel consumption models. Forest organic layers are important soil carbon pools that can, in the absence of disturbance, accumulate to great depths, especially in lowland areas. Across the Canadian boreal forest, fire is the primary disturbance agent, often limiting organic layer accumulation through the direct consumption of these fuels. Organic layer thickness (OLT) and fuel load (OLFL) are common physical attributes used to characterize these layers, especially for wildland fire science. Understanding the drivers and spatial distribution of these attributes is important to improve predictions of fire behaviour, emissions and effects models. We developed maps of OLT and OLFL using machine learning approaches (weighted K-nearest neighbour and random forests) for the forested region of the province of Alberta, Canada (538, 058 km 2 ). The random forests approach was found to be the best approach to model the spatial distribution of these forest floor attributes. A databased of 3, 237 OLT and 594 OLFL plots were used to train the models. The error in our final model, particularly for OLT (5 cm), was relatively close to the variability we would expect to find naturally (3 cm). The dominant tree species was the most important covariate in the models. Age, solar radiation, spatial location, climate variables and surficial geology were also important drivers, although their level of importance varied between tree species and depended on the modelling method that was used.

Metal leaves are commercially available for decoration purposes and offers a low-cost alternative to sputtering thin metal films. Although thin metal leaves have been sparingly used in physical and chemical sensing and solar cells, their application has been limited primarily due to lack of a simple patterning methods and to form microscale features with them. Here, a low-cost, rapid and simple xurography based cutting method has been developed for direct pattering of metal leaves. The method was able to pattern features with line width of < 100 µm and it was also able to cut patterns with a pitch of < 100 µm. Conductive lines < 250 µm were also achieved which is a sufficient resolution for application in sensors and most biomedical devices. The versatile capability of this method to cut various geometric shapes like circle, rectangle, triangles and hexagons was also demonstrated. The method is robust and can be applied to pattern leaves made of several materials or which gold, silver, palladium, aluminum and copper were demonstrated. This patterning method was used to fabricate contact electrodes for chemiresistive sensors with low and high surface roughness. These sensors were evaluated using the resistance and noise characteristics. The peak-to-peak noise for gold contact electrodes (11.5 nA) for chemiresistive sensors was significantly lower than the copper tape contact electrodes (18.2 nA). The process was also used to fabricate gold interdigitated electrodes for biamperometric glucose sensing at low potential (~10 mV). Finally, the method was used to indirectly pattern gold leaf on a shrink film to fabricate high surface 3D electrodes costing around one-fifth (~20%) of a sputtered gold electrode.

Globally, maize ( Zea mays , a C4-plant) and alfalfa ( Medicago sativa , a C3-plant) are common and economically important crops. Predicting the response of their water use efficiency, WUE , to changing hydrologic and climatic conditions is vital in helping farmers adapt to a changing climate. In this study, we assessed the effective leaf area index ( eLAI - the leaf area most involved in CO 2 and H 2 O exchange) and stomatal conductance in canopy scale in maize and alfalfa fields. In the process we used a theoretically-based photosynthesis C3-C4 model (C3C4PM) and carbon and water vapour fluxes measured by Eddy Covariance towers at our study sites. We found that in our study sites the eLAI was in the range of 25–32% of the observed total LAI in these crops. WUE s were in range of 8–9 mmol/mol. C3C4PM can be used in predictions of stomatal conductance and eLAI responses in C3 and C4 agricultural crops to elevated CO 2 concentration and changes in precipitation and temperature under future climate scenarios. • ~25 (maize) & 32% (alfalfa) of the observed crop LAI was involved in photosynthesis. • Extinction coefficient for beam radiation was 1.08 (maize) and 0.84 (alfalfa). • Canopy stomatal conductance, SC , was ~0.13 (maize) and ~0.15 (alfalfa). • Effective LAI and canopy SC can be evaluated by Eddy Covariance records.

Abstract This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

Abstract Globally, the extent of inland wetlands has declined by approximately 70% since the start of the twentieth century, resulting in the loss of important wetland-associated ecosystem services. We evaluate the drivers of wetland values in agricultural landscapes to increase the effectiveness and reliability of benefit transfer tools to assign values to local wetland services. We reviewed 668 studies that analyzed wetland ecosystem services within agricultural environments and identified 45 studies across 22 countries that provided sufficient economic information to be included in a quantitative meta-analysis. We developed meta-regression models to represent provisioning and regulating wetland ecosystem services and identify the main drivers of these ecosystem service categories. Provisioning wetland ecosystem service values were best explained (direction of effects in parenthesis) by high-income variable (+), peer-reviewed journal publications (+), agricultural total factor productivity index (−) and population density (+), while agricultural total factor productivity index (−), income level ( +) and wetland area (−) had significant effects on regulating wetland ecosystem service values. Our models can help estimate wetland values more reliably across similar regions because they have significantly lower transfer errors (66 and 185% absolute percentage error for the provisioning and regulating models, respectively) than the errors from unit value transfers. Model predicted wetland values ($/Ha/Year) range from $0.62 to $11,216 for regulating services and $0.95 to $2,122 for provisioning services and vary based on the differences in the levels of the variables (in the wetland locations) that best explained the estimated models.

Reclamation of wetlands, including peatlands, is legally required in the Athabasca oil sands region following bitumen extraction via surface mining which leaves large open pits that are backfilled with saline tailings waste. Six years of hydrochemical data (2013 – 2018) from the Sandhill Fen Watershed (SFW), a 52-ha upland-peatland catchment that was built upon highly saline soft tailings, were used to evaluate salinity and ion patterns and provide insight on its trajectory. In general, electrical conductivity (EC) increased throughout SFW from 1) reduced inflow and outflow, 2) changes in water table positions and 3) increased mixing of site-wide waters. Salinity has increased site-wide over time as EC increased by an average of 1585 and 2313 µS/cm in the wetland and margins, respectively from 2013 to 2018. The uplands were the only region where EC declined by 1747 µS/cm over the six years. There is also evidence of mixing with underlying tailings waste (Na-Cl dominant) as the chemical composition of SFW waters shifted from largely Ca-dominant in 2013 (> 90%) to Na-dominant by 2018 (> 70%). Based on its current conditions, SFW cannot support freshwater peat-forming bryophytes and is most chemically similar to naturally occurring saline fens. A shift in design strategies is recommended (replicating saline instead of freshwater peatlands) to increase the success of these systems. Changes in site-wide average annual electrical conductivity (µS/cm) from 2013 to 2018. • Electrical conductivity (EC) and Na+ increased site-wide from 2013 to 2018. • Decreased inflow and outflow increased EC and ion accumulation. • Higher water tables diluted wetland EC and increased margin EC (ion mobilization). • Increased mixing with tailings waste shifted waters from Ca +2 to Na + dominant. • Waters have become similar to brackish marshes and saline fens.

A simple algorithm is provided for randomly sampling a set of N +1 weights such that their sum is constrained to be equal to one, analogous to randomly subdividing a pie into N +1 slices where the probability distribution of slice volumes are identically distributed. The cumulative density and probability density functions of the random weights are provided. The algorithmic implementation for the random number sampling are made available. This algorithm has potential applications in calibration, uncertainty analysis, and sensitivity analysis of environmental models. Three example applications are provided to demonstrate the efficiency and superiority of the proposed method compared to alternative sampling methods. • Present unbiased method to sample weights that sum up to 1. • Examples demonstrating the benefit of unbiased sampling. • Code made available in multiple languages.

The surface properties of electrically conductive membranes (ECMs) govern their advanced abilities. During operation, these properties may differ considerably from their initially measured properties. Depending on their operating conditions, ECMs may undergo various degrees of passivation. ECM passivation can detrimentally impact their real time performance, causing large deviations from expected behaviour based on their initially measured properties. Quantifying these changes will enable consistent performance comparisons across the active and electrically conductive membrane research field. As such, consistent methods must be established to quantify ECM membrane properties. In this work, we proposed three standardized methods to assess the electrochemical, chemical, and physical stability of such membrane coatings: 1) electrochemical oxidation, 2) surface scratch testing, and 3) pressurized leaching. ECMs were synthesized by the most common approach - coating support ultrafiltration (UF) and/or microfiltration (MF) polyethersulfone (PES) membranes with carbon nanotubes (CNT) cross-linked with polyvinyl alcohol (PVA) and two types of cross-linkers (either succinic acid (SA) or glutaraldehyde (GA)). We then evaluated these ECMs based on the three standardized methods: 1) We evaluated electrochemical stability as a function of electro-oxidation induced by applying anodic potentials. 2) We measured the scratch resistance to quantify the surface mechanical stability. 3) We measured physical stability by quantifying the leaching of PVA during separation of a model foulant (polyethylene oxide (PEO)). Our methods can be extended to all types of electrically conductive membranes including MF, UF, nanofiltration (NF), and reverse osmosis (RO) ECMs. We propose that these fundamental measurements are critical to assessing the viability of ECMs for industrial MF, UF, NF, and RO applications.•Anodic-oxidation was used to check the electrochemical stability of ECMs•Depth of penetration resulted from scratch test is an indicator of the electrically conductive membrane coating's mechanical stability•The leaching of the main components forming the nanolayer was quantified to assess the membranes' physical stability.

Activities of gut microbiomes are often overlooked in assessments of ecotoxicological effects of environmental contaminants. Effects of the polycyclic aromatic hydrocarbon, benzo[a]pyrene (BaP) on active gut microbiomes of juvenile fathead minnows (Pimephales promelas) were investigated. Fish were exposed for two weeks, to concentrations of 0, 1, 10, 100, or 1000 μg BaP g-1 in the diet. The active gut microbiome was characterized using 16S rRNA metabarcoding to determine its response to dietary exposure of BaP. BaP reduced alpha-diversity at the greatest exposure concentrations. Additionally, exposure to BaP altered community composition of active microbiome and resulted in differential proportion of taxa associated with hydrocarbon degradation and fish health. Neighborhood selection networks of active microbiomes were not reduced with greater concentrations of BaP, which suggests ecological resistance and/or resilience of gut microbiota. The active gut microbiome had a similar overall biodiversity as that of the genomic gut microbiota, but had a distinct composition from that of the 16S rDNA profile. Responses of alpha- and beta-diversities of the active microbiome to BaP exposure were consistent with that of genomic microbiomes. Normalized activity of microbiome via the ratio of rRNA to rDNA abundance revealed rare taxa that became active or dormant due to exposure to BaP. These differences highlight the need to assess both 16S rDNA and rRNA metabarcoding to fully derive bacterial compositional changes resulting from exposure to contaminants.

A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Abstract Climate change is a threat to the 500 Gt carbon stored in northern peatlands. As the region warms, the rise in mean temperature is more pronounced during the non-growing season (NGS, i.e., winter and parts of the shoulder seasons) when net ecosystem loss of carbon dioxide (CO 2 ) occurs. Many studies have investigated the impacts of climate warming on NGS CO 2 emissions, yet there is a lack of consistency amongst researchers in how the NGS period is defined. This complicates the interpretation of NGS CO 2 emissions and hinders our understanding of seasonal drivers of important terrestrial carbon exchange processes. Here, we analyze the impact of alternative definitions of the NGS for a peatland site with multiple years of CO 2 flux records. Three climatic parameters were considered to define the NGS: air temperature, soil temperature, and snow cover. Our findings reveal positive correlations between estimates of the cumulative non-growing season net ecosystem CO 2 exchange (NGS-NEE) and the length of the NGS for each alternative definition, with the greatest proportion of variability explained using snow cover ( R 2 = 0.89, p < 0.001), followed by air temperature ( R 2 = 0.79, p < 0.001) and soil temperature ( R 2 = 0.54, p = 0.006). Using these correlations, we estimate average daily NGS CO 2 emitted between 1.42 and 1.90 gCO 2 m −2 , depending on which NGS definition is used. Our results highlight the need to explicitly define the NGS based on available climatic parameters to account for regional climate and ecosystem variability.

Abstract. Increasing hydrological variability, accelerating population growth and urbanisation, and the resurgence of water resources development projects have all indicated increasing tension among the riparian countries of transboundary rivers. While a wide range of disciplines develop their understandings of conflict and cooperation in transboundary river basins, few process-based interdisciplinary approaches are available for investigating the mechanism of conflict and cooperation. This article aims to develop a meta-theoretical socio-hydrological framework that brings the slow and less visible societal processes into existing hydrological–economic models and enables observations of the change in the cooperation process and the societal processes underlying this change, thereby contributing to revealing the mechanism that drives conflict and cooperation. This framework can act as a “middle ground”, providing a system of constituent disciplinary theories and models for developing formal models according to a specific problem or system under investigation. Its potential applicability is demonstrated in the Nile, Lancang–Mekong, and Columbia rivers.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

Fire plays a major role in the structuring and functioning of boreal ecosystems. As peatlands are important components of boreal forests, the impact of fire upon these wetter ecosystems is increasingly studied, but with the focus on treed peatlands and Sphagnum-dominated bogs so far. Important fires occurring more frequently in the past decade in southern Northwest Territories (Canada) provide the opportunity to assess early post-fire vegetation regeneration in open rich fens (one, two, and five years post-fire) and to better understand early recovery succession. We aimed to (i) evaluate whether and how open rich fens are affected by fire, and (ii) describe short-term vegetation regeneration for both bryophytes and vascular species. A shift was observed between pioneer bryophytes and brown mosses between the second and fifth year post-fire. Vascular plants, especially slow-growing species and the ones reproducing mainly by seeds, recovered partially. The first bryophyte species recovering were pioneer species adapted to colonize burned environments such as Marchantia polymorpha L. or Ceratodon purpureus (Hedw.) Brid. For vascular plant species, the ones previously present and able to regrow rapidly from unburned plant structures (base of tussocks, rhizomes, roots) were represented by species like Betula glandulosa Michx. or Carex aquatilis Wahlenb. The wetter conditions and lower fuel availability of fen depressional biotopes were important factors controlling the resistance and regeneration of species associated with them.

Heavy metal pollution on earth has evolved into a global issue causing serious risks to human health and other living entities and having an impact on sustainability. Accurate identification of metal contamination is often carried out in centralized facilities involving sampling, transportation, and the need for highly trained personnel, which becomes expensive, often causes delays in response to potential tragedies, and is prone to sample properties changes. Rapid, affordable methods for point-of-care (POC) detection of heavy metals with reasonable accuracy are ideal to address these challenges enabling diligent monitoring of metal pollution. There have been many POC systems reported, however, the systems that could work with real samples in which heavy metals are present in a complex form at a low concentration are limited. Sample preparation is often needed for the accurate identification of metal ions. Microfluidics offers tremendous potential for sample preparation and integration with various detection methods such as optical and electrochemical methods for POC detection of heavy metals. This review is limited to reviewing the reported microfluidic-based POC devices for heavy metal sensing and providing a brief perspective on the integration of microwave sensing methods with microfluidic devices for heavy metal detection. This review starts with introducing microfluidic-based heavy metal sensing using optical and electrochemical methods and then focuses on briefly discussing the development and potential of integrating microwave sensing with microfluidic devices for heavy metal sensing. The principle of each method and the limit of detection are briefly discussed.

Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.

Baseflow originating primarily from groundwater is a critical streamflow component, although its accurate estimation is fraught with significant difficulties. This study estimates baseflow through existing graphical and digital filter methods, using actual streamflow data from a gauging station at the Alder Creek Watershed (ACW) and synthetic streamflow data at ten study locations within the same watershed simulated with HydroGeoSphere (HGS) (Aquanty Inc., 2018). There are four widely used graphical (Institute for Hydrology, 1980; Sloto and Crouse, 1996; Aksoy et al., 2008) and six digital filtering (Lyne and Hollick, 1979; Chapman and Maxwell, 1996; Furey and Gupta, 2001; Eckhardt, 2005; Tularam and Ilahee, 2008; Aksoy et al., 2009) baseflow separation approaches compared in this study. To determine the most optimal approach, baseflow estimates from real data are assessed based on the subjective concept of hydrologic plausibility, while baseflow estimates obtained from a HGS streamflow record with graphical and digital filtering methods are compared to those computed directly by HGS. Overall, results from this study indicate that baseflow hydrographs reveal a seasonal pattern at the ACW. During wintertime, streamflow is composed almost entirely of baseflow, whereas during summertime, baseflow only consists approximately 20% to 60% of streamflow. After comparing baseflow estimates with those computed by HGS, the most optimal approaches at the ten study locations are assessed. Results show that the best approach at six study locations is the FUKIH (Aksoy et al., 2009) approach, while at three locations, the Chapman and Maxwell (1996) approach and for one location, the Eckhardt (2005) approach performed the best. In conclusion, it is inferred that the most optimal approach within the ACW varies spatially.

Data-driven hydrological modeling has seen rapid development in recent years owing to its flexibility to approximate the complex relationships between driving forces and hydrological fluxes. However, traditional data-driven models typically cannot simultaneously capture the processes that pose both chronic and acute impacts on streamflow, thus impeding further inference. Therefore, this study presents a baseflow-filtered hydrological inference model to gain insights into hydrological processes in irrigated watersheds. The proposed model starts with separating the streamflow process into two sub-processes using a process-based baseflow separation method. Each sub-process is simulated through a new interpretable data-driven model. The resulting hydrological inferences facilitate the identification of the dominant factors influencing flows in saturated and unsaturated zones. The proposed model is applied to three irrigated watersheds, and the evaluation metrics show that the proposed model outperforms two conventional data-driven models. Our findings reveal that predictors associated with air temperature and long-term (i.e., monthly) irrigation are mainly responsible for characterizing baseflow dynamics, while precipitation and short-term (i.e., semi-weekly or weekly) irrigation are primarily responsible for describing overland flow and interflow dynamics. The fidelity of the derived hydrological inference is further demonstrated through sensitivity analysis. The results show that the relative importance of predictors not only reflects their significance on model performance, but also influence the changes on streamflow.

Biofouling detection enables the adoption of effective cleaning strategies for biofouling prevention. This work investigates the use of electrical impedance spectroscopy (EIS) to monitor the biofilm development and the use of electric fields to mitigate biofouling on the surface of gold-coated membranes. The multi-bacterial suspension was injected into a two-electrode crossflow filtration system where the permeate flux and impedance spectra were recorded to monitor the biofilm growth. Permeate flux declined over time while the impedance at low frequency regions (<10 Hz) rapidly decreased with fouling at the early stages of fouling, and then gradually decreased as biofilm matured. The normalized diffusion-related impedance (Rd), an EIS-derived parameter, was extracted to determine the sensitivity of EIS detection. We observed that impedance-based detection was more sensitive to changes as compared to the decline of permeate flux during the early stage of biofouling. With early detection of fouling, fouling mitigation strategies could be applied more effectively. Further, under the same conditions as fouling detection, either applying an intermittent cathodic potential (−1.5 V) or cross-flow flushing delayed the biofilm growth on the electrically conductive membranes (ECMs). EIS sensitivity was repeatably recovered across four cycles of mechanical fouling removal. Hence ECMs were demonstrated to play a dual function: EIS-enabled detection of biofouling evolution and surface biofouling mitigation.

Abstract Wastewater monitoring of SARS-CoV-2 enables early detection and monitoring of the COVID-19 disease burden in communities and can track specific variants of concern. We determined proportions of the Omicron and Delta variants across 30 municipalities covering >75% of the province of Alberta (population 4.5 million), Canada, during November 2021–January 2022. Larger cities Calgary and Edmonton exhibited more rapid emergence of Omicron than did smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a medium-sized northern community that has many workers who fly in and out regularly. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden by late December, before the peak in newly diagnosed clinical cases throughout Alberta in mid-January. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of SARS-CoV-2 variants.

The availability of dissolved silicon (DSi) exerts an important control on phytoplankton communities in freshwater environments: DSi limitation can shift species dominance to non-siliceous algae and increase the likelihood of harmful algal blooms. The availability of DSi in the water column in turn depends on the dissolution kinetics of amorphous silica (ASi), including diatoms frustules and phytoliths. Here, batch dissolution experiments conducted with diatom frustules from three diatom species and synthetic Aerosil OX 50 confirmed the previously reported non-linear dependence of ASi dissolution rate on the degree of undersaturation of the aqueous solution. At least two first-order dissolution rate constants are therefore required to describe the dissolution kinetics at high (typically, ≥0.55) and low (typically, <0.55) degrees of undersaturation. Our results further showed aqueous ferrous ion (Fe2+), which is ubiquitous in anoxic waters, strongly inhibited ASi dissolution. The inhibition is attributed to the preferential binding of Fe2+ to Q2 groups (i.e., surface silicate groups bonded to the silica lattice via two bridging oxygen) which stabilizes the silica surface. However, further increasing the aqueous Fe2+ concentration likely catalyzes the detachment of Q3 groups (i.e., silicate groups bonded to the silica lattice via three bridging oxygen) from the surface. Overall, our study illustrates the manyfold effects the aqueous solution composition, notably the inhibition effect of Fe2+ under anoxic conditions, has on ASi dissolution. The results help to explain the controversial redox dependence of DSi internal loading from sediments, which is vital to quantitatively understanding silicon (Si) cycling in freshwater systems.

Most pharmaceuticals are found at trace concentrations in aquatic systems, but their continuous release and potential accumulation can lead to adverse health effects in exposed organisms. Concentrations can vary temporally, driven by variations in discharges of receiving waters, sorption to sediments, and other biotic and abiotic exchange processes. The principal aim of this research was to better understand the occurrence, trends, and dynamics of pharmaceuticals in a cold-climate, riverine environment. To this end, a suite of seven representative antipsychotic pharmaceuticals was measured upstream and downstream of two wastewater treatment plants (WWTPs) in Saskatchewan, Canada, located in the South Saskatchewan River and Wascana Creek, respectively, across three seasons. Concentrations of analytes were in the ng/L range and generally greater downstream of both WWTPs compared to upstream. Some compounds, including the tricyclic antidepressant amitriptyline, which was the most abundant analyte in water and sediment from both sites and across seasons, reached low μg/L concentrations. Data collected from this research effort indicate contamination with antipsychotic pharmaceuticals, with the potential to adversely impact exposed organisms.

Conservation practices that reduce nutrient and soil loss from agricultural lands to water are fundamental to watershed management programs. Avoiding trade-offs of conservation practices is essential to the successful mitigation of watershed phosphorus (P) losses. We review documented trade-offs associated with conservation practices, particularly those practices that are intended to control and trap P from agricultural sources. A regular theme is the trade-off between controlling P loss linked to sediment while increasing dissolved P losses (no-till, cover crops, vegetated buffers, constructed wetlands, sediment control basins). A variety of factors influence the degree to which these trade-offs occur, complicated by their interaction and uncertainties associated with climate change. However, acknowledging these trade-offs and anticipating their contribution to watershed outcomes are essential to the sustainability of conservation systems.

Abstract The stable isotopes of hydrogen and oxygen in xylem water are often used to investigate tree water sources. But this traditional approach does not acknowledge the contribution of water stored in the phloem to transpiration and how this may affect xylem water and source water interpretations. Additionally, there is a prevailing assumption that there is no isotope fractionation during tree water transport. Here, we systematically sampled xylem and phloem water at daily and subdaily resolutions in a large lysimeter planted with Salix viminalis . Stem diurnal change in phloem water storage and transpiration rates were also measured. Our results show that phloem water is significantly less enriched in heavy isotopes than xylem water. At subdaily resolution, we observed a larger isotopic difference between xylem and phloem during phloem water refilling and under periods of tree water deficit. These findings contrast with the expectation of heavy‐isotope enriched water in phloem due to downward transport of enriched leaf water isotopic signatures. Because of previous evidence of aquaporin mediated phloem and xylem water transport and higher osmotic permeability of lighter hydrogen isotopologues across aquaporins, we propose that radial water transport across the xylem–phloem boundary may drive the relative depletion of heavy isotopes in phloem and their relative enrichment in xylem.

Abstract Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Abstract Ecohydrological investigations commonly use the stable isotopes of water (hydrogen and oxygen) as conservative ecosystem tracers. This approach requires accessing and analysing water from plant and soil matrices. Generally, there are six steps involved to retrieve hydrogen and oxygen isotope values from these matrices: (1) sampling, (2) sample storage and transport, (3) extraction, (4) pre‐analysis processing, (5) isotopic analysis, and (6) post‐processing and correction. At each step, cumulative errors can be introduced which sum to non‐trivial magnitudes. These can impact subsequent interpretations about water cycling and partitioning through the soil–plant‐atmosphere continuum. At each of these steps, there are multiple possible options to select from resulting in tens of thousands of possible combinations used by researchers to go from plant and soil samples to isotopic data. In a newly emerging field, so many options can create interpretive confusion and major issues with data comparability. This points to the need for development of shared standardized approaches. Here we critically examine the state of the process chain, reflecting on the issues associated with each step, and provide suggestions to move our community towards standardization. Assessing this shared ‘process chain’ will help us see the problem in its entirety and facilitate community action towards agreed upon standardized approaches.

Delineating the relative solubility of soil phosphorus (P) in agricultural landscapes is essential to predicting potential P mobilization in the landscape and can improve nutrient management strategies. This study describes spatial patterns of soil extractable P (easily, moderately, and poorly soluble P) in agricultural landscapes of the Red River basin and the southern Great Lakes region. Surface soils (0-30 cm) and select deeper cores (0-90 cm) were collected from 10 cropped fields ranging in terrain (near-level to hummocky), soil texture (clay to loam), composition (calcareous to noncalcareous), and climate across these differing glacial landscapes. Poorly soluble P dominated (up to 91%) total extractable P in the surface soils at eight sites. No differences in the relative solubilities of soil extractable P with microtopography were apparent in landscapes without defined surface depressions. In contrast, in landscapes with pronounced surface depressions, increased easily soluble P (Sol-P), and decreased soil P sorption capacity were found in soil in wetter, low-slope zones relative to drier upslope locations. The Sol-P pool was most important to soil P retention (up to 28%) within the surface depressions of the Red River basin and at sites with low-carbonate soils in the southern Lake Erie watershed (up to 28%), representing areas at elevated risk of soil P remobilization. This study demonstrates interrelationships among soil extractable P pools, soil development, and soil moisture regimes in agricultural glacial landscapes and provides insight into identifying potential areas for soil P remobilization and associated P availability to crops and runoff.

Abstract Software engineering (SE) methodologies are widely used in both academia and industry to manage the software development life cycle. A number of studies of SE methodologies involve interviewing stakeholders to explore the real‐world practice. Although these interview‐based studies provide us with a user's perspective of an organization's practice, they do not describe the concrete summary of releases in open‐source social coding platforms. In particular, no existing studies investigated how releases are evolved in open‐source coding platforms, which assist release planners to a large extent. This study explores software development patterns followed in open‐source projects to see the overall management's reflection on software release decisions rather than concentrating on a particular methodology. Our experiments on 51 software origins (with 1777k revisions and 12k releases) from the Software Heritage Graph Dataset (SWHGD) and their GitHub project boards (with 23k cards) reveal reasonably active project management with phase simplicity can release software versions more frequently and can follow the small release conventions of Extreme Programming. Additionally, the study also reveals that a combination of development and management activities can be applied to predict the possible number of software releases in a month ( ).

Resilience of plant communities to disturbance is supported by multiple mechanisms, including ecological legacies affecting propagule availability, species' environmental tolerances, and biotic interactions. Understanding the relative importance of these mechanisms for plant community resilience supports predictions of where and how resilience will be altered with disturbance. We tested mechanisms underlying resilience of forests dominated by black spruce (Picea mariana) to fire disturbance across a heterogeneous forest landscape in the Northwest Territories, Canada. We combined surveys of naturally regenerating seedlings at 219 burned plots with experimental manipulations of ecological legacies via seed addition of four tree species and vertebrate exclosures to limit granivory and herbivory at 30 plots varying in moisture and fire severity. Black spruce recovery was greatest where it dominated pre-fire, at wet sites with deep residual soil organic layers, and fire conditions of low soil or canopy combustion and longer return intervals. Experimental addition of seed indicated all species were seed-limited, emphasizing the importance of propagule legacies. Black spruce and birch (Betula papyrifera) recruitment were enhanced with vertebrate exclusion. Our combination of observational and experimental studies demonstrates black spruce is vulnerable to effects of increased fire activity that erode ecological legacies. Moreover, black spruce relies on wet areas with deep soil organic layers where other species are less competitive. However, other species can colonize these areas if enough seed is available or soil moisture is altered by climate change. Testing mechanisms underlying species' resilience to disturbance aids predictions of where vegetation will transform with effects of climate change.The online version contains supplementary material available at 10.1007/s10021-022-00772-7.

Abstract Warming temperatures in the circumpolar north have led to new discussions around climate-driven frontiers for agriculture. In this paper, we situate northern food systems in Canada within the corporate food regime and settler colonialism, and contend that an expansion of the conventional, industrial agriculture paradigm into the Canadian North would have significant socio-cultural and ecological consequences. We propose agroecology as an alternative framework uniquely accordant with northern contexts. In particular, we suggest that there are elements of agroecology that are already being practiced in northern Indigenous communities as part of traditional hunter-gatherer food systems. We present a framework for agroecology in the North and discuss its components of environmental stewardship, economies, knowledge, social dimensions and governance using examples from the Dehcho region, Northwest Territories, Canada. Finally, we discuss several challenges and cautions in creating policy around agroecology in the North and encourage community-based research in developing and testing this framework moving forward.

Amplified climate warming in high latitudes is expected to affect growing season timing of the vast boreal biome. It is unclear whether the presence of permafrost (perennially frozen ground) might have an influence on changes in growing season timing. This study examined how different environmental variables explained, either directly or indirectly, the variation in growing season timing of boreal forest stands with and without permafrost. We expected that environmental variables explaining the variation in growing season timing differed or had different explanatory power depending on permafrost presence or absence. The growing season was delineated from daily gross primary productivity (GPP) time series derived from 40 site-year data of net ecosystem carbon dioxide exchange measured with eddy covariance techniques over five black spruce (Picea mariana [Mill.])-dominated boreal forest stands in North America. In permafrost-free forest stands, a combination of start in canopy ‘green-up’ in spring and the timing of air and soil temperature increasing above freezing explained the start-of-season (SOSGPP). Results from commonality analysis and structural equation modeling suggest that canopy ‘green-up’ and air temperature directly affected SOSGPP in permafrost-free forest stands. In addition, soil temperature acted as mediator for an indirect effect of air temperature on SOSGPP. In contrast, none of the environmental variables, or their combination, explained the variation in SOSGPP in forest stands with permafrost. The explanatory power of environmental variables was more consistent regarding the end-of-season (EOSGPP). In both, forest stands with and without permafrost, EOSGPP was directly explained by mean soil water content in the fall and the first day of continuous snowpack formation. A better understanding how environmental variables control SOSGPP and EOSGPP in forest stands with and without permafrost will help to refine parameterizations of the boreal biome in Earth system models.

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.

In many practical geochemical systems that are at the center of providing indispensable energy, resources and service to our society, (bio)geochemical reactions are coupled with other physical processes, such as multiphase flow, fracturing and deformation. Predictive understanding of these processes in hosting and evolving porous media is the key to design reliable and sustainable practices. In this article, we provide a brief review of recent developments and applications of reactive transport modeling to study geochemically driven processes and alteration in porous media. We also provide a perspective on opportunities and challenges for continuously developing and expanding the role of this valuable methodology to advance fundamental understanding and transferable knowledge of various dynamic geochemical systems.

This paper describes the occurrences and conditions before Mesoscale Convective Systems (MCSs) are initiated over the Canadian Prairies, using 10 years of observations and convection-permitting climate model simulations. The features of MCSs occurring in summer were analyzed using the Regional Deterministic Reforecast System (RDRS, hourly and 10-km grid spacing) and ECMWF Reanalysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) model simulations (FSCT, 4-km grid spacing). MCSs were defined and tracked using the Method for Object-Based Diagnostic Evaluation-Time Domain (MTD). MTD-identified MCSs were divided into short−/long-lived and daytime/nighttime, considering the longevities and initiation times. FCST showed the skills to simulate MCSs but overestimated MCS features compared to RDRS. Fifteen meteorological parameters were calculated using sounding data from FCST to determine pre-conventional conditions of MCSs (at init. -9, −6, −3, and − 1 h). The distributions of parameters were tested to determine the significance of differences between short- and long-lived MCSs. The key findings are as follows: 1) long-lived daytime MCSs (LLM12) showed favorable thermodynamic processes and 2) long-lived nighttime MCSs (LLM00) were initiated based on dynamic processes. We also found that the most appropriate parameters (i.e., those that were statistically different in short- and long-lived MCSs) to determine the longevities of MCSs were 1) most unstable convective available potential energy and 2) vertical wind shear of 0–3 km.

Many mathematical models of natural phenomena are described by partial differential equations (PDEs) that consist of additive contributions from different physical processes. Classical methods for the numerical solution to such equations are monolithic in that all processes are treated with a single method. Additive numerical methods, in contrast, apply distinct methods to each additive term. There are, however, different ways mathematically to specify the additive terms, and it is not always clear which ways (if any) offer advantages over monolithic methods. This study compares the performance of two different additive splitting techniques (physics-based splitting and dynamic linearization) on a suite of eight test problems that involve advection, reaction, and diffusion with various 2-additive Runge–Kutta methods and (monolithic) Runge–Kutta–Chebyshev (RKC) methods. Results show that dynamic linearization generally outperforms physics-based splitting and so should be preferred as the splitting technique when splitting is required or otherwise desirable. RKC methods are the best performers on three of the eight problems, especially at coarse tolerances, but they can also be prone to severe underperformance.

The Grand River watershed is the largest in southern Ontario and assimilates thirty wastewater treatment plants (WWTP) with varied degrees of treatment. Many WWTPs are unable to effectively eliminate several contaminants of emerging concern (CECs) from final effluent, leading to measurable concentrations in surface waters. Exposures to CECs have reported impacts on oxidative stress measured through antioxidative enzymes (SOD, CAT, GPX). This study focuses on the effects of WWTP effluent on four Etheostoma (Darter) species endemic to the Grand River, by investigating if increased antioxidative response markers are present in darter brains downstream from the effluent outfall compared to an upstream reference site relative to the Waterloo, ON WWTP across two separate years (Oct 2020 and Oct 2021). This was assessed using transcriptional and enzyme analysis of antioxidant enzymes and an enzyme involved in serotonin synthesis, tryptophan hydroxylase (tph). In fall 2020, significant differences in transcript markers were found between sites and sexes in GSD with SOD and CAT showing increased expression downstream, in JD with both sexes showing increased SOD downstream, and an interactive effect for tph in RBD. Changes in transcripts aligned with enzyme activity where interactive effects with sex-related differences were observed in fish collected fall 2020. In contrast, transcripts measured in fall 2021 were increased upstream compared to downstream species in RBD and GSD. This study additionally displayed yearly, species and sex differences in antioxidant responses. Continued investigation on the impacts of CECs in effluent in non-target species is required to better understand WWTP effluent impacts.

In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.

Widespread application of poly- and per-fluoroalkyl substances (PFAS) has resulted in some substances being ubiquitous in environmental matrices. That and their resistance to degradation have allowed them to accumulate in wildlife and humans with potential for toxic effects. While specific substances of concern have been phased-out or banned, other PFAS that are emerging as alternative substances are still produced and are being released into the environment. This review focuses on describing three emerging, replacement PFAS: perfluoroethylcyclohexane sulphonate (PFECHS), 6:2 chlorinated polyfluoroalkyl ether sulfonate (6:2 Cl-PFAES), and hexafluoropropylene oxide dimer acid (HFPO-DA). By summarizing their physicochemical properties, environmental fate and transport, and toxic potencies in comparison to other PFAS compounds, this review offers insight into the viabilities of these chemicals as replacement substances. Using the chemical scoring and ranking assessment model, the relative hazards, uncertainties, and data gaps for each chemical were quantified and related to perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) based on their chemical and uncertainty scores. The substances were ranked PFOS > 6:2 Cl-PFAES > PFOA > HFPO-DA > PFECHS according to their potential toxicity and PFECHS > HFPO-DA > 6:2 Cl-PFAES > PFOS > PFOA according to their need for future research. Since future uses of PFAS remain uncertain in the face of governmental regulations and production bans, replacement PFAS will continue to emerge on the world market and in the environment, raising concerns about their general lack of information on mechanisms and toxic potencies.

Selenium (Se) is an environmental contaminant of global concern that can cause adverse effects in fish at elevated levels. Fish gut microbiome play essential roles in gastrointestinal function and host health and can be perturbed by environmental contaminants, including metals and metalloids. Here, an in-situ Se exposure of female finescale dace (Phoxinus neogaeus) using mesocosms was conducted to determine the impacts of Se accumulation on the gut microbiome and morphometric endpoints. Prior to this study, the gut microbiome of finescale dace, a widespread Cyprinid throughout North America, had not been characterized. Exposure to Se caused a hormetic response of alpha diversity of the gut microbiome, with greater diversity at the lesser concentration of 1.6 μg Se/L, relative to that of fish exposed to the greater concentration of 5.6 μg Se/L. Select gut microbiome taxa of fish were differentially abundant between aqueous exposure concentrations and significantly correlated with liver-somatic index (LSI). The potential effects of gut microbiome dysbiosis on condition of wild fish might be a consideration when assessing adverse effects of Se in aquatic environments. More research regarding effects of Se on field-collected fish gut microbiome and the potential adverse effects or benefits on the host is warranted.

Seeking available and economical carbon sources for denitrification process is an intractable issue for wastewater treatment. However, no study compared different types of waste sludge as carbon source from denitrification mechanism, organics utilization and microbial community aspects. In this study, primary and secondary sludge were pretreated by thermophilic bacteria (TB), and its hydrolysis or acidogenic liquid were prepared as carbon sources for denitrification. At C/N of 8-3, the variations of NO3--N and NO2--N were profiled in typical cycles and denitrification kinetics was analyzed. Primary sludge achieved a competitive NOX-N removal efficiency with less dosage than secondary sludge. Fourier transform infrared (FTIR) spectroscopy was introduced to analyze organic composition from functional-group perspective and the utilization of organic matters in different sludge carbon sources was investigated. To further analyze the microbial community shift in different denitrification systems, high-throughput sequencing technology was applied. Results showed that denitrifier Thauera, belonging to Proteobacteria, was predominant, and primary sludge acidogenic liquid enriched Thauera most intensively with relative abundance of 47.3%.

Flavins and siderophores secreted by various plants, fungi and bacteria under iron (Fe) deficient conditions play important roles in the biogeochemical cycling of Fe in the environment. Although the mechanisms of flavin and siderophore mediated Fe(III) reduction and dissolution under anoxic conditions have been widely studied, the influence of these compounds on Fe(II) oxidation under oxic conditions is still unclear. In this study, we investigated the kinetics of aqueous Fe(II) (17.8 μM) oxidation by O 2 at pH 5‒7 in the presence of riboflavin (oxidized (RBF) and reduced (RBFH 2 )) and desferrioxamine B (DFOB) as representative flavins and siderophores, respectively. Results showed that the addition of RBF/RBFH 2 or DFOB markedly accelerates the oxidation of aqueous Fe(II) by O 2 . For instance, at pH 6, the rate of Fe(II) oxidation was enhanced 20‒70 times when 10 μM RBFH 2 was added. The mechanisms responsible for the accelerated Fe(II) oxidation are related to the redox reactivity and complexation ability of RBFH 2 , RBF and DFOB. While RBFH 2 does not readily complex Fe(II)/Fe(III), it can activate O 2 and generate reactive oxygen species, which then rapidly oxidize Fe(II). In contrast, both RBF and DFOB do not reduce O 2 but react with Fe(II) to form RBF/DFOB-complexed Fe(II), which in turn accelerates Fe(II) oxidation. Furthermore, the lower standard reduction potential of the Fe(II)-DFOB complex, compared to the Fe(II)-RBF complex, correlates with a higher oxidation rate constant for the Fe(II)-DFOB complex. Our study reveals an overlooked catalytic role of flavins and siderophores that may contribute to Fe(II)/Fe(III) cycling at oxic-anoxic interfaces.

There is increasing interest in the cost-effectiveness and economic benefits of replacing traditional engineering-based ‘grey’ infrastructure with nature-based ‘green’ infrastructure in the water sector. This study builds on the emerging literature in this field and sets itself apart in several ways. New in this study is the focus on the interrelationship between green infrastructure, water treatment costs proxied by drinking water rates, and drinking water safety. The latter refers to adverse treated water quality incidents (AWQI's) such as unsatisfactory bacteriological test results that may lead to drinking water advisories when sufficiently severe. An integrated modelling framework is furthermore developed, accounting simultaneously for possible spatial spill-over effects due to watershed land cover and potential endogeneity embedded in the relationship between water treatment costs, drinking water billing, and the occurrence of AWQI's. Data from the water- and forest-abundant and densely populated Canadian province of Ontario were used and significant negative correlations between forested land area and both drinking water rates and AWQI's are observed. While causality underlying these relationships needs further investigation, these results indicate support for the use of techno-ecological nature-based solutions in drinking water risk management.

Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.

The eutrophication of freshwater systems is a pervasive issue in North America and elsewhere, which has been linked to elevated phosphorus (P) loading from watersheds. Most excess P is thought to originate from non-point agricultural sources, and less attention has been given to small rural point sources, such as bunker silos on livestock farms. Sophisticated management practices are rarely used to attenuate nutrients from bunker silo effluent, leaving simple vegetated buffer strips or riparian zones to protect surface water; however, the efficacy of these systems or larger constructed treatment systems is unclear. This study compared two systems receiving bunker silo effluent, one a natural riparian system with a vegetated buffer strip that is the most common practice and the other a constructed treatment system with a forebay, slag filter, and swale. The study quantified P retention within various subsections of each system and characterized the forms of stored P to infer the potential for remobilization. Results indicate that soils receiving bunker silo effluent represent considerable stores of legacy P in the landscape (750 and 3400 kg ha−1), the majority of which is stored in labile forms that may be vulnerable to remobilization under the waterlogged conditions that often occur in management practices and riparian zones. Some areas of the systems were able to store considerably more P than others, with the slag filter showing the greatest treatment efficacy. Spatial variability in stored P was apparent, where sections of the systems that directly received effluent retained more P than sections located farther away from bunker silos (indirect inputs). Results indicate that passive treatment systems become P saturated over time, limiting their longterm P removal efficacy. The efficacy of these systems may be improved with the inclusion of sorptive materials as a slag filter within the constructed treatment system significantly increased the life expectancy of that system. Greater understanding of both quantity and forms of P retained in systems and soils receiving bunker silo effluent will help develop management strategies that are more effective and longer-lasting for reducing excess P losses to surface water bodies.

Several studies have shown that large, experimental additions of nitrate (NO3) to eutrophic systems can mitigate large populations of nuisance cyanobacteria and that high NO3 concentrations can oxidize anoxic sediments. These studies are consistent with observations from numerous aquatic systems across a broad trophic range showing development of reduced surficial sediments precedes the formation of large cyanobacteria populations. We use 50+ years of data to explore whether high NO3 concentrations may have been instrumental both in the absence of large populations of cyanobacteria in eutrophic Hamilton Harbour, Lake Ontario in the 1970s when total phosphorus (TP) and total nitrogen (TN) concentrations were high, and in delaying large populations until August and September in recent decades despite much lower TP and TN. Our results indicate that large cyanobacteria population events do not occur at the central station in July-September when epilimnetic NO3 > 2.2 mg N L−1. The results further suggest that remedial improvements to wastewater treatment plant oxidation capacity may have been inadvertently responsible for high NO3 concentrations > 2.2 mg N L−1 and thus for mitigating large cyanobacteria populations. This also implies that large cyanobacteria populations may form earlier in the summer if NO3 concentrations are lowered.

Extensive efforts are underway to reduce phosphorus (P) export from the Lake Erie watershed. On the Canadian side, the Thames River is the largest tributary source of P to Lake Erie’s western basin. However, the role of dams in retaining and modifying riverine P loading to the lake has not been comprehensively evaluated. We assessed whether Fanshawe Reservoir, the largest dam reservoir on the Thames River, acts as a source or sink of P, using year-round discharge and water chemistry data collected in 2018 and 2019. We also determined how in-reservoir processes alter P speciation by comparing the dissolved reactive P to total P ratio (DRP:TP) in upstream and downstream loads. Annually, Fanshawe Reservoir was a net sink for P, retaining 25% (36 tonnes) and 47% (91 tonnes) of TP in 2018 and 2019, respectively. Seasonally, the reservoir oscillated between a source and sink of P. Net P release occurred during the spring of 2018 and the summers of 2018 and 2019, driven by internal P loading and hypolimnetic discharge from the dam. The reservoir did not exert a strong influence on DRP:TP annually, but ratio increases occurred during both summers, concurrent with water column stratification. Our analysis demonstrates that Fanshawe Reservoir is not only an important P sink on the Thames River, but also modulates the timing and speciation of P loads. We therefore propose that the potential of using existing dam reservoirs to attenuate downstream P loads should be more thoroughly explored alongside source based P mitigation strategies.

The Köppen-Geiger (KG) climate classification has been widely used to determine the climate at global and regional scales using precipitation and temperature data. KG maps are typically developed using a single product; however, uncertainties in KG climate types resulting from different precipitation and temperature datasets have not been explored in detail. Here, we assess seven global datasets to show uncertainties in KG classification from 1980 to 2017. Using a pairwise comparison at global and zonal scales, we quantify the similarity among the seven KG maps. Gauge- and reanalysis-based KG maps have a notable difference. Spatially, the highest and lowest similarity is observed for the North and South Temperate zones, respectively. Notably, 17% of grids among the seven maps show variations even in the major KG climate types, while 35% of grids are described by more than one KG climate subtype. Strong uncertainty is observed in south Asia, central and south Africa, western America, and northeastern Australia. We created two KG master maps (0.5° resolution) by merging the climate maps directly and by combining the precipitation and temperature data from the seven datasets. These master maps are more robust than the individual ones showing coherent spatial patterns. This study reveals the large uncertainty in climate classification and offers two robust KG maps that may help to better evaluate historical climate and quantify future climate shifts.

Soot deposition from wildfires decreases snow and ice albedo and increases the absorption of shortwave radiation, which advances and accelerates melt. Soot deposition also induces algal growth, which further decreases snow and ice albedo. In recent years, increasingly severe and widespread wildfire activity has occurred in western Canada in association with climate change. In the summers of 2017 and 2018, westerly winds transported smoke from extensive record-breaking wildfires in British Columbia eastward to the Canadian Rockies, where substantial amounts of soot were deposited on high mountain glaciers, snowfields, and icefields. Several studies have addressed the problem of soot deposition on snow and ice, but the spatiotemporal resolution applied has not been compatible with studying mountain icefields that are extensive but contain substantial internal variability and have dynamical albedos. This study evaluates spatial patterns in the albedo decrease and net shortwave radiation (K*) increase caused by soot from intense wildfires in Western Canada deposited on the Columbia Icefield (151 km2), Canadian Rockies, during 2017 and 2018. Twelve Sentinel-2 images were used to generate high spatial resolution albedo retrievals during four summers (2017 to 2020) using a MODIS bidirectional reflectance distribution function (BRDF) model, which was employed to model the snow and ice reflectance anisotropy. Remote sensing estimates were evaluated using site-measured albedo on the icefield's Athabasca Glacier tongue, resulting in a R2, mean bias, and root mean square error (RMSE) of 0.68, 0.019, and 0.026, respectively. The biggest inter-annual spatially averaged soot-induced albedo declines were of 0.148 and 0.050 (2018 to 2020) for southeast-facing glaciers and the snow plateau, respectively. The highest inter-annual spatially-averaged soot-induced shortwave radiative forcing was 203 W/m2 for southeast-facing glaciers (2018 to 2020) and 106 W/m2 for the snow plateau (2017 to 2020). These findings indicate that snow albedo responded rapidly to and recovered rapidly from soot deposition. However, ice albedo remained low the year after fire, and this was likely related to a bio-albedo feedback involving microorganisms. Snow and ice K* were highest during low albedo years, especially for south-facing glaciers. These large-scale effects accelerated melt of the Columbia Icefield. The findings highlight the importance of using large-area high spatial resolution albedo estimates to analyze the effect of wildfire soot deposition on snow and ice albedo and K* on icefields, which is not possible using other approaches.

The availability and quality of quad-pol synthetic aperture radar (SAR) datasets has increased substantially since the early 2000s, allowing for polarimetrically complete investigations of freshwater ice. These investigations have lead to improved ice classification methods, new understanding of microwave-ice scattering processes, and the potential for new methods to extract ice observables. Such analyses are predicated on the decomposition of the target’s polarimetric properties along mathematical or physical lines. This paper comprehensively reviews the underlying theory and contemporary application of radar polarimetric decomposition as it applies to freshwater ice systems. Modelling and investigation of lake ice, river ice, and glacial systems are discussed. We conclude with recommendations for further research, discussing the value of further development of freshwater-ice models, their use in characterization of the scattering process, and the potential for new methods to extract environmental observables.

Arctic Indigenous Peoples are among the most exposed humans when it comes to foodborne mercury (Hg). In response, Hg monitoring and research have been on-going in the circumpolar Arctic since about 1991; this work has been mainly possible through the involvement of Arctic Indigenous Peoples. The present overview was initially conducted in the context of a broader assessment of Hg research organized by the Arctic Monitoring and Assessment Programme. This article provides examples of Indigenous Peoples' contributions to Hg monitoring and research in the Arctic, and discusses approaches that could be used, and improved upon, when carrying out future activities. Over 40 mercury projects conducted with/by Indigenous Peoples are identified for different circumpolar regions including the U.S., Canada, Greenland, Sweden, Finland, and Russia as well as instances where Indigenous Knowledge contributed to the understanding of Hg contamination in the Arctic. Perspectives and visions of future Hg research as well as recommendations are presented. The establishment of collaborative processes and partnership/co-production approaches with scientists and Indigenous Peoples, using good communication practices and transparency in research activities, are key to the success of research and monitoring activities in the Arctic. Sustainable funding for community-driven monitoring and research programs in Arctic countries would be beneficial and assist in developing more research/monitoring capacity and would promote a more holistic approach to understanding Hg in the Arctic. These activities should be well connected to circumpolar/international initiatives to ensure broader availability of the information and uptake in policy development.

Monitoring the communal incidence of COVID-19 is important for both government and residents of an area to make informed decisions. However, continuous reliance on one means of monitoring might not be accurate because of biases introduced by government policies or behaviours of residents. Wastewater surveillance was employed to monitor concentrations of SARS-CoV-2 RNA in raw influent wastewater from wastewater treatment plants serving three Canadian Prairie cities with different population sizes. Data obtained from wastewater are not directly influenced by government regulations or behaviours of individuals. The means of three weekly samples collected using 24 h composite auto-samplers were determined. Viral loads were determined by RT-qPCR, and whole-genome sequencing was used to charaterize variants of concern (VOC). The dominant VOCs in the three cities were the same but with different proportions of sub-lineages. Sub-lineages of Delta were AY.12, AY.25, AY.27 and AY.93 in 2021, while the major sub-lineage of Omicron was BA.1 in January 2022, and BA.2 subsequently became a trace-level sub-variant then the predominant VOC. When each VOC was first detected varied among cities; However, Saskatoon, with the largest population, was always the first to present new VOCs. Viral loads varied among cities, but there was no direct correlation with population size, possibly because of differences in flow regimes. Population is one of the factors that affects trends in onset and development of local outbreaks during the pandemic. This might be due to demography or the fact that larger populations had greater potential for inter- and intra-country migration. Hence, wastewater surveillance data from larger cities can typically be used to indicate what to expect in smaller communities.

Rapid climate warming across northern high latitudes is leading to permafrost thaw and ecosystem carbon release while simultaneously impacting other biogeochemical cycles including nitrogen. We used a two-year laboratory incubation study to quantify concomitant changes in carbon and nitrogen pool quantity and quality as drivers of potential CO2 production in thawed permafrost soils from eight soil cores collected across the southern Northwest Territories (NWT), Canada. These data were contextualized via in situ annual thaw depth measurements from 2015 to 2019 at 40 study sites that varied in burn history. We found with increasing time since experimental thaw the dissolved carbon and nitrogen pool quality significantly declined, indicating sustained microbial processing and selective immobilization across both pools. Piecewise structural equation modeling revealed CO2 trends were predominantly predicted by initial soil carbon content with minimal influence of dissolved phase carbon. Using these results, we provide a first-order estimate of potential near-surface permafrost soil losses of up to 80 g C m−2 over one year in southern NWT, exceeding regional historic mean primary productivity rates in some areas. Taken together, this research provides mechanistic knowledge needed to further constrain the permafrost‑carbon feedback and parameterize Earth system models, while building on empirical evidence that permafrost soils are at high risk of becoming weaker carbon sinks or even significant carbon sources under a changing climate.

Lake Wilcox (LW), a shallow kettle lake located in southern Ontario, has experienced multiple phases of land use change associated with human settlement and residential development in its watershed since the early 1900s. Urban growth has coincided with water quality deterioration, including the occurrence of algal blooms and depletion of dissolved oxygen (DO) in the water column. We analyzed 22 years of water chemistry, land use, and climate data (1996-2018) using principal component analysis (PCA) and multiple linear regression (MLR) to identify the contributions of climate, urbanization, and nutrient loading to the changes in water chemistry. Variations in water column stratification, phosphorus (P) speciation, and chl-a (as a proxy for algal abundance) explain 76 % of the observed temporal trends of the four main PCA components derived from water chemistry data. MLR results further imply that the intensity of stratification, quantified by the Brunt-Väisälä frequency, is a major predictor of the changes in water quality. Other important factors explaining the variations in nitrogen (N) and P speciation, and the DO concentrations, are watershed imperviousness and lake chloride concentrations that, in turn, are closely correlated. We conclude that the observed in-lake water quality trends over the past two decades are linked to urbanization via increased salinization associated with expanding impervious land cover, rather than increasing external P loading. The rising salinity promotes water column stratification, which reduces the oxygenation of the hypolimnion and enhances internal P loading to the water column. Thus, stricter controls on the application and runoff of de-icing salt should be considered as part of managing eutrophication symptoms in lakes of cold climate regions.

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

Various functional DNA molecules have been used for the detection of environmental contaminants in water, but their practical applications have been limited. To address this gap, this review highlights the efforts to develop field-deployable water quality biosensors. The biosensor devices include microfluidic, lateral flow and paper-based devices, and other novel ideas such as the conversion of glucometers for the detection of environmental analytes. In addition, we also review DNA-functionalized hydrogels and their use in diffusive gradients in thin films (DGT) devices. We classify the sensors into one-step and two-step assays and discuss their practical implications. While the review is focused on works reported in the last five years, some classic early works are cited as well. Overall, most of the existing work only tested spiked water samples. Future work needs to shift to real environmental samples and the comparison of DNA-based sensors to standard analytical methods. • Recent development in field-deployable functional DNA based biosensors for environmental water monitoring reviewed. • Articulated the concept of one-step and two-step assays. • Microfluidic device, lateral flow device, paper, hydrogel, and glucose meter based examples reviewed.

Abstract Debris-covered glaciers are an important component of the mountain cryosphere and influence the hydrological contribution of glacierized basins to downstream rivers. This study examines the potential to make estimates of debris thickness, a critical variable to calculate the sub-debris melt, using ground-based thermal infrared radiometry (TIR) images. Over four days in August 2019, a ground-based, time-lapse TIR digital imaging radiometer recorded sequential thermal imagery of a debris-covered region of Peyto Glacier, Canadian Rockies, in conjunction with 44 manual excavations of debris thickness ranging from 10 to 110 cm, and concurrent meteorological observations. Inferring the correlation between measured debris thickness and TIR surface temperature as a base, the effectiveness of linear and exponential regression models for debris thickness estimation from surface temperature was explored. Optimal model performance ( R 2 of 0.7, RMSE of 10.3 cm) was obtained with a linear model applied to measurements taken on clear nights just before sunrise, but strong model performances were also obtained under complete cloud cover during daytime or nighttime with an exponential model. This work presents insights into the use of surface temperature and TIR observations to estimate debris thickness and gain knowledge of the state of debris-covered glacial ice and its potential hydrological contribution.

Abstract Reliable, long-term records of glacier mass change are invaluable to the glaciological and climate-change communities and used to assess the importance of glacier wastage on streamflow. Here we evaluate the in-situ observations of glacier mass change for Place (1982–2020) and Peyto glaciers (1983–2020) in western Canada. We use geodetic mass balance to calibrate a physically-based mass-balance model coupled with an ice dynamics routine. We find large discrepancies between the glaciological and geodetic records for the periods 1987–1993 (Place) and 2001–2006 (Peyto). Over the period of observations, the exclusion of ice dynamics in the model increased simulated cumulative mass change by ~10.6 (24%) and 7.1 (21%) m w.e. for Place and Peyto glacier, respectively. Cumulative mass loss using geodetic, modelled and glaciological approaches are respectively − 30.5 ± 4.5, − 32.0 ± 3.6, − 29.7 ± 3.6 m w.e. for Peyto Glacier (1982–2017) and − 45.9 ± 5.2, − 43.1 ± 3.1, − 38.4 ± 5.1 m w.e. for Place Glacier (1981–2019). Based on discrepancies noted in the mass-balance records for certain decades (e.g. 1990s), we caution the community if these data are to be used for hydrological model development.

Health risks of chronic exposure to microcystins (MCs), a family of aquatic contaminants produced mainly by cyanobacteria, are critical yet unsolved problems. Despite a few epidemiological studies, the metabolic profiles of humans exposed to MCs remain unknown, hindering the deep understanding of the molecular toxicity mechanisms. Here, sensitive nuclear magnetic resonance (NMR)- and liquid chromatography-mass spectrometry (LC-MS)-based metabolomics were applied to investigate the serum metabolic profiles of humans living near Lake Chao, where toxic cyanobacterial blooms occur annually. MCs were positively detected in 92 of 144 sera by ultra-high-pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with a median concentration of 0.016 μg/L. The estimated daily intake (0.15-0.27 μg MC-LReq/day) was less than the tolerable daily intake (TDI, 2.4 μg MC-LR for 60 kg adults) recommended by the World Health Organization (WHO). Obvious disruptions of the amino acid metabolism were confirmed and played important roles in renal impairments associated with serum MC burdens. Chronic oral exposure of mice to 30 μg MC-LR/kg body mass, which is less than the no observed adverse effect level, also led to obvious renal lesions and metabolic dysfunction. These observations provide the first evidence of metabolic disturbance of humans exposed to MCs and indicate that the WHO's TDI value determined traditionally should be lessened to protect human health effectively.

In situ bioremediation is a common remediation strategy for many groundwater contaminants. It was traditionally believed that (in the absence of mixing-limitations) a better in situ bioremediation is obtained in a more homogeneous medium where the even distribution of both substrate and bacteria facilitates the access of a larger portion of the bacterial community to a higher amount of substrate. Such conclusions were driven with the typical assumption of disregarding substrate inhibitory effects on the metabolic activity of enzymes at high concentration levels. To investigate the influence of pore matrix heterogeneities on substrate inhibition, we use a numerical approach to solve reactive transport processes in the presence of pore-scale heterogeneities. To this end, a rigorous reactive pore network model is developed and used to model the reactive transport of a self-inhibiting substrate under both transient and steady-state conditions through media with various, spatially correlated, pore-size distributions. For the first time, we explore on the basis of a pore-scale model approach the link between pore-size heterogeneities and substrate inhibition. Our results show that for a self-inhibiting substrate, (1) pore-scale heterogeneities can consistently promote degradation rates at toxic levels, (2) the effect reverses when the concentrations fall to levels essential for microbial growth, and (3) an engineered combination of homogeneous and heterogeneous media can increase the overall efficiency of bioremediation.

Endocrine-disrupting potential was evaluated during the sewage treatment process using in vitro bioassays. Aryl hydrocarbon receptor (AhR)-, androgen receptor (AR)-, glucocorticoid receptor (GR)-, and estrogen receptor (ER)-mediated activities were assessed over five steps of the treatment process. Bioassays of organic extracts showed that AhR, AR, and GR potencies tended to decrease through the sewage treatment process, whereas ER potencies did not significantly decrease. Bioassays on reverse-phase high-performance liquid chromatography fractions showed that F5 (log KOW 2.5-3.0) had great ER potencies. Full-scan screening of these fractions detected two novel ER agonists, arenobufagin and loratadine, which are used pharmaceuticals. These compounds accounted for 3.3-25% of the total ER potencies and 4% of the ER potencies in the final effluent. The well-known ER agonists, estrone and 17β-estradiol, accounted for 60 and 17% of the ER potencies in F5 of the influent and primary treatment, respectively. Fourier transform ion cyclotron resonance mass spectrometry analysis showed that various molecules were generated during the treatment process, especially CHO and CHOS (C: carbon, H: hydrogen, O: oxygen, and S: sulfur). This study documented that widely used pharmaceuticals are introduced into the aquatic environments without being removed during the sewage treatment process.

In vitro biotransformation assays with primary trout hepatocytes (RT-HEP) or liver subcellular fractions (RT-S9) have been proposed as valuable tools to help scientists and regulators better understand the toxicokinetics of chemicals. While both assays have been applied successfully to a diversity of neutral organic chemicals, only the RT-S9 assay has been applied to a large number of ionizable organic chemicals. Here, a combination of an in vitro biotransformation assay with RT-HEP with an active transport assay based on the permanent rainbow trout liver cell line RTL-W1 was used to qualitatively predict the potential hepatic clearance of nine psychotropic drugs with various degrees of ionization. Predictions were compared with rates of clearance measured in isolated perfused rainbow trout livers, and the importance of active transport was verified in the presence of the active transport inhibitor cyclosporin A. For the first time, it was demonstrated that a combination of biotransformation and active transport assays is powerful for the prediction of rates of hepatic clearance of ionizable chemicals. Ultimately, it is expected that this approach will allow for use of fewer animals while at the same time improving our confidence in the use of data from in vitro assays in chemical risk assessment.

Since the report of the RNA aptamer for theophylline, theophylline has become a key molecule in chemical biology for designing RNA switches and riboswitches. In addition, theophylline is an important drug for treating airway diseases including asthma. The classic RNA aptamer with excellent selectivity for theophylline has been used to design biosensors, although DNA aptamers are more desirable for stability and cost considerations. In this work, we selected DNA aptamers for theophylline, and all the top sequences shared the same binding motifs. Binding was confirmed using isothermal titration calorimetry and a nuclease digestion assay, showing a dissociation constant (Kd) around 0.5 μM theophylline. The Theo2201 aptamer can be truncated down to 23-mer while still has a Kd of 9.8 μM. The selectivity for theophylline over caffeine is around 250,000-fold based on a strand-displacement assay, which was more than 20-fold higher compared to the classic RNA aptamer. For other tested analogs, the DNA aptamer also showed better selectivity. Using the structure-switching aptamer sensor design method, a detection limit of 17 nM theophylline was achieved in the selection buffer, and a detection limit of 31 nM was obtained in 10% serum.

Abstract Machine learning (ML) models have been widely used for hydrological simulation. Previous studies have reported that conventional ML models fail to accurately simulate extreme flows which are crucial for design flood estimation and associated risk analysis in the context of climate change. Therefore, this study proposes a joint probabilistic rainfall‐runoff model (JPRR) for improving high‐to‐extreme flow projection. With the aid of paired copula constructions, bootstrap aggregation, and multi‐model ensemble approaches, the proposed model is able to effectively characterize the dependence relationships of predictors (i.e., precipitation time series with different moving sums) with various probability distributions. JPRR has been applied to four pristine basins in China, representing different climate zones and landscapes. The results reveal that JPRR significantly outperforms three well‐known ML models (i.e., random forest, artificial neural networks, and long short‐term memory) in high‐to‐extreme flow simulations. In JPRR, the copulas exhibiting the right tail dependence play a more important role in streamflow simulations at mountainous basins. Moreover, a significant difference in streamflow projections (from 2030 to 2099) derived from JPRR and benchmark models imply that flood risks from conventional ML models may be underestimated under changing climatic conditions.

Abstract What elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two‐state rainfall model, the CoSMoS‐2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential ( ) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank‐based CoSMoS‐2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula‐based CoSMoS‐2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS‐2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes.

Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process-based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific data sets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling” (CWARHM; pronounced “swarm”).

Abstract Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: ( go-fair.org )). Easy availability of FAIR data has become possible because the hydrology‐oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.

Abstract Wildfire occurrence and severity is predicted to increase in the upcoming decades with severe negative impacts on human societies. The impacts of upwind wildfire activity on glacier melt, a critical source of freshwater for downstream environments, were investigated through analysis of field and remote sensing observations and modeling experiments for the 2015–2020 melt seasons at the well‐instrumented Athabasca Glacier in the Canadian Rockies. Upwind wildfire activity influenced surface glacier melt through both a decrease in the surface albedo from deposition of soot on the glacier and through the impact of smoke on atmospheric conditions above the glacier. Athabasca Glacier on‐ice weather station observations show days with dense smoke were warmer than clear, non‐smoky days, and sustained a reduction in surface shortwave irradiance of 103 W m −2 during peak shortwave irradiance and an increase in longwave irradiance of 10 W m −2 , producing an average 15 W m −2 decrease in net radiation. Albedo observed on‐ice gradually decreased after the wildfires started, from a summer average of 0.29 in 2015 before the wildfires to as low as 0.16 in 2018 after extensive wildfires and remained low for two more melt seasons without substantial upwind wildfires. Reduced all‐wave irradiance partly compensated for the increase in melt due to lowered albedo in those seasons when smoke was detected above Athabasca Glacier. In melt seasons without smoke, the suppressed albedo increased melt by slightly more than 10% compared to the simulations without fire‐impacted albedo, increasing melt by 0.42 m. w.e. in 2019 and 0.37 m. w.e. in 2020.

Abstract Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly‐used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that Earth System Model (ESM) simulated runoff‐ratio and soil‐moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor‐pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.

Abstract Intensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice‐rich moraine, and organic‐rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions defined by terrain relief and accumulation of organics. In previously unglaciated mountainous watersheds, bicarbonate dominated carbon export (70% of total) due to chemical weathering of bedrock. In lowland watersheds, where soil organic carbon stores were largest, lateral transport of dissolved organic carbon (50%) and efflux of biotic CO 2 (25%) dominated. In watersheds affected by thaw‐induced mass wasting, erosion of ice‐rich tills enhanced chemical weathering and increased particulate carbon fluxes by two orders of magnitude. From an ecosystem carbon balance perspective, fluvial carbon export in watersheds not affected by thaw‐induced wasting was, on average, equivalent to 6%–16% of estimated net ecosystem exchange (NEE). In watersheds affected by thaw‐induced wasting, fluvial carbon export approached 60% of NEE. Because future intensification of thermokarst activity will amplify fluvial carbon export, determining the fate of carbon across diverse northern landscapes is a priority for constraining trajectories of permafrost region ecosystem carbon balance.

Long-term groundwater droughts are known to persist over timescales from multiple years up to decades. The mechanisms leading to drought persistence are, however, only partly understood. Applying a unique terrestrial system modeling platform in a probabilistic simulation framework over Europe, we discovered an important positive feedback mechanism from groundwater into the atmosphere that may increase drought persistence at interannual time scales over large continental regions. In the feedback loop, groundwater drought systematically increases net solar radiation via a cloud feedback, which, in turn, increases the drying of the land. In commonly applied climate and Earth system models, this feedback cannot be simulated due to a lack of groundwater memory effects in the representation of terrestrial hydrology. Thus, drought persistence and compound events may be underestimated in current climate projections.

Abstract Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events.

Abstract Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Abstract Risk management has reduced vulnerability to floods and droughts globally 1,2 , yet their impacts are still increasing 3 . An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data 4,5 . On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change 3 .

Sustained eutrophication of the aquatic environment by the remobilization of legacy phosphorus (P) stored in soils and sediments is a prevailing issue worldwide. Fluxes of P from the sediments to the water column, referred to as internal P loading, often delays the recovery of water quality following a reduction in external P loads. Here, we report on the vertical distribution and geochemistry of P, lanthanum (La), iron (Fe) and carbon (C) in the culturally eutrophied Lake Bromont. This lake underwent remediation treatment using La modified bentonite (LMB) commercially available as Phoslock™. We investigated the effectiveness of LMB in decreasing soluble reactive phosphorus (SRP) availability in sediments and in reducing dissolved fluxes of P across the sediment-water interface. Sediment cores were retrieved before and after LMB treatment at three sites representing bottom sediment, sediment influenced by lakeside housing and finally littoral sediment influenced by the lake inflow. Sequential extractions were used to assess changes in P speciation. Depth profiles of dissolved porewater concentrations were obtained after LMB treatment at each site. Results indicate that SRP extracted from the sediments decreased at all sites, while total extracted P (PTOT) bound to redox-sensitive metal oxides increased. 31P NMR data on P extract reveals that 20-43% of total solid-phase P is in the form of organic P (Porg) susceptible to be released via microbial degradation. Geochemical modelling of porewater data provides evidence that LaPO4(s) mineral phases, such as rhabdophane and/or monazite, are likely forming. However, results also suggest that La3+ binding by dissolved organic carbon (DOC) hinders La-phosphate precipitation. We rely on thermodynamic modelling to suggest that high Fe2+ would bind to DOC instead of La3+, therefore promoting P sequestrations by LMB under anoxic conditions.

Evaluations of analytical performance through interlaboratory comparisons and proficiency tests are underway globally for biomolecular-based methods [e.g., reverse-transcription quantitative polymerase chain reaction (RT-qPCR)] used in the surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater. These evaluations often rely on sharing a common reference wastewater sample that is split among participating laboratories. A known quantity of recovery surrogates can be introduced to the wastewater matrix by the coordinating laboratory as an exogenous control in a spike-and-recovery approach; however, split-sample comparisons are increasingly performed to evaluate in situ quantities of SARS-CoV-2 genetic signal native to the sample due to the lack of a universally accepted recovery surrogate of SARS-CoV-2. A reproducible procedure that minimizes the variability of SARS-CoV-2 genetic signal among split wastewater aliquots is therefore necessary to facilitate the method comparisons, especially when a large number of aliquots are required. Emerging literature has suggested that SARS-CoV-2 genetic signal in wastewater is linked to the solids fraction. Accordingly, a protocol that allows for equal distribution of solids content evenly among wastewater aliquots was also likely to facilitate even distribution of the SARS-CoV-2 genetic signal. Based on this premise, we reviewed existing sample splitting apparatus and approaches used for solids-based parameters in environmental samples. A portable batch reactor was designed, comprised of readily accessible materials and equipment. This design was validated through splitting of real wastewater samples collected from a municipal wastewater treatment facility serving a population with reported cases of COVID-19. This work applies well-established solid-liquid mixing theory and concepts that are likely unfamiliar to molecular microbiologists and laboratory analysts, providing (1) a prototype adaptable for a range of sample quantities, aliquot sizes, microbial targets, and water matrices; and (2) a pragmatic demonstration of critical considerations for design and validation of a reproducible and effective sample splitting protocol.

Sustainable groundwater management is founded on the sound understanding of the effects of water extraction on the aquifer water level and the springs and streams receiving groundwater discharge. Pumping test data are commonly used in extraction licence applications to evaluate aquifer properties and assess the magnitude of storage depletion resulting from pumping. However, a short duration (eg 48 hours) pumping test can fail to detect the presence of aquifer boundaries, as the cone of depression is not large enough to reach the boundaries. This may cause an underestimation of long-term drawdown and an overestimation of permissible extraction rate (ie safe yield). In the rural town of Irricana in Alberta, groundwater extraction licences for municipal water supply wells were issued in the early 1980s based on the analysis of 48-hour pumping tests. Actual water extraction rates were substantially below the licensed rates, but the unanticipated and excessive drawdown in the aquifer forced the town to discontinue pumping and switch to surface water supply after 25 years. To examine the cause of overallocation, a new 48-hour pumping test was conducted in the same aquifer, which included an extended drawdown analysis using 26 days of recovery data. Geological formation logs for existing wells in the area surrounding Irricana were used to infer the extent of sandstone aquifer units within the heterogeneous bedrock formation. The new data analysis showed that the aquifer is semi-closed, contrary to the infinite-aquifer assumption used in the original pumping test, which caused additional drawdown due to the aquifer boundary effects. This study suggests an improved procedure for estimation of storage depletion using standard hydrogeological methods and readily available data. The new procedure provides a useful tool as part of adaptive groundwater management, in which water levels and other relevant variables are monitored and licensed extraction rates are adjusted accordingly.

Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R2 = 0.70) and a relatively close agreement with pin profiler (R2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.

Abstract Historic land alterations and agricultural intensification have resulted in legacy phosphorus (P) accumulations within lakes and reservoirs. Internal loading from such legacy stores can be a major driver of future water quality degradation. Yet, little is known about the magnitude and spatial patterns of legacy P accumulation in lentic systems, and how watershed disturbance trajectories drive these patterns. Here, we used a meta-analysis of 113 paleolimnological studies across 124 lakes and four reservoirs (referred here on as lakes) in 20 countries to quantify the linkages between the 100 year trajectories of P concentrations in lake sediments, watershed inputs, and lake morphology. We find five distinct clusters for lake sediment P trajectories, with lakes in the developing and developed world showing distinctly different patterns. Lakes in the developed world (Europe and North America) with early agricultural intensification had the highest sediment P concentrations (1176–1628 mg kg −1 ), with a peak between the 1970–1980s and a decline since then, while lakes in the developing world, specifically China, documented monotonically increasing sediment P concentrations (857–1603 mg kg −1 ). Sediment P trajectories reflected watershed disturbance patterns and were driven by a combination of anthropogenic drivers (fertilizer input and population density) and lake morphology (watershed to lake area ratio). Specifically, we found the largest legacy accumulation rates to occur in shallow lakes experiencing long-term land-use disturbances. These links between land-use change and P accumulation in lentic systems can provide insights about inland water quality response and help to develop robust predictive models useful for resource managers and decision-makers.

Abstract Oceans are well-known to be directly altered by global climate forcings such as greenhouse gas changes, but how oceans are indirectly influenced by land and its response to such forcings remains less explored. Here, we assess the present-day and projected future state of a little-explored feature of the climate system—a ‘land wake’ in relative humidity downwind of the east coast of North America, consisting of low-humidity continental air extending roughly 1000 km over the Atlantic ocean. The wake exists throughout the year, but is supported by high continental temperatures in summer and low continental moisture in winter. The wake is well represented in an ensemble of global climate models (GCMs), qualitatively matching reanalysis data. Under increasing atmospheric CO 2 , the land wake intensifies in GCM simulations through two pathways: the radiative effects of CO 2 on surface temperatures, and the biogeochemical effect of CO 2 on terrestrial vegetation. Vegetation responses to increased CO 2 alter the summer wake from Florida to Newfoundland, and both the radiative and biogeochemical effects of CO 2 drive reductions in coastal cloud cover. These changes illustrate the potential of rapidly changing terrestrial climate to influence coastal regions and the ocean environment downwind of continents through both light conditions and the energy balance of the surface ocean.

Abstract Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.

In this paper, non-contact microplastic concentration monitoring is enabled using a sensitivity-enhanced planar microwave sensor. The sensing platform is a tag-reader structure that consists of a dual-resonator tag and a passive microwave reader. The dual-tag structure is combined with a silicon reservoir as a sample container and is energized through the electromagnetic (EM) coupling with the reader. The extremely sensitive area of the tag resonators is exposed to the sample under the test; it creates an exceptional microplastic deposition monitoring with the concentration of the microplastic in the liquid under the test as low as 400K particles/L. The sensor monitors the deposition of microplastic particles in the mixed liquid in a real-time manner and reflects it as the frequency resonance variation in the transmission response.

Abstract Modelling is widely used in ecology and its utility continues to increase as scientists, managers and policy‐makers face pressure to effectively manage ecosystems and meet conservation goals with limited resources. As the urgency to forecast ecosystem responses to global change grows, so do the number and complexity of predictive ecological models and the value of iterative prediction, both of which demand validation and cross‐model comparisons. This challenges ecologists to provide predictive models that are reusable, interoperable, transparent and able to accommodate updates to both data and algorithms. We propose a practical solution to this challenge based on the PERFICT principles (frequent Predictions and Evaluations of Reusable, Freely accessible, Interoperable models, built within Continuous workflows that are routinely Tested), using a modular and integrated framework. We present its general implementation across seven common components of ecological model applications—(i) the modelling toolkit; (ii) data acquisition and treatment; (iii) model parameterisation and calibration; (iv) obtaining predictions; (v) model validation; (vi) analysing and presenting model outputs; and (vii) testing model code—and apply it to two approaches used to predict species distributions: (1) a static statistical model, and (2) a complex spatiotemporally dynamic model. Adopting a continuous workflow enabled us to reuse our models in new study areas, update predictions with new data, and re‐parameterise with different interoperable modules using freely accessible data sources, all with minimal user input. This allowed repeating predictions and automatically evaluating their quality, while centralised inputs, parameters and outputs, facilitated ensemble forecasting and tracking uncertainty. Importantly, the integrated model validation promotes a continuous evaluation of the quality of more‐ or less‐parsimonious models, which is valuable in predictive ecological modelling. By linking all stages of an ecological modelling exercise, it is possible to overcome common challenges faced by ecological modellers, such as changing study areas, choosing between different modelling approaches, and evaluating the appropriateness of the model. This ultimately creates a more equitable and robust playing field for both modellers and end users (e.g. managers), and contributes to position predictive ecology as a central contributor to global change forecasting.

Abstract As groundwater levels steadily decline in India, authorities are concerned about reducing extraction for irrigation purposes without jeopardizing food security. Very low or zero prices for electricity and water in agriculture is partly responsible for overextraction, but charging higher prices is politically not feasible. In this study, we describe the results of a pilot scheme implemented in Punjab, India, where farmers who enrolled were allocated a monthly entitlement of electricity units and compensated for unused electricity. Eight hours of uninterrupted daytime electricity supply were also provided under the scheme instead of the usual mix of daytime and night‐time supply. Analyzing data from a cross‐sectional farm household survey and instrumenting for enrollment, we find that self‐reported hours of irrigation for enrolled farmers were significantly lower than for non‐enrolled ones, with no impact on rice yields. We also find a reduction in monthly electricity consumption at electricity‐feeder level due to the pilot scheme using the synthetic control method. Our results suggest that the combination of daytime electricity provision and cash incentives for unused electricity has the potential to incentivize farmers to reduce electricity consumption and irrigation hours by at least 7.5% and up to 30% without impacting paddy yields.

Groundwater Monitoring & RemediationVolume 42, Issue 3 p. 131-132 In My Experience In My Experience: The Nature of Groundwater Discharge David Rudolph, Corresponding Author David Rudolph [email protected] Search for more papers by this author David Rudolph, Corresponding Author David Rudolph [email protected] Search for more papers by this author First published: 05 July 2022 https://doi.org/10.1111/gwmr.12540Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume42, Issue3Special Issue with Focus on Data ManagementSummer 2022Pages 131-132 RelatedInformation

Abstract The Yukon River Basin (YRB) is one of the most important river networks shared between Canada and The United States, and is one of the largest river basins in the subarctic region of North America. The Canadian part of the YRB is characterized by steeply sloped, partly glaciated mountain headwaters that generate considerable runoff during melt of glaciers and seasonal snowcover. Snow redistribution, snowmelt, glacier melt and freezing–thawing soil processes in winter and spring along with summertime rainfall‐runoff and evapotranspiration processes are thus key components of streamflow generation in the basin, making conceptual rainfall‐runoff models unsuitable for this cold region. Due to the remote high latitudes and high altitudes of the basin, there is a paucity of observational data, making heavily calibrated conceptual modeling approaches infeasible. At the request of the Yukon Government, this project developed and operationalized a streamflow forecasting system for the Yukon River and several of its tributary rivers using a distributed land surface modeling approach developed for large‐scale implementation in cold regions. This represents a substantial advance in bringing operational hydrological forecasting to the Canadian subarctic for the first time. This experience will inform future research to operation improvements as Canada develops a nationally coordinated flood forecast system.

Abstract The Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.

We report metagenomic sequencing analyses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in composite wastewater influent from 10 regions in Ontario, Canada, during the transition between Delta and Omicron variants of concern. The Delta and Omicron BA.1/BA.1.1 and BA.2-defining mutations occurring in various frequencies were reported in the consensus and subconsensus sequences of the composite samples.

Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by interviewing and surveying practitioners about their expectations of research in code comment generation. We then compared what practitioners need and the current state-of-the-art research by performing a literature review of papers on code comment generation techniques published in the premier publication venues from 2010 to 2020. From this comparison, we highlighted the directions where researchers need to put effort to develop comment generation techniques that matter to practitioners.

We present a hybrid text and geospatial search application for hydrographic datasets built on the open-source Lucene search library. Our goal is to demonstrate that it is possible to build custom GIS applications by integrating existing open-source components and data sources, which contrasts with existing approaches based on monolithic platforms such as ArcGIS and QGIS. Lucene provides rich index structures and search capabilities for free text and geometries; the former has already been integrated and exposed via our group's Anserini and Pyserini IR toolkits. In this work, we extend these toolkits to include geospatial capabilities. Combining knowledge extracted from Wikidata with the HydroSHEDS dataset, our application enables text and geospatial search of rivers worldwide.

Nigella sativa (NS) is a plant that has long been utilized in traditional medicine as a treatment for certain diseases. The aim of this work was to valorize the essential oil (EO) of this species by phytochemical analysis and antimicrobial and antioxidant evaluation. EO was extracted by hydrodistillation from the seeds of Nigella sativa (EO-NS). Phytochemical content of EO-NS was evaluated by use of gas chromatography coupled to mass spectrometry (GC-MS/MS). Antioxidant ability was in vitro determined by use of three assays: 2.2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing power (FRAP), and total antioxidant capacity (TAC) relative to two synthetic antioxidants: BHT and quercetin. Antimicrobial effect was evaluated against four clinically important bacterial strains (Staphylococcus aureus, ATCC 6633; Escherichia coli, K12; Bacillus subtilis, DSM 6333; and Proteus mirabilis, ATCC 29906) and against four fungal strains (Candida albicans, ATCC 10231; Aspergillus niger, MTCC 282; Aspergillus flavus, MTCC 9606; and Fusarium oxysporum, MTCC 9913). Fifteen constituents that accounted for the majority of the mass of the EO-NS were identified and quantified by use of GC-MSMS. The main component was O-cymene (37.82%), followed by carvacrol (17.68%), α-pinene (10.09%), trans-sabinene hydrate (9.90%), and 4-terpineol (7.15%). EO-NS exhibited significant antioxidant activity with IC50, EC50, and total antioxidant capacity (TAC) of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>0.017</mn> <mo>±</mo> <mn>0.0002</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0.1196</mn> <mo>±</mo> <mn>0.012</mn> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>114.059</mn> <mo>±</mo> <mn>0.97</mn> </math> mg EAA/g, respectively. Additionally, EO-NS exhibited promising antibacterial activity on all strains under investigation, especially on E. coli K12 resulting in inhibition diameter of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>38.67</mn> <mo>±</mo> <mn>0.58</mn> </math> mm and a minimum inhibitory concentration (MIC) of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mn>1.34</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL. Also, EO-NS had significant antifungal efficacy, with a percentage of inhibition of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mn>67.45</mn> <mo>±</mo> <mn>2.31</mn> </math> % and MIC of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mn>2.69</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL against F. oxysporum, MTCC 9913 and with a diameter of inhibition <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mn>42</mn> <mo>±</mo> <mn>0.00</mn> </math> mm and MIC of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mn>0.67</mn> <mo>±</mo> <mn>0.00</mn> </math> μg/mL against C. albicans. To minimize development of antibiotic-resistant bacteria, EO-NS can be utilized as a natural, alternative to synthetic antibiotics and antioxidants to treat free radicals implicated in microbial infection-related inflammatory reactions.

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Abstract Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16th degree daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross-validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.

Abstract In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs). Significance Statement The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.

Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019.Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m-2 yr-1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks.Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.

Abstract Background Variable Retention Harvesting (VRH) is a forest management practice applied to enhance forest growth, improve biodiversity, preserve ecosystem function and provide economic revenue from harvested timber. There are many different forms and compositions in which VRH is applied in forest ecosystems. In this study, the impacts of four different VRH treatments on transpiration were evaluated in an 83-year-old red pine (Pinus Pinus resinosa ) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. These VRH treatments were implemented in 1-ha plots in the winter of 2014, while sap flow measurements were conducted from 2018 to 2020. Results Study results showed that tree-level transpiration was highest among trees in the 55D treatment, followed by 33D, 55A, 33A and CN plots. We found that photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) were major controls or drivers of transpiration in all VRH treatments. Our study suggests that dispersed or distributed retention of 55% basal area (55D) is the ideal forest management technique to enhance transpiration and forest growth. Conclusions This study will help researchers, forest managers and decision-makers to improve their understanding of water cycling in forest ecosystem and adopt the best forest management regimes to enhance forest growth, health and resiliency to climate change.

The main objective of this study is to assess the economic value of the Brazilian Amazon’s ecosystem services accruing to Brazilians based on a meta-analysis of the Brazilian valuation literature. Insight in these local values provides an important benchmark to demonstrate the importance of preserving the Brazilian Amazon forest. The review covers almost 30 years of Brazilian valuation research on the Amazon, published predominantly in Portuguese, highlighting a high degree of study and data heterogeneity. The estimated mean value of the provision of habitat for species, carbon sequestration, water regulation, recreation and ecotourism to local populations is about 410 USD/ha/year. The standard deviation is however high, reflecting a wide dispersion in the distribution of values. Between 50 and 70 percent of the variation in these values can be explained with the help of the estimated meta-regression models, resulting in considerable prediction errors when applying a within-sample resampling procedure. These findings demonstrate the need for a more robust, common ecosystem services accounting and valuation framework before these values can be scaled up and aggregated across the entire Brazilian Amazon.

Abstract Thermal pollution is an environmental impact of large dams altering the natural temperature regime of downstream river ecosystems. The present study proposes a simulation–optimization framework to reduce thermal pollution downstream from reservoirs and tests it on a real-world case study. This framework attempts to simultaneously minimize the environmental impacts as well as losses to reservoir objectives for water supply. A hybrid machine-learning model is applied to simulate water temperature downstream of the reservoir under various operation scenarios. This model is shown to be robust and achieves acceptable predictive accuracy. The results of simulation–optimization indicate that the reservoir could be operated in such a way that the natural temperature regime is reasonably preserved to protect downstream habitats. Doing so, however, would result in significant trade-offs for reservoir storage and water supply objectives. Such trade-offs can undermine the benefits of reservoirs and need to be carefully considered in reservoir design and operation.

In boreal North America, much of the landscape is covered by fire-adapted forests dominated by serotinous conifers. For these forests, reductions in fire return interval could limit reproductive success, owing to insufficient time for stands to reach reproductive maturity i.e., to initiate cone production. Improved understanding of the drivers of reproductive maturity can provide important information about the capacity of these forests to self-replace following fire. Here, we assessed the drivers of reproductive maturity in two dominant and widespread conifers, semi-serotinous black spruce and serotinous jack pine. Presence or absence of female cones were recorded in approximately 15,000 individuals within old and recently burned stands in two distinct ecozones of the Northwest Territories (NWT), Canada. Our results show that reproductive maturity was triggered by a minimum tree size threshold rather than an age threshold, with trees reaching reproductive maturity at smaller sizes where environmental conditions were more stressful. The number of reproductive trees per plot increased with stem density, basal area, and at higher latitudes (colder locations). The harsh climatic conditions present at these higher latitudes, however, limited the recruitment of jack pine at the treeline ecotone. The number of reproductive black spruce trees increased with deeper soils, whereas the number of reproductive jack pine trees increased where soils were shallower. We examined the reproductive efficiency i.e., the number of seedlings recruited per reproductive tree, linking pre-fire reproductive maturity of recently burned stands and post-fire seedling recruitment (recorded up to 4 years after the fires) and found that a reproductive jack pine can recruit on average three times more seedlings than a reproductive black spruce. We suggest that the higher reproductive efficiency of jack pine can explain the greater resilience of this species to wildfire compared with black spruce. Overall, these results help link life history characteristics, such as reproductive maturity, to variation in post-fire recruitment of dominant serotinous conifers.

Northern Indigenous communities require collaborative approaches to health communication about food that are grounded in Indigenous knowledges and cultures; however, preferences and best methods for this process remain understudied. This participatory study discusses how Inuvialuit (Inuit from the Western Arctic) knowledge and the perspectives of territorial, regional, and local dietary message stakeholders can inform the co-development of culture-centered dietary messaging to support healthy, safe, and culturally appropriate diets in Tuktoyaktuk, NWT. A community researcher in Tuktoyaktuk conducted storytelling interviews with country food knowledge holders (n = 7) and community members (n = 3), and a talking circle with local public health dietary message disseminators (n = 2) in June-July 2021. The lead author conducted key informant telephone and videoconference interviews with territorial and regional dietary message disseminators (n = 5) in June 2021. Interviews were coded and analyzed thematically. Our findings indicate that participants at all levels support increased inclusion of cultural and community perspectives about food to develop regionally and locally tailored dietary messaging. While most dietary message stakeholders wish to be involved in co-development processes, some country food knowledge holders in Tuktoyaktuk expressed a desire to lead local communications about country foods. Informed by participants' experiences and needs, we provide recommendations for future community-led approaches to further (co-)develop and communicate effective, culturally meaningful dietary messaging that promotes Inuvialuit food sovereignty.

Source code repositories allow developers to manage multiple versions (or branches) of a software system. Pull-requests are used to modify a branch, and backporting is a regular activity used to port changes from a current development branch to other versions. In open-source software, backports are common and often need to be adapted by hand, which motivates us to explore backports and backporting challenges and strategies. In our exploration of 68,424 backports from 10 GitHub projects, we found that bug, test, document, and feature changes are commonly backported. We identified a number of backporting challenges, including that backports were inconsistently linked to their original pull-request (49%), that backports had incompatible code (13%), that backports failed to be accepted (10%), and that there were backporting delays (16 days to create, 5 days to merge). We identified some general strategies for addressing backporting issues. We also noted that backporting strategies depend on the project type and that further investigation is needed to determine their suitability. Furthermore, we created the first-ever backports dataset that can be used by other researchers and practitioners for investigating backports and backporting.

Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parameterizations to model dam operations, the representation of reservoir regulation is still lacking in many Earth system models. Here we implement and evaluate a widely used reservoir parametrization in the global river-routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs and is currently being coupled to an Earth system model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing them to simulations ignoring inland waters and simulations with reservoirs represented as natural lakes using (i) local simulations for 26 individual reservoirs driven by observed inflows and (ii) global-domain simulations driven by runoff from the Community Land Model. The local simulations show the clear added value of the reservoir parametrization, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-domain application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but a similar performance is found compared to the natural lake parametrization. The limited impact of reservoirs on skill statistics could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that including dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test runoff simulations and water management parameterizations in order to improve the representation of anthropogenic interference of the terrestrial water cycle in Earth system models.

Abstract. The Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. This toolbox can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. While improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition from which all models inherit. The new modelling framework maintains and improves on several good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats that simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub (https://github.com/wknoben/MARRMoT, last access: 12 May 2022; https://doi.org/10.5281/zenodo.6484372, Trotter and Knoben, 2022b).

Abstract. Mountain snow and ice greatly influence the hydrological cycle of alpine regions by regulating both the quantity of and seasonal variations in water availability downstream. This study considers the combined impacts of climate and glacier changes due to recession on the hydrology and water balance of two high-elevation basins in the Canadian Rockies. A distributed, physically based, uncalibrated glacier hydrology model developed in the Cold Regions Hydrological Modelling platform (CRHM) was used to simulate the glacier mass balance and basin hydrology of the Peyto and Athabasca glacier basins in Alberta, Canada. Bias-corrected reanalysis data were used to drive the model. The model calculates the water balance of glacierized basins, influenced by the surface energy and mass balance, and considers the redistribution of snow by wind and avalanches. It was set up using hydrological response units based on elevation bands, surface slope, and aspect, as well as changing land cover. Aerial photos, satellite images and digital elevation models (DEMs) were assimilated to represent the changing configurations of glacier area and the exposure of ice and firn. Observations of glacier mass balance, snow, and glacier ice surface elevation changes at glacier and alpine tundra meteorological stations and streamflow discharge at the glacier outlets were used to evaluate the model performance. Basin hydrology was simulated over two periods, 1965–1975 and 2008–2018, using the observed glacier configurations for those time periods. Both basins have undergone continuous glacier loss over the last 3 to 5 decades, leading to a 6 %–31 % reduction in glacierized area, a 78 %–109 % increase in ice exposure, and changes to the elevation and slope of the glacier surfaces. Air temperatures are increasing, mainly due to increasing winter maximum and summer minimum daily temperatures. Annual precipitation has increased by less than 11 %, but rainfall ratios have increased by 29 %–44 %. The results show that changes in both climate and glacier configuration have influenced the melt rates and runoff and a shift of peak flows in the Peyto Glacier basin from August to July. Glacier melt contributions increased/decreased from 27 %–61 % to 43 %–59 % of the annual discharges. Recent discharges were 3 %–19 % higher than in the 1960s and 1970s. The results suggest that increased exposure of glacier ice and lower surface elevation due to glacier thinning were less influential than climate warming in increasing streamflow. Streamflow from these glaciers continues to increase.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

Abstract. Wetland drainage has been pervasive in the North American Prairie Pothole Region. There is strong evidence that this drainage increases the hydrological connectivity of previously isolated wetlands and, in turn, runoff response to snowmelt and rainfall. It can be hard to disentangle the role of climate from the influence of wetland drainage in observed records. In this study, a basin-classification-based virtual modelling approach is described that can isolate these effects on runoff regimes. The basin class which was examined, entitled Pothole Till, extends throughout much of Canada's portion of the Prairie Pothole Region. Three knowledge gaps were addressed. First, it was determined that the spatial pattern in which wetlands are drained has little influence on how much the runoff regime was altered. Second, no threshold could be identified below which wetland drainage has no effect on the runoff regime, with drainage thresholds as low as 10 % in the area being evaluated. Third, wetter regions were less sensitive to drainage as they tend to be better hydrologically connected, even in the absence of drainage. Low flows were the least affected by drainage. Conversely, during extremely wet years, runoff depths could double as the result of complete wetland removal. Simulated median annual runoff depths were the most responsive, potentially tripling under typical conditions with high degrees of wetland drainage. As storage capacity is removed from the landscape through wetland drainage, the size of the storage deficit of median years begins to decrease and to converge on those of the extreme wet years. Model simulations of flood frequency suggest that, because of these changes in antecedent conditions, precipitation that once could generate a median event with wetland drainage can generate what would have been a maximum event without wetland drainage. The advantage of the basin-classification-based virtual modelling approach employed here is that it simulated a long period that included a wide variety of precipitation and antecedent storage conditions across a diversity of wetland complexes. This has allowed seemingly disparate results of past research to be put into context and finds that conflicting results are often only because of differences in spatial scale and temporal scope of investigation. A conceptual framework is provided that shows, in general, how annual runoff in different climatic and drainage situations will likely respond to wetland drainage in the Prairie Pothole Region.

Abstract. The Red River is one of the largest contributing sources of discharge and nutrients to the world's 10th largest freshwater lake, Lake Winnipeg. Conversion of large areas of annual cropland to perennial forage has been proposed as a strategy to reduce both flooding and nutrient export to Lake Winnipeg. Such reductions could occur either via a reduction in the concentration of nutrients in runoff or through changes in the basin-scale hydrology, resulting in a lower water yield and the concomitant export of nutrients. This study assessed the latter mechanism by using the physically based Cold Regions Hydrological Modelling platform to examine the hydrological impacts of land use conversion from annual crops to perennial forage in a subbasin of the La Salle River basin in Canada. This basin is a typical agricultural subbasin in the Red River Valley, characterised by flat topography, clay soils, and a cold subhumid, continental climate. Long-term simulations (1992–2013) of the major components of water balance were compared between canola and smooth bromegrass, representing a conversion from annual cropping systems to perennial forage. An uncertainty framework was used to represent a range of fall soil saturation status (0 % to 70 %), which governs the infiltration to frozen soil in the subsequent spring. The model simulations indicated that, on average, there was a 36.5 ± 6.6 % (36.5 ± 7.2 mm) reduction in annual cumulative discharge and a 29.9 ± 16.3 % (2.6 ± 1.6 m3 s−1) reduction in annual peak discharge due to forage conversion over the assessed period. These reductions were driven by reduced overland flow 52.9 ± 12.8 % (28.8 ± 10.1 mm), increased peak snowpack (8.1 ± 1.5 %, 7.8 ± 1.6 mm), and enhanced infiltration to frozen soils (66.7 ± 7.7 %, 141.5 ± 15.2 mm). Higher cumulative evapotranspiration (ET) from perennial forage (34.5 ± 0.9 %, 94.1 ± 2.5 mm) was also predicted by the simulations. Overall, daily soil moisture under perennial forage was 18.0 % (57.2 ± 1.2 mm) higher than that of crop simulation, likely due to the higher snow water equivalent (SWE) and enhanced infiltration. However, the impact of forage conversion on daily soil moisture varied interannually. Soil moisture under perennial forage stands could be either higher or lower than that of annual crops, depending on antecedent spring snowmelt infiltration volumes.

Abstract. Snow represents the largest potential source of water for thermokarst lakes, but the runoff generated by snowmelt (freshet) can flow beneath lake ice and via the outlet without mixing with and replacing pre-snowmelt lake water. Although this phenomenon, called “snowmelt bypass”, is common in ice-covered lakes, it is unknown which lake and watershed properties cause variation in snowmelt bypass among lakes. Understanding the variability of snowmelt bypass is important because the amount of freshet that is mixed into a lake affects the hydrological and biogeochemical properties of the lake. To explore lake and watershed attributes that influence snowmelt bypass, we sampled 17 open-drainage thermokarst lakes for isotope analysis before and after snowmelt. Isotope data were used to estimate the amount of lake water replaced by freshet and to observe how the water sources of lakes changed in response to the freshet. Among the lakes, a median of 25.2 % of lake water was replaced by freshet, with values ranging widely from 5.2 % to 52.8 %. For every metre that lake depth increased, the portion of lake water replaced by freshet decreased by an average of 13 %, regardless of the size of the lake's watershed. The thickness of the freshet layer was not proportional to maximum lake depth, so that a relatively larger portion of pre-snowmelt lake water remained isolated in deeper lakes. We expect that a similar relationship between increasing lake depth and greater snowmelt bypass could be present at all ice-covered open-drainage lakes that are partially mixed during the freshet. The water source of freshet that was mixed into lakes was not exclusively snowmelt but a combination of snowmelt mixed with rain-sourced water that was released as the soil thawed after snowmelt. As climate warming increases rainfall and shrubification causes earlier snowmelt timing relative to lake ice melt, snowmelt bypass may become more prevalent, with the water remaining in thermokarst lakes post-freshet becoming increasingly rainfall sourced. However, if climate change causes lake levels to fall below the outlet level (i.e., lakes become closed-drainage), more freshet may be retained by thermokarst lakes as snowmelt bypass will not be able to occur until lakes reach their outlet level.

Abstract. Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth (R2=0.77,RMSE=0.066) and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets.

Objective: To explore literature regarding youth with Adverse childhood experiences (ACEs), their potential reactivity to research, and research trauma mitigation protocols. Methods: A systematic scoping review was conducted in APA PsychInfo, CINAHL, Embase, and OVID Medline. 2 reviewers screened each article for 12 eligible studies. Quantitative and qualitative studies measuring maltreatment and trauma research responses were eligible. Youth were defined as individuals aged 10-19. Results: No study utilized the ACEs questionnaire with research-related stress measures. Among those that included research reactivity measures, various forms of childhood and youth victimization were considered. The majority of participants did not report feeling upset, with many reporting benefits to participation. Information on protocols for managing distress was available for 11 studies, the most common being the provision of a resource helpsheet and/or referral system. Implications: There is no indication of distress following ACEs-related research, with few studies measuring across the research experience. One study measured follow-up for distress and further action. Additional research may be indicated to assess the effectiveness of these protocols in this population with a follow-up assessment.

Agricultural trade poses dilemmas for adaptive water governance as farmers and irrigation systems become integrated into global food value chains and are affected by their ongoing dynamics. The benefits and risks of agricultural trade and agrarian transitions are unevenly distributed, giving rise to complex interdependencies and externalities. Despite these growing linkages, the understanding of agricultural markets and their influence on water conflict and cooperation remains limited and dependent on context, which can lead to seemingly contradictory evidence. Progress has been hampered by boundary problems, disputed concepts, measurement issues, and divergent normative perspectives. Addressing these challenges will require that water governance scholars account more explicitly for agricultural trade when diagnosing collective action problems and assessing different modes of adaptive water governance. Drawing on the common-pool resource governance literature, we distinguish three separate, but interrelated, conceptual perspectives examining agricultural trade as an external factor in water governance: (1) market integration as a disturbance, (2) market integration as an opportunity, and (3) agricultural trade as a form of telecoupling with nested externalities. We compare these perspectives in terms of the externalities involved, their major claims about the relationship between market integration and collective action in the context of irrigation governance, and the broader implications for adaptive water governance. The comparison demonstrates the prevalence of institutional misfits and the common struggle of boundary shifting, i.e., matching water governance to the expanding problem-shed associated with agricultural markets. Institutional fit offers one important lens through which to consider the shifting boundaries (and actors) relevant for water governance, the scope and limits for strengthening fit through social learning, and the importance of nested governance to address nested externalities. These insights point the way for an agenda of research that examines the evolution of agricultural trade and adaptive water governance and pays explicit attention to the politics and power relations that shape who wins and loses and the different levers and entry points to improve management of the associated transitions and trade-offs. We conclude by arguing that future research should identify and examine pathways of adaptive water governance that strengthen processes of social learning and institutional nesting to address the external pressures and opportunities created by global food value chains.

Water use efficiency (WUE) can be calculated using a range of methods differing in carbon uptake and water use variable selection. Consequently, inconsistencies arise between WUE calculations due to complex physical and physiological interactions. The purpose of this study was to quantify and compare WUE estimates (harvest or flux-based) for alfalfa (C3 plant) and maize (C4 plant) and determine effects of input variables, plant physiology and farming practices on estimates. Four WUE calculations were investigated: two “harvest-based” methods, using above ground carbon content and either precipitation or evapotranspiration (ET), and two “flux-based” methods, using gross primary productivity (GPP) and either ET or transpiration. WUE estimates differed based on method used at both half-hourly and seasonal scales. Input variables used in calculations affected WUE estimates, and plant physiology led to different responses in carbon assimilation and water use variables. WUE estimates were also impacted by different plant physiological responses and processing methods, even when the same carbon assimilation and water use variables were considered. This study highlights a need to develop a metric of measuring cropland carbon-water coupling that accounts for all water use components, plant carbon responses, and biomass production.

Agricultural tile drainage is expanding in the northern Great Plains of North America. Given ongoing environmental and political concerns related to the eutrophication of Lake Winnipeg in Canada and the potential for tile drains to transport significant quantities of nutrients from agricultural fields, an improved understanding of nutrient dynamics in tile drains in this region is needed. This study characterized seasonal patterns in tile flow and chemistry under variable hydroclimatic conditions and related this variance to temporal variability in soil hydraulic properties in a farm in southern Manitoba, Canada, from 2015 to 2017. Tile flow, soil hydraulic properties, and groundwater table position all varied seasonally, as did the chemistry of tile drain effluent. The majority of annual tile discharge, which occurred in late spring, appears to have been contributed by shallow groundwater, primarily through soil matrix pathways. At these greater tile flow rates, concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) were low (<0.03 mg L^–1 SRP, <0.04 mg L^–1 TP), but concentrations of nitrate (NO₃-N) were high (20 to 25 mg L^–1 NO₃-N). In contrast, tile flows outside of this peak period appeared to be primarily attributed to preferential flow pathways through frozen (snowmelt) and dry soil cracks (summer). Phosphorus (P) concentrations were greater during snowmelt and summer (~0.05 mg L^–1 SRP, ~0.1 mg L^–1 TP) but did not produce significant nutrient loads due to the minimal tile discharge rates (<1 mm d^–1). This work suggests that the expansion of tile drainage may not exacerbate water quality issues involving P in the northern Great Plains but may increase nitrogen (N) loads in local water bodies.

Phosphorus (P) runoff from agricultural land plays a critical role in downstream water quality. This article summarizes P and sediment runoff data for both snowmelt and rainfall runoff from 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario. The data were collected from 216 site-years of field experiments, with climates ranging from semi-arid to humid and a wide range of field management practices. In the article, mean annual and seasonal (in terms of snowmelt and rain) precipitation inputs, runoff depths, and P and sediment concentrations and loads are presented, along with ranges of yearly values. In addition, information of field management and soil characteristics (e.g. soil type and soil Olsen P) is also presented for each field. The data have potential to be reused for national and international cross-region comparisons of P and sediment losses, constructing and validating decision-support models and tools for assessing and managing P losses in both snowmelt and rainfall runoff, and informing beneficial management practices to improve agricultural water quality. Interpretation of the data is found in “Phosphorus runoff from Canadian agricultural land: A cross-region synthesis of edge-of-field results” [1] .

Algal blooms fueled by phosphorus (P) enrichment are threatening surface water quality around the world. Although P loss from arable land is a critical contributor to P loads in many agricultural watersheds , there has been a lack of understanding of P loss patterns and drivers across regions. Here, we synthesized edge-of-field P and sediment runoff data for 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario (a total of 216 site-years) to elucidate spatial and temporal differences in runoff and P mobilization in snowmelt and rainfall runoff, and discuss climatic, soil and management drivers for these patterns. Across all regions, precipitation inputs were positively correlated with runoff amounts and consequently P loads. Runoff and P losses were dominated by snowmelt across all sites, however, regional differences in runoff amounts, and P concentrations, loads and speciation were apparent. Proportions of total P in the dissolved form were greater in the prairie region (55–94% in Manitoba) than in the Great Lakes region (26–35% in Ontario). In Manitoba, dissolved P concentrations in both snowmelt and rainfall runoff were strongly positively correlated to soil Olsen P concentrations in the 0–5 cm soil depth; however, this relationship was not found for Ontario fields, where tile drainage dominated hydrologic losses. Although precipitation amounts and runoff volumes were greater in Ontario than Manitoba, some of the greatest P loads were observed from Manitoba fields, driven by management practices. This synthesis highlights the differences across the Canadian agricultural regions in P runoff patterns and drivers, and suggests the need of co-ordinated and standardized monitoring programs to better understand regional differences and inform management. Phosphorus runoff patterns vary with climatic regions across Canada. †The dissolved P was measured as total dissolved P in MB and dissolved reactive P in SK and ON. ‡Total P was not measured in SK. • Phosphorus runoff patterns and drivers vary with climatic regions across Canada. • Co-ordinated and standardized monitoring programs are key to clarify regional differences. • Snowmelt dominates runoff volume and phosphorus loss across Canada. • The predominant form of P in runoff differs between the Prairie region and the Great Lakes region. • Reducing phosphorus sources is important for mitigating phosphorus runoff.

Agricultural phosphorus (P) losses to surface water bodies remain a global eutrophication concern, despite the application of conservation practices on farm fields. Although it is generally agreed upon that the use of multiple conservation practices (“stacking”) will lead to greater improvements to water quality, this may not be cost effective to farmers, reducing the likelihood of adoption. At present, wholesale recommendations of conservation practices are given; however, the application of specific conservation practices in certain environments (e.g., no-till with surface application, cover crops) may not be effective and can even lead to unintended consequences. In this paper, we present the Lake Erie watershed as a case study. The Lake Erie watershed contains regions with unique physical geographies that include differences in climate, soil, topography, and land use, which have implications for both P transport from agricultural fields and the efficacy of conservation practices in mitigating P losses. We define major regions within the Lake Erie watershed where common strategies for conservation practice implementation are appropriate, and we propose a five-step plan for bringing regionally tailored, adaptive, and cost-conscious conservation practice into watershed planning. Although this paper is specific to the Lake Erie watershed, our framework can be transferred across broader geographic regions to provide guidance for watershed planning.

Nutrient losses from agricultural operations are a major contributor to the eutrophication of freshwaters. Although many studies have quantified diffuse nutrient losses, less is known about agricultural point-source contributions, such as bunker silos, to watershed phosphorus (P) loads. This study examined the contributions of a dairy farm bunker silo effluent to watershed soluble reactive P (SRP) and total P (TP) losses. The bunker silo effluent discharged to an adjacent stream via a riparian soakaway for ca. 15 years. Prior to the annual refilling of the bunker silo, flow weighted mean concentrations of SRP (TP) were similar between stream locations up and downstream of the farm. After the bunker silo was refilled, flow-weighted SRP (TP) concentrations in the stream increased by factors of 1.5(2.2) during events and 3.1(2.3) during baseflow. Higher P concentrations occurred in the riparian soils receiving bunker silo effluent (525–3125 mg/kg TP, and 0.1–9.9 mg/kg water extractable P (WEP), compared with 525–939 mg/kg TP, and 0.11–1.43 mg/kg WEP on the opposite side of the stream with no bunker silo effluent. Riparian soils impacted by the bunker silo were near P-saturation, and the riparian zone did little to reduce P transfer in shallow groundwater. The net contributions of bunker silo effluent to annual watershed P losses were 32% (SRP) and 22% (TP). This study highlights the importance of agricultural point sources, and the need to quantify their contributions to watershed P budgets to target P remediation effectively.

To assess the potential change in agroclimatic indices in western Canada, this study used a convection‐permitting Weather Research Forecasting (WRF) model to conduct simulations for the current climate (CTL, 2000–2015) and future climate under the RCP8.5 scenario based on a pseudo‐global‐warming (PGW) approach. Both CTL and PGW simulations were bias‐corrected to the GEM‐CaPA dataset using a multivariate quantile mapping method. An evaluation of the CTL simulation of daily maximum and minimum temperatures and precipitation during the growing season against the gridded observations has been performed, indicating good agreements in the spatial patterns of air temperature and precipitation in western Canada. The PGW − CTL differences in several selected agroclimatic indices were then examined. Due to rising temperatures, substantial increases in growing degree‐days (GDD) by 800–1,200° days and reductions in frost days by 10 to 20 days, favouring regional crop production, are found in southern Alberta and Saskatchewan. However, global warming also poses great risks to Canadian agriculture by modifying heat accumulations and water availability during the growing season. Plant heat stress will substantially increase by ∼50° days in southern Alberta and Saskatchewan, offsetting the positive effects caused by the reduction in frost days and increase in GDD. The southern Canadian Prairies will experience statistically significant increases in the number of dry days and precipitation deficit, suggesting an exacerbation of water stress on the Canadian Prairies by global warming.

• Greater restored moss cover decreased peat burn severity. • Deep vs shallow harvesting depth drove divergent post-fire soil water conditions. • Shallow harvest increased suitable conditions for Sphagnum establishment. • Deep harvest lowers the risk of subsequent peat ignition. • Deep harvest likely to promote longer-term carbon sequestration due to fewer fires. Peatland disturbances can disrupt the ecohydrological functions that sustain net carbon sequestration in peatlands. Anthropogenic disturbances, such as peatland drainage and harvesting, are often followed by peatland restoration that aims to return the carbon sink function. This is typically achieved by raising the water table and re-establishing keystone Sphagnum moss species. However, with an increasingly uncertain climate and intensifying land-use changes, the potential for multiple disturbances (such as co-occurring wildfires, drainage, and harvesting) to disrupt the ecohydrological feedbacks that support peatland function is increasing. Yet, few studies investigate the ecohydrological trade-offs induced by multiple disturbances in peatlands. To elucidate the complexities of multiple disturbances and restoration on Sphagnum re-establishment and wildfire potential, we studied a Deep and Shallow harvested area in a drained and restored peatland in southern Ontario, Canada that experienced a wildfire in 2012. Harvesting depth did not significantly increase the bulk density of the upper 32 cm of exposed peat, but the shallower harvest depth did significantly increase the depth of burn (DOB) due to the more varied remnant topography. The difference in topography of the shallower harvested area increased peat carbon losses (16.5 kg C m −2 ) from the wildfire relative to the deeper harvest area (15.1 kg C m −2 ). The difference in post-fire peat hydrophysical properties of the Deep and Shallow harvest area drove divergent soil water conditions. In the post-burn peat, the establishment of suitable conditions for the regeneration of Sphagnum mosses was more prevalent at the Shallow harvest areas but the higher soil water retention capabilities of the Deep harvest peat lowered the risk of subsequent peat ignition. This study highlights the complex interactions multiple disturbances have on peatland ecohydrology and that we urgently need to understand these interactions to better manage our shared peatland resources in an increasingly uncertain future.

• The CRHM-created Boreal Hydrology Model performed quite well on simultaneously simulating runoff, snow water equivalent, soil liquid water content and evapotranspiration (ET) with minor parameter calibration. • The basin hydrological variables showed quite different sensitivities to perturbations of precipitation (P) and temperature (T). Annual runoff was more sensitive to rising P than warming T, but annual ET was more sensitive to warming T. • Perturbed P and T had distinctively different influences on the streamflow regime. Increased P enhanced the intra- and inter-annual variabilities of basin runoff, whilst rising T resulted in the inverse changes. • Effects of warming on annual runoff and snow processes could be compensated for to varying degrees by the effects of increases in P. Hydrological processes over and through frozen and unfrozen ground were simulated in the well instrumented boreal forest basin of White Gull Creek, Saskatchewan, Canada using a model created using the flexible Cold Regions Hydrological Modelling (CRHM) platform. The CRHM-created Boreal Hydrology Model was structured and initially parameterized using decades of process hydrology research in the southern boreal forest with minor parameter calibration, and generally produced quite good performance on simultaneously reproducing the measurements of runoff, snow water equivalent (SWE), soil liquid water content and eddy correlation flux tower observations of evapotranspiration (ET) over two decades. To examine the sensitivity of basin hydrology to perturbed climate inputs, air temperature (T) inputs were set up by linear increments in the reference observation of up to +6 ℃, and precipitation (P) inputs were generated by multiplying the reference observed P from 70% to 130%. The model results showed that the basin hydrological variables showed quite different sensitivities to perturbations of P and T. The volume of annual runoff and the annual runoff coefficient increased more rapidly with rising P, at rates of 31% and 16% per 10% increase in P, but decreased by only 3.8% and 4.7% per 1 ℃ of warming. Annual ET increased rapidly with temperature, by 7% per 1 ℃ of warming and therefore drove the streamflow volumetric changes with warming, but increased only 1% per 10% increase in P. Perturbations of P and T had distinctively different influences on the streamflow regime. Increased P enhanced the intra- and inter-annual variabilities of basin runoff, reduced the relative contribution of winter runoff to annual runoff and increased the relative contribution of summer runoff; whilst rising T resulted in the inverse changes in the streamflow regime. Effects of warming on some hydrological processes could be compensated for to varying degrees by the effects of increases in P. Reductions in the annual runoff volume and runoff coefficient caused by warming up to 6 ℃ could be compensated for by increases of <20% in P. However, the maximum increase in P (+30%) examined could only compensate for the changes in snow processes caused by warming of less than 4 ℃ and snow-cover duration decreases with 1 ℃ warming could not be compensated for by any precipitation increase considered. These results inform the vulnerability of boreal forest hydrology to the first-order changes in P and T and provide guidance for further climate impact assessments for hydrology in the southern boreal forest in Canada.

2020 is the year of wildfire records. California experienced its three largest fires early in its fire season. The Pantanal, the largest wetland on the planet, burned over 20% of its surface. More than 18 million hectares of forest and bushland burned during the 2019–2020 fire season in Australia, killing 33 people, destroying nearly 2500 homes, and endangering many endemic species. The direct cost of damages is being counted in dozens of billion dollars, but the indirect costs on water-related ecosystem services and benefits could be equally expensive, with impacts lasting for decades. In Australia, the extreme precipitation (“200 mm day −1 in several location”) that interrupted the catastrophic wildfire season triggered a series of watershed effects from headwaters to areas downstream. The increased runoff and erosion from burned areas disrupted water supplies in several locations. These post-fire watershed hazards via source water contamination, flash floods, and mudslides can represent substantial, systemic long-term risks to drinking water production, aquatic life, and socio-economic activity. Scenarios similar to the recent event in Australia are now predicted to unfold in the Western USA. This is a new reality that societies will have to live with as uncharted fire activity, water crises, and widespread human footprint collide all-around of the world. Therefore, we advocate for a more proactive approach to wildfire-watershed risk governance in an effort to advance and protect water security. We also argue that there is no easy solution to reducing this risk and that investments in both green (i.e., natural) and grey (i.e., built) infrastructure will be necessary. Further, we propose strategies to combine modern data analytics with existing tools for use by water and land managers worldwide to leverage several decades worth of data and knowledge on post-fire hydrology.

Hydrological processes in mountain headwater basins are changing as climate and vegetation change. Interactions between hydrological processes and subalpine forest ecological function are important to mountain water supplies due to their control on evapotranspiration (ET). Improved understanding of the sensitivity of these interactions to seasonal and interannual changes in snowmelt and summer rainfall is needed as these interactions can impact forest growth, succession, health, and susceptibility to wildfire. To better understand this sensitivity, this research examined ET for a sub-alpine forest in the Canadian Rockies over two contrasting growing seasons and quantified the contribution of transpiration (T) from the younger tree population to overall stand ET. The younger population was focused on to permit examination of trees that have grown under the effect of recent climate change and will contribute to treeline migration, and subalpine forest densification and succession. Research sites were located at Fortress Mountain Research Basin, Kananaskis, Alberta, where the subalpine forest examined is composed of Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce). Seasonal changes in water availability from snowmelt, precipitation, soil moisture reserves yielded stark differences in T and ET between 2016 and 2017. ET was higher in the drier year (2017), which had late snowmelt and lower summer rainfall than in the wetter year (2016) that had lower snowmelt and a rainy summer, highlighting the importance of spring snowmelt recharge of soil moisture. However, stand T of the younger trees (73% of forest population) was greater (64 mm) in 2016 (275 mm summer rainfall) than 2017 (39 mm T, 147 mm summer rainfall), and appears to be sensitive to soil moisture decreases in fall, which are largely a function of summer period rainfall. Relationships between subalpine forest water use and different growing season and antecedent (snowmelt period) hydrological conditions clarify the interactions between forest water use and alpine hydrology, which can lead to better anticipation of the hydrological response of subalpine forest-dominated basins to climate variability and change.

Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada School of Geosciences, University of Birmingham, Birmingham, UK Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia, Canada Landcare Research, Lincoln, New Zealand Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada School of Geographical Sciences, University of Bristol, Bristol, UK Cabot Institute, University of Bristol, Bristol, UK Hetch Hetchy Power, San Francisco, California, USA Division of Earth and Ocean Sciences, Nicolas School of the Environment, Duke University, Durham, North Carolina, USA GNS Science, Lower Hutt, New Zealand Department of Civil Engineering, Univeristy of Calgary, Calgary, Alberta, Canada Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan Department of Geography, University of Zurich, Zurich, Switzerland Dept of Biological and Ecological Engineering, Oregon State University, Corvallis, Oregon, USA Faculty of Environment & Natural Resources, University of Freiburg, Freiburg, Germany Faculty of Engineering, University of Bristol, Bristol, UK

Treed peatlands exhibit both crown and smouldering fire potential; however, neither are included in Canadian wildfire management models and, as such, they are not formally represented in management decision-making. The lack of smouldering fire risk assessment is a critical research gap as these fires can represent heavy resource draws and are predominant sources of smoke, air pollutants and atmospheric carbon. Here, for the first time, we combine existing knowledge of the controls on smouldering peat fire with expert opinion-based weightings through a multi-criteria decision analysis, to map the smouldering fire potential (i.e. hazard) of treed peatlands in the Boreal Plains, Alberta, Canada. We find that smouldering potential varies considerably between treed peatlands and that areas of sparser peatland coverage may contain high smouldering-potential peatlands. Further, we find that treed peatlands are a common feature in the wildland–human interface and that proportionally, the area of high smouldering potential is greater closer to roads compared with farther away. Our approach enables a quantitative measure of smouldering fire potential and evidences the need to incorporate peatland–wildfire interactions into wildfire management operations. We suggest that similar frameworks could be used in other peatland dominated regions as part of smouldering fire risk assessments.

In Canada, Indigenous youth have remained resilient despite being confronted with a wide range of structural and systemic risks, such as long-lasting boil water advisories, over-representation in the child welfare system, and injustices related to land treaties. As people of the land, all disruptions to ecological health are a disruption to personal and community holistic health. Land-based activities and cultural continuity strengthen pathways of perseverance for Indigenous youth (Toombs et al., 2016). For youth, cultural self-expression and personal agency are enhanced with digital platforms, which are well-suited to Indigenous people’s strengths in art, music, and oral forms of passing on knowledge. The field of mental health has turned to e-supports such as mobile applications (apps) that can provide easy-to-access intervention, when needed. To date, resilience interventions have received comparatively less attention than the study of resilience factors and processes. It is timely to review the extant literature on mental health apps with Indigenous youth as, currently, Indigenous apps are in early research stages. Critically reviewing work to date, it is argued that an inclusive and expansive concept of resilience, coherent with Indigenous holistic health views, is well-positioned as a foundation for collaborative resilience app development. To date, few mental health apps have been researched with Indigenous youth, and fewer have been co-constructed with Indigenous youth and their community members. The current literature points to feasibility in terms of readiness or potential usage, and functionality for promoting an integrated cultural and holistic health lens. As this effort may be specific to a particular Indigenous nation’s values, stories, and practices, we highlight the Haudenosaunee conceptual wellness model as one example to guide Indigenous and non-Indigenous science integration, with a current project underway with the JoyPopTM mHealth app for promoting positive mental health and resilience.

Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.

Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyze and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, the Portage Network, Gordon Foundation's DataStream, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally. This article is protected by copyright. All rights reserved.

As continental to global scale high-resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing-based calibration with two deterministic forcing-based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor-quality forcings. The comparison experiment is conducted with a six-parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing-based calibrations. Results show that the ensemble forcing-based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing-based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor-quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor-quality meteorological inputs can be effectively filtered out via our ensemble forcing-based calibration methodology and thus discarded in any post-calibration model applications. The proposed ensemble forcing-based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.

To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple task. Handling such components is even more burdensome for frequently assembled and executed complex workflows for investigating large datasets with different technologies (i.e., various learning algorithms or models). However, a great many studies propose various techniques and technologies for managing and recommending services in a SWfMS, but only a very few studies consider the management of data in a SWfMS for efficient storing and facilitating workflow executions. Furthermore, there is no study to inquire about the effectiveness and efficiency of such data management in a SWfMS from a user perspective. In this paper, we present and evaluate a GUI version of such a novel approach of intermediate data management with two use cases (Plant Phenotyping and Bioinformatics). The technique we call GUI-RISPTS (Recommending Intermediate States from Pipelines Considering Tool-States) can facilitate executions of workflows with processed data (i.e., intermediate outcomes of modules in a workflow) and can thus reduce the computational time of some modules in a SWfMS. We integrated GUI-RISPTS with an existing workflow management system called SciWorCS. In SciWorCS, we present an interface that users use for selecting the recommendation of intermediate states (i.e., modules' outcomes). We investigated GUI-RISPTS's effectiveness from users' perspectives along with measuring its overhead in terms of storage and efficiency in workflow execution.

Given an m×n table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m×n convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell’s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. It aims to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained as a standard Python package and comes with queries, relevance judgments, pre-built indexes, and evaluation scripts for many commonly used IR test collections. We aim to support, out of the box, the entire research lifecycle of efforts aimed at improving ranking with modern neural approaches. In particular, Pyserini supports sparse retrieval (e.g., BM25 scoring using bag-of-words representations), dense retrieval (e.g., nearest-neighbor search on transformer-encoded representations), as well as hybrid retrieval that integrates both approaches. This paper provides an overview of toolkit features and presents empirical results that illustrate its effectiveness on two popular ranking tasks. Around this toolkit, our group has built a culture of reproducibility through shared norms and tools that enable rigorous automated testing.

AbstractHydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequen...

Many organizations use legacy systems as these systems contain their valuable business rules. However, these legacy systems answer the past requirements but are difficult to maintain and evolve due to old technology use. In this situation, stockholders decide to renovate the system with a minimum amount of cost and risk. Although the renovation process is a more affordable choice over redevelopment, it comes with its risks such as performance loss and failure to obtain quality goals. A proper test process can minimize risks incorporated with the renovation process. This work introduces a testing model tailored for the migration and re-engineering process and employs test automation, which results in early bug detection. Moreover, the automated tests ensure functional sameness between the old and the new system. This process enhances reliability, accuracy, and speed of testing.

Distributed hydrological models predict the spatial variability in processes that govern observed mass and energy fluxes. A challenge associated with the use of these models is the computational burden associated with representing the Earth's (sub)surface via millions of computational elements. This burden is exacerbated as more complex process representations are included because their parameterizations involve computationally intensive mathematical functions. Lookup tables (LUTs) approximate a mathematical function by interpolating precomputed values of the function. Highly accurate approximations are possible for substantially reduced computational costs. In this work, a general methodology using the C++ LUT library FunC is applied to identify and replace computationally intensive mathematical function evaluations in the Canadian Hydrological Model (CHM). The use of LUTs introduces a pointwise relative error below 10 − 8 and provides a reduction in run time of almost 20%. This work shows how LUTs can be implemented with relatively little pain and yield significant computational savings for distributed hydrological models. • The Canadian Hydrological Model (CHM) is profiled and expensive mathematical functions identified. • FunC was used to replace the expensive mathematical functions in CHM with lookup tables. • The run-time performance of CHM was improved by approximately 20% on two realistic simulations. • A general methodology for using FunC to replace expensive mathematical functions with lookup tables is given.

Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements. For many regions, however, such data are only scarcely available. Facing this problem, many studies simply trained their machine learning models on the region's available data, leaving possible repercussions of this strategy unclear. In this study, we evaluate the sensitivity of tree- and LSTM-based models to limited training data, both in terms of geographic diversity and different time spans. We feed the models meteorological observations disseminated with the CAMELS dataset, and individually restrict the training period length, number of training basins, and input sequence length. We quantify how additional training data improve predictions and how many previous days of forcings we should feed the models to obtain best predictions for each training set size. Further, our findings show that tree- and LSTM-based models provide similarly accurate predictions on small datasets, while LSTMs are superior given more training data.

Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive. Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer’s profile, form a balanced team, support code review, etc. However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement. To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions. First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes. Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples. We also studied the sensitivity of change classification of various types of tokens present in commit messages. The experimental outcomes employing 10-fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%. The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification

Release notes are admitted as an essential document by practitioners. They contain the summary of the source code changes for the software releases, such as issue fixes, added new features, and performance improvements. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1,900 releases, among them 37% of the release notes are empty. We propose an automatic generate release notes approach based on the commit messages and merge pull-request (PR) titles to mitigate this problem. We implement one of the popular extractive text summarization techniques, i.e., the TextRank algorithm. However, accurate keyword extraction is a vital issue in text processing. The keyword matching and topic extraction process of the TextRank algorithm ignores the semantic similarity among texts. To improve the keyword extraction method, we integrate the GloVe word embedding technique with TextRank. We develop a dataset with 1,213 release notes (after null filtering) and evaluate the generated release notes through the ROUGE metric and human evaluation. We also compare the performance of our technique with another popular extractive algorithm, latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms LSA.

Software architectural changes involve more than one module or component and are complex to analyze compared to local code changes. Development teams aiming to review architectural aspects (design) of a change commit consider many essential scenarios such as access rules and restrictions on usage of program entities across modules. Moreover, design review is essential when proper architectural formulations are paramount for developing and deploying a system. Untangling architectural changes, recovering semantic design, and producing design notes are the crucial tasks of the design review process. To support these tasks, we construct a lightweight tool [4] that can detect and decompose semantic slices of a commit containing architectural instances. A semantic slice consists of a description of relational information of involved modules, their classes, methods and connected modules in a change instance, which is easy to understand to a reviewer. We extract various directory and naming structures (DANS) properties from the source code for developing our tool. Utilizing the DANS properties, our tool first detects architectural change instances based on our defined metric and then decomposes the slices (based on string processing). Our preliminary investigation with ten open-source projects (developed in Java and Kotlin) reveals that the DANS properties produce highly reliable precision and recall (93-100%) for detecting and generating architectural slices. Our proposed tool will serve as the preliminary approach for the semantic design recovery and design summary generation for the project releases.

Evolutionary coupling is a well investigated phenomenon in software maintenance research and practice. Association rules and two related measures, support and confidence, have been used to identify evolutionary coupling among program entities. However, these measures only emphasize the co-change (i.e., changing together) frequency of entities and cannot determine whether the entities co-evolved by experiencing related changes. Consequently, the approach reports false positives and fails to detect evolutionary coupling among infrequently co-changed entities. We propose a new measure, identifier correspondence (id-correspondence), that quantifies the extent to which changes that occurred to the co-changed entities are related based on identifier similarity. Identifiers are the names given to different program entities such as variables, methods, classes, packages, interfaces, structures, unions etc. We use Dice-Sørensen co-efficient for measuring lexical similarity between the identifiers involved in the changed lines of the co-changed entities. Our investigation on thousands of revisions from nine subject systems covering three programming languages shows that id-correspondence can considerably improve the detection accuracy of evolutionary coupling. It outperforms the existing state-of-the-art evolutionary coupling based techniques with significantly higher recall and F-score in predicting future co-change candidates.

When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.

Release notes are admitted as an essential technical document in software maintenance. They summarize the main changes, e.g. bug fixes and new features, that have happened in the software since the previous release. Manually producing release notes is a time-consuming and challenging task. For that reason, sometimes developers neglect to write release notes. For example, we collect data from GitHub with over 1900 releases, and among them, 37% of the release notes are empty. To mitigate this problem, we propose an automatic release notes generation approach by applying the text summarization techniques, i.e. TextRank. To improve the keyword extraction method of traditional TextRank, we integrate the GloVe word embedding technique with TextRank. After generating release notes automatically, we apply machine learning algorithms to classify the release note contents (or sentences). We classify the contents into six categories, e.g. bug fixes and performance improvements, to represent the release notes better for users. We use the evaluation metric, e.g. ROUGE, to evaluate the automatically generated release notes. We also compare the performance of our technique with two popular extractive algorithms, e.g. Luhn’s and latent semantic analysis (LSA). Our evaluation results show that the improved TextRank method outperforms the two algorithms.

The acceleration of climate change and its impact highlight the need for long-term reliable climate data at high spatiotemporal resolution to answer key science questions in cold regions hydrology. Prior to the digital age, climate records were archived on paper. For example, from the 1950s to the 1990s, solar radiation data from recording stations worldwide were published in booklets by the former Union of Soviet Socialist Republics (USSR) Hydrometeorological Service. As a result, the data are not easily accessible by most researchers. The overarching aim of this research is to develop techniques to convert paper-based climate records into a machine-readable format to support environmental research in cold regions. This study compares the performance of a proprietary optical character recognition (OCR) service with an open-source OCR tool for digitizing hydrometeorological data. We built a digitization pipeline combining different image preprocessing techniques, semantic segmentation, and an open-source OCR engine for extracting data and metadata recorded in the scanned documents. Each page contains blocks of text with station names and tables containing the climate data. The process begins with image preprocessing to reduce noise and to improve quality before the page content is segmented to detect tables and finally run through an OCR engine for text extraction. We outline the digitization process and report on initial results, including different segmentation approaches, preprocessing image algorithms, and OCR techniques to ensure accurate extraction and organization of relevant metadata from thousands of scanned climate records. We evaluated the performance of Tesseract OCR and ABBYY FineReader on text extraction. We find that although ABBY FineReader has better accuracy on the sample data, our custom extraction pipeline using Tesseract is efficient and scalable because it is flexible and allows for more customization.

Applications of image registration tasks are computation-intensive, memory-intensive, and communication-intensive. Robust efforts are required on error recovery and re-usability of both the data and the operations, along with performance optimization. Considering these, we explore various programming models aiming to minimize the folding operations (such as join and reduce) which are the primary candidates of data shuffling, concurrency bugs and expensive communication in a distributed cluster. Particularly, we analyze modular MapReduce execution of an image registration pipeline (IRP) with the external and internal data (data-tunneling) flow mechanism and compare them with the compact model. Experimental analyzes with the ComputeCanada cluster and a crop field data-sets containing 1000 images show that these design options are valuable for large-scale IRPs executed with a MapReduce cluster. Additionally, we present an effectiveness measurement metric to analyze the impact of a design model for the Big IRP, accumulating the error-recovery and re-usability metrics along with the data size and execution time. Our explored design models and their performance analysis can serve as a benchmark for the researchers and application developers who deploy large-scale image registration and other image processing tasks.

We describe a method for analyzing the structure of a system of nonlinear integro-differential–algebraic equations (IDAEs) that generalizes the Σ -method for the structural analysis of differential–algebraic equations. The method is based on the sparsity pattern of the IDAE and the ν -smoothing property of a Volterra integral operator. It determines which equations and how many times they need to be differentiated to determine the index, and it reveals the hidden constraints and compatibility conditions in order to prove the existence of a solution. The success of the Σ -method is indicated by the non-singularity of a certain Jacobian matrix. Although it is likely the Σ -method can be directly applied with success to many problems of practical interest, it can fail on some solvable IDAEs. Accordingly, we also present two techniques for addressing these failures.

Abstract The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are two-fold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral sub-surface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as “laugh tests” for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives model (SUMMA) implemented using the SUite of Nonlinear and DIfferential/Algebraic equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against (1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature; (2) analytical solutions; and (3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models.

Abstract Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: (1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? (2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.

Abstract In this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

Plant transpiration is the dominant water flux in the global terrestrial water balance and a key process in the hydrological sciences. Stable isotopes have contributed greatly to this understanding...

Source water apportionment studies using the dual isotopes of oxygen and hydrogen have revolutionized our understanding of ecohydrology. But despite these developments—mostly over the past decade—many technical problems still exist in terms of linking xylem water to its soil water and groundwater sources. This is mainly due to sampling issues and possible fractionation of xylem water. Here we explore whether or not leaf water alone can be used to quantify the blend of rainfall event inputs from which the leaf water originates. Leaf water has historically been avoided in plant water uptake studies due to the extreme fractionation processes at the leaf surface. In our proof of concept work we embrace those processes and use the well-known Craig and Gordon model to map leaf water back to its individual precipitation event water sources. We also employ a Bayesian uncertainty estimation approach to quantify source apportionment uncertainties. We show this using a controlled, vegetated lysimeter experiment where we were able to use leaf water to correctly identify the mean seasonal rainfall that was taken up by the plant, with an uncertainty typically within ±1‰ for δ18O. While not appropriate for all source water studies, this work shows that leaf water isotope composition may provide a new, relatively un-intrusive method for addressing questions about the plant water source.

Closure of the soil water balance is fundamental to ecohydrology. But closing the soil water balance with hydrometric information offers no insight into the age distribution of water transiting the soil column via deep drainage or the combination of soil evaporation and transpiration. This is a major challenge in our discipline currently; tracing the water balance is the needed next step. Here we report results from a controlled tracer experiment aimed at both closing the soil water balance and tracing its individual components. This was carried out on a 2.5 m3 lysimeter planted with a willow tree. We applied 25 mm of isotopically enriched water on top of the lysimeter and tracked it for 43 days through the soil water, the bottom drainage, and the plant xylem. We then destructively sampled the system to quantify the remaining isotope mass. More than 900 water samples were collected for stable isotope analysis to trace the labeled irrigation. We then used these data to quantify when and where the labeled irrigation became the source of plant uptake or deep percolation. Evapotranspiration dominated the water balance outflow (88%). Tracing the transpiration flux showed further that transpiration was soil water that had fallen as precipitation 1–2 months prior. The tracer breakthrough in transpiration was complex and different from the breakthrough curves observed within the soil or in the bottom drainage. Given the lack of direct experimental data on travel time to transpiration, these results provide a first balance closure where all the relevant outflows are traced.

This commentary explores the challenges and opportunities associated with a possible transition of Water Resources Research to a publication model where all articles are freely available upon publication (“Gold” open access). It provides a review of the status of open access publishing models, a summary of community input, and a path forward for AGU leadership. The decision to convert to open access is framed by a mix of finances and values. On the one hand, the challenge is to define who pays, and how, and what we can do to improve the affordability of publishing. On the other hand, the challenge is to increase the extent to which science is open and accessible. The next steps for the community include an incisive analysis of the financial feasibility of different cost models, and weighing the financial burden for open access against the desire to further advance open science.

Projections of change in high-flow extremes with global warming vary widely among, and within, large midlatitude river basins. The spatial variability of these changes is attributable to multiple causes. One possible and little-studied cause of changes in high-flow extremes is a change in the synchrony of mainstem and tributary streamflow during high-flow extremes at the mainstem-tributary confluence. We examined reconstructed and simulated naturalized daily streamflow at confluences on the Columbia River in western North America, quantifying changes in synchrony in future streamflow projections and estimating the impact of these changes on high-flow extremes. In the Columbia River basin, projected flow regimes across colder tributaries initially diverge with warming as they respond to climate change at different rates, leading to a general decrease in synchrony, and lower high-flow extremes, relative to a scenario with no changes in synchrony. Where future warming is sufficiently large to cause most subbasins upstream from a confluence to transition toward a rain-dominated, warm regime, the decreasing trend in synchrony reverses itself. At one confluence with a major tributary (the Willamette River), where the mainstem and tributary flow regimes are initially very different, warming increases synchrony and, therefore, high-flow magnitudes. These results may be generalizable to the class of large rivers with large contributions to flood risk from the snow (i.e., cold) regime, but that also receive considerable discharge from tributaries that drain warmer basins.

Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain-on-snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics-based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes.

Rationale Hydrogen and oxygen stable isotope ratios (δ2H, δ17O, and δ18O values) are commonly used tracers of water. These ratios can be measured by isotope ratio infrared spectroscopy (IRIS). However, IRIS approaches are prone to errors induced by organic compounds present in plant, soil, and natural water samples. A novel approach using 17O-excess values has shown promise for flagging spectrally contaminated plant samples during IRIS analysis. A systematic assessment of this flagging system is needed to prove it useful. Methods Errors induced by methanol and ethanol water mixtures on measured IRIS and isotope ratio mass spectrometry (IRMS) results were evaluated. For IRIS analyses both liquid- and vapour-mode (via direct vapour equilibration) methods are used. The δ2H, δ17O, and δ18O values were measured and compared with known reference values to determine the errors induced by methanol and ethanol contamination. In addition, the 17O-excess contamination detection approach was tested. This is a post-processing detection tool for both liquid and vapour IRIS triple-isotope analyses, utilizing calculated 17O-excess values to flag contaminated samples. Results Organic contamination induced significant errors in IRIS results, not seen in IRMS results. Methanol caused larger errors than ethanol. Results from vapour-IRIS analyses had larger errors than those from liquid-IRIS analyses. The 17O-excess approach identified methanol driven error in liquid- and vapour-mode IRIS samples at levels where isotope results became unacceptably erroneous. For ethanol contaminated samples, a mix of erroneous and correct flagging occurred with the 17O-excess method. Our results indicate that methanol is the more problematic contaminant for data corruption. The 17O-excess method was therefore useful for data quality control. Conclusions Organic contamination caused significant errors in IRIS stable isotope results. These errors were larger during vapour analyses than during liquid IRIS analyses, and larger for methanol than ethanol contamination. The 17O-excess method is highly sensitive for detecting narrowband (methanol) contamination error in vapour and liquid analysis modes in IRIS.

• Natural tracers reveal runoff sources in UK natural flood management catchment. • Water already stored in the catchments dominated runoff in high flow events. • Plantation forest cover reduced the fraction of rapid rainfall runoff. • Soils and geology dominated forest cover as control on rapid rainfall runoff fraction. • Differences in sources were greater between events than between catchments. United Kingdom (UK). Natural flood management (NFM) schemes are increasingly prominent in the UK. Studies of NFM have not yet used natural tracers at catchment scale to investigate how interventions influence partitioning during storms between surface rainfall runoff and water already stored in catchments. Here we investigate how catchment properties, particularly plantation forestry, influence surface storm rainfall runoff. We used hydrograph separation based on hydrogen and oxygen isotopes ( 2 H, 18 O) and acid neutralising capacity from high flow events to compare three headwater catchments (2.4-3.1 km 2 ) with differences in plantation forest cover ( Picea sitchensis: 94%, 41%, 1%) within a major UK NFM pilot, typical of the UK uplands. Plantation forest cover reduced the total storm rainfall runoff fraction during all events (by up to 11%) when comparing two paired catchments with similar soils, geology and topography but ∼50% difference in forest cover. However, comparison with the third catchment, with negligible forest cover but different characteristics, suggests that soils and geology were dominant controls on storm rainfall runoff fraction. Furthermore, differences between events were greater than differences between catchments. These findings suggest that while plantation forest cover may influence storm rainfall runoff fractions, it is not a dominant control in temperate upland UK catchments, especially for larger events. Soils and geology may exert greater influence, with implications for planning NFM.

• Streamflow was satisfactorily simulated by MESH model in Hudson Bay lowlands. • Higher precipitation and streamflow observed in the western watersheds in 1995–2008. • The wet period in 1995–2008 was due to a shift in regional atmospheric circulation. • PDO and EP-NP also influenced this wet period. • Dryer period but sustained streamflow in 2009–2019 due to permafrost thaw. Hudson Bay Lowlands watersheds, Ontario, Canada. The rivers in the Hudson Bay Lowlands are a major source of freshwater entering the Arctic Ocean and they also cause major floods. In recent decades, this region has been affected by major changes in hydroclimatic processes attributed to climate change and natural climate variability. In this study, we used ERA5 reanalysis data, hydrometric observations, and the hydrological model MESH, to investigate the impact of atmospheric circulation on the inter-decadal variability of streamflow between 1979 and 2018 in the Hudson Bay Lowlands. The natural climate variability was assessed using a weather regimes approach based on the discretization of daily geopotential height anomalies (Z500) from ERA5 reanalysis, as well as large scale oceanic and atmospheric variability modes. The results showed an anomalous convergence of atmospheric moisture flux between 1995–2008 that enhanced precipitation and increased streamflow in the western part of the region. This moisture convergence was likely driven by the combination of (i) low pressure anomalies in the East Coast of North America and (ii) low pressure anomalies in western regions of Canada, associated with the cold phase of the pacific decadal oscillation (PDO). Since 2009, streamflow remains high, likely due to more groundwater discharge associated with the degradation of permafrost.

• Stable isotope tracing of plant water use can illuminate plant water sources. • Xylem water isotope values showed strong sorting and niche segregation. • The majority of the observed species relied on 0.0–0.2 m depth soil water. • Tropical forest water uptake depth is largely driven by tree functional traits. Stable isotope tracing of plant water use can illuminate plant water sources. But to date, the number of species tested at any given site has been minimal. Here, we sample 46 tropical hardwood tree species in a 0.32 ha plot with uniform soils. Soil water was characterized at 6 depths at 0.2 m intervals down to 1 m and showed simple and predictable depth patterns of δ 2 H and δ 18 O, and simple and spatially uniform isotope composition at each depth. Nevertheless, tree xylem water δ 2 H and δ 18 O showed remarkable variation covering the full range of soil composition, suggesting strong sorting and niche segregation across the small plot. Wood density, tree size and mean basal area increment together explained approximately 55% of the variance of xylem water isotope composition through principal component analysis. A Bayesian mixing model was applied to the data and showed that sampled trees were either sourcing their water from very shallow or deep soil layers, with very little contribution from the middle portion of the soil profile. The majority of the observed species relied on 0.0–0.2 m depth soil water. This layer contributed approximately 75% of the xylem water which was significantly higher than the contributions from all other depths. The contribution from shallow soil was highest for trees with high wood density, slow-growing trees and small-sized trees. Our work suggests that stable isotope tracers may aid a better understanding of tropical forest water uptake depths and their relation to tree functional traits and potential hydrological niche segregation among co-occurring tropical species.

Studies of plant water sources generally assume that xylem water integrates the isotopic composition (δ 2 H and δ 18 O) of water sources and does not fractionate during uptake or transport along the transpiration pathway. However, woody xerophytes, halophytes, and trees in mesic environments can show isotopic fractionation from source waters. Isotopic fractionation and variation in isotope composition can affect the interpretation of tree water sources, but most studies to date have been greenhouse experiments. Here we present a field-based forensic analysis of xylem water isotope composition for 12 Eucalyptus tetrodonta and Corymbia nesophila trees . We used a 25-tonne excavator to access materials from the trees' maximum rooting depth of 3 m to their highest canopies at 38 m. Substantial within-tree variation occurred in δ 2 H (−91.1‰ to −35.7‰ E. tetrodonta ; −88.8‰ to −24.5‰ C. nesophila ) and δ 18 O (−12.3‰ to −5.0‰ E. tetrodonta ; −10.9‰ to −0.3‰ C. nesophila ), with different root-to-branch isotope patterns in each species. Soil water δ 2 H and δ 18 O dual isotope slopes (7.26 E. tetrodonta , 6.66 C. nesophila ) were closest to the Local Meteoric Water Line (8.4). The dual isotope slopes of the trees decreased progressively from roots (6.45 E. tetrodonta , 6.07 C. nesophila ), to stems (4.61 E. tetrodonta , 5.97 C. nesophila ) and branches (4.68 E. tetrodonta , 5.67 C. nesophila ), indicative of fractionation along the xylem stream. Roots of both species were more enriched in 2 H and 18 O than soil water at all sampled depths. Bayesian mixing model analysis showed that estimated proportions of water sourced from different depths reflected the contrasting root systems of these species. Our study adds evidence of isotopic fractionation from water uptake and along the transpiration stream in mature trees in monsoonal environments, affecting the interpretation of water sources. We discuss the findings with view of interpreting aboveground xylem water isotopic composition, incorporating knowledge of root systems. • Isotopic fractionation of xylem water may affect plant water source identification. • We analysed xylem δ 2 H and δ 18 O from roots to branches in mature trees in a savanna. • Fractionation increased from below- to aboveground xylem in the dual isotope space • Root structure assessment helped clarify aboveground interpretation of water use. • Future studies should consider xylem water fractionation and include plant traits.

Rivers are under enormous threat worldwide and large amounts of money are invested in river restoration. Contrary to the costs, the benefits of river restoration are much harder to quantify. In this study, the benefits of restoring different sections of the Yongding River in Beijing, China, are estimated through a discrete choice experiment (DCE). Place attachment is measured by sampling residents upstream and downstream and using the river sections as labelled alternatives in the DCE. As expected, the improvement of water quality is valued highly by all river basin residents, and place attachment and spatial preference heterogeneity play a significant role in public willingness to pay (WTP) for river restoration. Although respondents are willing to give up only a small share of their disposable income, public WTP for improved river water quality is a factor 2 to 4 higher than the current household water bill. These findings provide important guidance for the recovery of the investment costs associated with river restoration projects. • The benefits of urban river restoration in Beijing are estimated • Residents living up and downstream of the Yongding River are interviewed • Public WTP for water quality improvements is several times higher than the current water bill • Spatial preferences and place attachment play a key role in public preferences • The study provides important info to guide investment decisions in river restoration

Three decades of non-market water quality valuation (NMWQV) studies in Canada are analyzed to generate a generic benefits transfer function. Contrary to the large valuation literature focusing on water and wilderness-based recreation in Canada, the number of studies related to water quality is limited. NMWQV studies lack a common design, including consistent adherence to a Canada-specific water quality ladder (WQL). Despite the high degree of data heterogeneity, values extracted from the literature show an increasing step function when relating them to the Resources for the Future WQL. Meta-regression models (MRMs) explain a large share of the variation in value estimates based on the type of water resources, population and methodological characteristics. Baseline water quality and the size of the water quality change are significant determinants of the estimated non-market values. With a relative mean prediction error of no more than 20 percent, the predictive power of the estimated MRMs is high. As such, they are an important step forward in the development of a policy-relevant water quality valuation model. However, there is a clear need for the development of more coherent non-market valuation guidelines in the Canadian water context.

Extensive wetland drainage has occurred across the Canadian Prairies, and drainage activities are ongoing in many areas (Dahl 1990; Watmough and Schmoll 2007; Bartzen et al. 2010; Dahl 2014; Prairi...

Abstract Regulators require adequate information to select best practices with less ecosystem impacts for remediation of freshwater ecosystems after oil spills. Zooplankton are valuable indicators of aquatic ecosystem health as they play pivotal roles in biochemical cycles while stabilizing food webs. Compared with morphological identification, metabarcoding holds promise for cost-effective, high-throughput, and benchmarkable biomonitoring of zooplankton communities. The objective of this study was to apply DNA and RNA metabarcoding of zooplankton for ecotoxicological assessment and compare it with traditional morphological identification in experimental shoreline enclosures in a boreal lake. These identification methods were also applied in context of assessing response of the zooplankton community exposed to simulated spills of diluted bitumen (dilbit), with experimental remediation practices (enhanced monitored natural recovery and shoreline cleaner application). Metabarcoding detected boreal zooplankton taxa up to the genus level, with a total of 24 shared genera, and while metabarcoding-based relative abundance served as an acceptable proxy for biomass inferred by morphological identification (ρ ≥ 0.52). Morphological identification determined zooplankton community composition changes due to treatments at 11 days post-spill (PERMANOVA, p = 0.0143) while metabarcoding methods indicated changes in zooplankton richness and communities at 38 days post-spill (T-test, p

Due to commercial uses and environmental degradation of aryl phosphate esters, diphenyl phosphate (DPhP) is frequently detected in environmental matrices and is thus of growing concern worldwide. However, information on potential adverse effects of chronic exposure to DPhP at environmentally realistic concentrations was lacking. Here, we investigated the effects of life cycle exposure to DPhP on zebrafish at environmentally relevant concentrations of 0.8, 3.9, or 35.6 μg/L and employed a dual-omics approach (metabolomics and transcriptomics) to characterize potential modes of action. Exposure to DPhP at 35.6 μg/L for 120 days resulted in significant reductions in body mass and length of male zebrafish, but did not cause those same effects to females. Predominant toxicological mechanisms, including inhibition of oxidative phosphorylation, down-regulation of fatty acid oxidation, and up-regulation of phosphatidylcholine degradation, were revealed by integrated dual-omics analysis and successfully linked to adverse outcomes. Activity of succinate dehydrogenase and protein content of carnitine O-palmitoyltransferase 1 were significantly decreased in livers of male fish exposed to DPhP, which further confirmed the proposed toxicological mechanisms. This study is the first to demonstrate that chronic, low-level exposure to DPhP can retard growth via inhibiting energy output in male zebrafish.

Detection of SARS-CoV-2 RNA in wastewater is a promising tool for informing public health decisions during the COVID-19 pandemic. However, approaches for its analysis by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) are still far from standardized globally. To characterize inter- and intra-laboratory variability among results when using various methods deployed across Canada, aliquots from a real wastewater sample were spiked with surrogates of SARS-CoV-2 (gamma-radiation inactivated SARS-CoV-2 and human coronavirus strain 229E [HCoV-229E]) at low and high levels then provided "blind" to eight laboratories. Concentration estimates reported by individual laboratories were consistently within a 1.0-log10 range for aliquots of the same spiked condition. All laboratories distinguished between low- and high-spikes for both surrogates. As expected, greater variability was observed in the results amongst laboratories than within individual laboratories, but SARS-CoV-2 RNA concentration estimates for each spiked condition remained mostly within 1.0-log10 ranges. The no-spike wastewater aliquots provided yielded non-detects or trace levels (<20 gene copies/mL) of SARS-CoV-2 RNA. Detections appear linked to methods that included or focused on the solids fraction of the wastewater matrix and might represent in-situ SARS-CoV-2 to the wastewater sample. HCoV-229E RNA was not detected in the no-spike aliquots. Overall, all methods yielded comparable results at the conditions tested. Partitioning behavior of SARS-CoV-2 and spiked surrogates in wastewater should be considered to evaluate method effectiveness. A consistent method and laboratory to explore wastewater SARS-CoV-2 temporal trends for a given system, with appropriate quality control protocols and documented in adequate detail should succeed.

The microbiome has been described as an additional host “organ” with well-established beneficial roles. However, the effects of exposures to chemicals on both structure and function of the gut microbiome of fishes are understudied. To determine effects of benzo[ a ]pyrene (BaP), a model persistent organic pollutant, on structural shifts of gut microbiome in juvenile fathead minnows ( Pimephales promelas ), fish were exposed ad libitum in the diet to concentrations of 1, 10, 100, or 1000 μg BaP g −1 food, in addition to a vehicle control, for two weeks. To determine the link between exposure to BaP and changes in the microbial community, concentrations of metabolites of BaP were measured in fish bile and 16S rRNA amplicon sequencing was used to evaluate the microbiome. Exposure to BaP only reduced alpha-diversity at the greatest exposure concentrations. However, it did alter community composition assessed as differential abundance of taxa and reduced network complexity of the microbial community in all exposure groups. Results presented here illustrate that environmentally-relevant concentrations of BaP can alter the diversity of the gut microbiome and community network connectivity. Highlights • Dominant phyla of gut microbiome are consistent with those of other freshwater fishes. • BaP metabolites and exposure doses were consistent with those found in contaminated sites. • Dietary BaP exposure has significant, dose-dependent effects on the fish gut microbiome. • Dietary BaP exposure altered association networks of gut microbiome. Environmentally-relevant concentrations of BaP can alter the diversity of the gut microbiome and community network connectivity via dietary exposure route.

Like many amphibians, wood frog (Lithobates sylvaticus) populations have likely declined or experienced local extirpations as a result of habitat alterations. Despite this, wood frogs are still present and breeding in altered landscapes, like the agricultural Prairie Pothole Region of central Canada, and are exposed to a variety of anthropogenic impacts. As tadpoles, water contamination can have negative effects on growth, development, and immune systems. To investigate the potential effects of agricultural land use on tadpole growth and immune system stress, we used boosted regression trees to model body mass, body condition, and neutrophil to lymphocyte ratios, a measure of immune stress, against 32 variables including water quality, wetland habitat, and landscape-level measures. Developmental stage strongly influenced all 3 endpoints, and body mass was negatively influenced by higher levels of total dissolved solids (>600-700 mg/L) and at the first sign of pesticide detection (>0.01 proportion pesticides detected of those screened). While correlative, these data suggest that tadpoles developing in agricultural environments may experience survival and reproductive disadvantages if they metamorphose at smaller body sizes. Given the potential impacts this can have on adult frogs and frog populations, these results provide an impetus for further field-based investigation into the effects that pesticides, and especially total dissolved solids, may have on tadpoles. Environ Toxicol Chem 2021;40:2269-2281. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Arctic warming associated with global climate change poses a significant threat to populations of wildlife in the Arctic. Since lipids play a vital role in adaptation of organisms to variations in temperature, high-resolution mass-spectrometry-based lipidomics can provide insights into adaptive responses of organisms to a warmer environment in the Arctic and help to illustrate potential novel roles of lipids in the process of thermal adaption. In this study, we studied an ecologically and economically important species-Arctic char (Salvelinus alpinus)-with a detailed multi-tissue analysis of the lipidome in response to chronic shifts in temperature using a validated lipidomics workflow. In addition, dynamic alterations in the hepatic lipidome during the time course of shifts in temperature were also characterized. Our results showed that early life stages of Arctic char were more susceptible to variations in temperature. One-year-old Arctic char responded to chronic increases in temperature with coordinated regulation of lipids, including headgroup-specific remodeling of acyl chains in glycerophospholipids (GP) and extensive alterations in composition of lipids in membranes, such as less lyso-GPs, and more ether-GPs and sphingomyelin. Glycerolipids (e.g., triacylglycerol, TG) also participated in adaptive responses of the lipidome of Arctic char. Eight-week-old Arctic char exhibited rapid adaptive alterations of the hepatic lipidome to stepwise decreases in temperature while showing blunted responses to gradual increases in temperature, implying an inability to adapt rapidly to warmer environments. Three common phosphatidylethanolamines (PEs) (PE 36:6|PE 16:1_20:5, PE 38:7|PE 16:1_22:6, and PE 40:7|PE 18:1_22:6) were finally identified as candidate lipid biomarkers for temperature shifts via machine learning approach. Overall, this work provides additional information to a better understanding of underlying regulatory mechanisms of the lipidome of Arctic organisms in the face of near-future warming.

High‐throughput pipelines supported by eDNA metabarcoding have been applied in various freshwater ecosystems. Both eDNA in ethanol (EtOH) samples (ES‐eDNA) and in water samples (WS‐eDNA) can provide comprehensive classification lists with good taxonomic resolution and coverage for determining freshwater biodiversity and biomonitoring. But, the advantages of ES‐eDNA metabarcoding over WS‐eDNA metabarcoding remain unclear for routine assessments of diversity of benthic macroinvertebrates in streams.

Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.

Haig HA, Chegoonian AM, Davies J-M, Bateson D, Leavitt PR. 2021. Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria. Lake Reserv Manage. XX:XXX–XXX.Continu...

Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for man...

Abstract Two small, oligotrophic lakes at the IISD-Experimental Lakes Area in northwestern Ontario, Canada were fertilized weekly with only phosphorus (P) in the summer and early fall of 2019. The P fertilization rates were high enough (13.3 µ g l −1 added weekly) to produce dense, month-long blooms of N 2 -fixing Dolichospermum species in both lakes within 9–12 weeks after fertilization began, turning them visibly green without the addition of nitrogen. P-only fertilization increased average seasonal chlorophyll a concentrations and cyanobacteria biomass well above the pre-fertilization levels of 2017 and 2018. Nitrogen (N) content in the epilimnion of thermally stratified Lake 304 and the water column of shallow Lake 303 doubled and P storage in the water column temporarily increased during the blooms. These whole-lake fertilization experiments demonstrate that large cyanobacteria blooms can develop rapidly under high P loading without anthropogenic N inputs, suggesting that aggressive N control programs are unlikely to prevent bloom formation and that P controls should remain the cornerstone for cyanobacteria management.

Phytoplankton blooms are a global water quality issue, and successful management depends on understanding their responses to multiple and interacting drivers, including nutrient loading and climate change. Here, we examine a long-term dataset from Lake 227, a site subject to a fertilization experiment (1969–present) with changing nitrogen:phosphorus (N:P) ratios. We applied a process-oriented model, MyLake, and updated the model structure with nutrient uptake kinetics that incorporated shifting N:P and competition among phytoplankton functional groups. We also tested different temperature and P-loading scenarios to examine the interacting effects of climate change and nutrient loading on phytoplankton blooms. The model successfully reproduced lake physics over 48 yr and the timing, overall magnitude, and shifting community structure (diazotrophs vs. non-diazotrophs) of phytoplankton blooms. Intra- and interannual variability was captured more accurately for the P-only fertilization period than for the high N:P and low N:P fertilization periods, highlighting the difficulty of modeling complex blooms even in well-studied systems. A model scenario was also run which removed climate-driven temperature trends, allowing us to disentangle concurrent drivers of blooms. Results showed that increases in water temperature in the spring led to earlier and larger phytoplankton blooms under climate change than under the effects of nutrient fertilization alone. These findings suggest that successful lake management efforts should incorporate the effects of climate change in addition to nutrient reductions, including intensifying and/or expanding monitoring periods and incorporating climate change into uncertainty estimates around future conditions.

Uranium (U) contamination in groundwater from geogenic sources affects water quality globally. Here, we use a multifaceted isotopic and geochemical approach to elucidate U sources and controls on g...

Communities in Canada’s Northwest Territories (NWT) are at the forefront of the global climate emergency. Yet, they are not passive victims; local-level programs are being implemented across the region to maintain livelihoods and promote adaptation. At the same time, there is a recent call within global governance literature to pay attention to how global policy is implemented and affecting people on the ground. Thinking about these two processes, we ask the question: (how) can global governance assist northern Indigenous communities in Canada in reaching their goals of adapting their food systems to climate change? To answer this question, we argue for a “community needs” approach when engaging in global governance literature and practice, which puts community priorities and decision-making first. As part of a collaborative research partnership, we highlight the experiences of Ka’a’gee Tu First Nation, located in Kakisa, NWT, Canada. We include their successes of engaging in global network building and the systemic roadblock of lack of formal land tenure. Moreover, we analyze potential opportunities for this community to engage with global governance instruments and continue connecting to global networks that further their goals related to climate change adaptation and food sovereignty.

This paper reports the findings of an ethnographic study that involved working with local organizations, food advocates, and communities to develop strategies for expanding the nascent Northwest Territories (NWT), Canada agri-food industry. The NWT represents a unique case study in that the fledging agri-food industry has been recognized for its promise in contributing to the core goals of the transitioning NWT food system. The study is guided by two research questions: (1) How is the promise of the emerging NWT agri-food industry framed within the context of the broader food system? (2) Given this framing of the NWT agri-food industry, how can it contribute to the sustainability of the NWT food system and to the goals of food security, poverty reduction, nutrition, and economic development? Grounded in a food systems approach, we used a correlative, evolutionary SWOT analysis to profile the nascent NWT agri-food industry within the context of the existing NWT food system. Through further thematic analysis, we identify and describe two dominant narratives (agri-food industry business case narrative and agri-food industry implications narrative) and key themes within the narratives based on an adapted food systems framework. The agri-food business case narrative highlights discourse articulating the business or commercial viability for a local agri-food value chain to function, evolve, and expand. The agri-food industry implications narrative envisions the ways in which the emerging NWT agri-food industry may interact within the existing NWT food system, highlighting potential environmental, social, cultural, and political implications of an expanding commercial-based agri-food value chain. Within the two narratives, certain subcomponents of the NWT agri-food system appear to be more prevalent, including climate, soil, and ecosystems, policy/regulations/governance, socio-cultural norms, knowledge, inputs, finance, production, and consumption. We make policy and practice recommendations for co-designing an agri-food industry that serves the multiple goals of the NWT food system. As an exploratory, descriptive-structural analysis the study provides a critical empirical basis for future in-depth, fully integrated synthesis of the complex social, cultural, economic, political, and ecological dynamics shaping Northern food systems in transition.

Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L-moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L-variation, and of L-skewness and L-kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value () distribution coupled with a novel application of the Anderson-Darling Goodness-of-fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within 10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3-GC31-MM model performing well in all three scales when compared to the ground-based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi-arid regions.

As droughts have widespread social and ecological impacts, it is critical to develop long-term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best-performing models that are useful for impact assessments.

Abstract. Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding under current and future climate conditions. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. This study evaluates the extent to which models calibrated according to standard model calibration metrics such as the widely used Kling–Gupta efficiency are able to capture flood spatial coherence and triggering mechanisms. To highlight challenges related to flood simulations, we investigate how flood timing, magnitude, and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance also in flood-related studies. Specifically, we compare how four well-known models (the Sacramento Soil Moisture Accounting model, SAC; the Hydrologiska Byråns Vattenbalansavdelning model, HBV; the variable infiltration capacity model, VIC; and the mesoscale hydrologic model, mHM) represent (1) flood characteristics and their spatial patterns and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics are challenging as models underestimate flood magnitude, and flood timing is not necessarily well captured. They further show that changes in precipitation and temperature are not always well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. From a large sample of catchments and with multiple models, we conclude that calibration on the integrated Kling–Gupta metric alone is likely to yield models that have limited reliability in flood hazard assessments, undermining their utility for regional and future change assessments. We underscore that such assessments can be improved by developing flood-focused, multi-objective, and spatial calibration metrics, by improving flood generating process representation through model structure comparisons and by considering uncertainty in precipitation input.

Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

Ice-jam floods pose a serious threat to many riverside communities in cold regions. Ice-jam-related flooding can cause loss of human life, millions of dollars in property damage, and adverse impacts on ecology. An effective flood management strategy is necessary to reduce the overall risk in flood-prone areas. Most of these strategies require a detailed risk-based management study to assess their effectiveness in reducing flood risk. Zoning regulation is a sustainable measure to reduce overall flood risk for a flood-prone area. Zoning regulation is a specified area in a floodplain where certain restrictions apply to different land uses (e.g., development or business). A stochastic framework was introduced to evaluate the effectiveness of a potential zoning regulation. A stochastic framework encompasses the impacts of all the possible expected floods instead of a more traditional approach where a single design flood is incorporated. The downtown area of Fort McMurray along the Athabasca River was selected to explore the impact of zoning regulation on reducing expected annual damages (EAD) from ice-jam flooding. The results show that a hypothetical zoning regulation for a certain area in the town of Fort McMurray (TFM) can be effective in substantially reducing the level of EAD. A global sensitivity analysis was also applied to understand the impacts of model inputs on ice-jam flood risk using a regional sensitivity method. The results show that model boundary conditions such as river discharge, the inflowing volume of ice and ice-jam toe locations are highly sensitive to ice-jam flood risk.

River ice is an important hydraulic and hydrological component of many rivers in the high northern latitudes of the world. It controls the hydraulic characteristics of streamflow, affects the geomorphology of channels, and can cause flooding due to ice-jam formation during ice-cover freeze-up and breakup periods. In recent decades, climate change has considerably altered ice regimes, affecting the severity of ice-jam flooding. Although many approaches have been developed to model river ice regimes and the severity of ice-jam flooding, appropriate methods that account for the impacts of future climate on ice-jam flooding have not been well established. Therefore, the main goals of this study are to review current knowledge regarding climate change impacts on river ice processes and to assess current modelling capabilities to determine the severity of ice jams under future climatic conditions. Finally, a conceptual river ice-jam modelling approach is presented for incorporating climate change impacts on ice jams.

• Most conceptual bucket models have an upper limit on simulated soil moisture deficit. • Problems arise when the bucket “empties” because ET drops to unrealistic (low) levels. • Alternatives include bottomless buckets or deficit-based soil moisture accounting. • Here, we switch to a deficit-based scheme while keeping everything else constant. • Tested over historic drought, model performance and realism are enhanced. Rainfall-runoff models based on conceptual “buckets” are frequently used in climate change impact studies to provide runoff projections. When these buckets approach empty, the simulated evapotranspiration approaches zero, which places an implicit limit on the soil moisture deficit that can accrue within the model. Such models may cease to properly track the moisture deficit accumulating in reality as dry conditions continue, leading to overestimation of subsequent runoff and possible long-term bias under drying climate. Here, we suggest that model realism may be improved through alternatives which remove the upper limit on simulated soil moisture deficit, such as “bottomless” buckets or deficit-based soil moisture accounting. While some existing models incorporate such measures, no study until now has systematically assessed their impact on model realism under drying climate. Here, we alter a common bucket model by changing the soil moisture storage to a deficit accounting system in such a way as to remove the upper limit on simulated soil moisture deficit. Tested on 38 Australian catchments, the altered model is better able to track the decline in soil moisture at the end of seasonal dry periods, which leads to superior performance over varied historic climate, including the 13-year “Millennium” drought. However, groundwater and GRACE data reveal long-term trends that are not matched in simulations, indicating that further changes may be required. Nonetheless, the results suggest that a broader adoption of bottomless buckets and/or deficit accounting within conceptual rainfall runoff models may improve the realism of runoff projections under drying climate.

Models that mimic an original model might have a different model structure than the original model, that affects model output. This study assesses model structure differences and their impact on output by comparing 7 model implementations that carry the name HBV. We explain and quantify output differences with individual model structure components at both the numerical (e.g., explicit/implicit scheme) and mathematical level (e.g., lineair/power outflow). It was found that none of the numerical and mathematical formulations of the mimicking models were (originally) the same as the benchmark, HBV-light. This led to small but distinct output differences in simulated streamflow for different numerical implementations (KGE difference up to 0.15), and major output differences due to mathematical differences (KGE median loss of 0.27). These differences decreased after calibrating the individual models to the simulated streamflow of the benchmark model. We argue that the lack of systematic model naming has led to a diverging concept of the HBV-model, diminishing the concept of model mimicry. Development of a systematic model naming framework, open accessible model code and more elaborate model descriptions are suggested to enhance model mimicry and model development.

Descriptions of runoff generation processes continue to grow, helping to reveal complexities and hydrologic behavior across a wide range of environments and scales. But to date, there has been little grouping of these process facts. Here, we discuss how the “fill‐and‐spill” concept can provide a framework to group event‐based runoff generation processes. The fill‐and‐spill concept describes where vertical and lateral additions of water to a landscape unit are placed into storage (the fill)—and only when this storage reaches a critical level (the spill), and other storages are filled and become connected, does a previously infeasible (but subsequently important) outflow pathway become activated. We show that fill‐and‐spill can be observed at a range of scales and propose that future fieldwork should first define the scale of interest and then evaluate what is filling‐and‐spilling at that scale. Such an approach may be helpful for those instrumenting and modeling new hillslopes or catchments because it provides a structured way to develop perceptual models for runoff generation and to group behaviors at different sites and scales.

Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on approximate numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation like that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation events. In this experiment, a large number of hydrographs is generated with the modular modeling framework FUSE, using eight numerical techniques across a variety of forcing datasets. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational expense and numerical error associated with each hydrograph were recorded. It was found that numerical error (root mean square error) usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both low numerical error and low computational cost. A basic literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be sub-optimal. We conclude that relatively large numerical errors might be common in current models, and because these will likely become larger as the climate changes, we advocate for the use of low cost, low error numerical methods.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.

Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

High-latitude cold regions are warming more than twice as fast as the rest of the planet, with the greatest warming occurring during the winter. Warmer winters are associated with shorter periods of snow cover, resulting in more frequent and extensive soil freezing and thawing. Freeze-thaw cycles influence soil chemical, biological, and physical properties and any changes to winter soil processes may impact carbon and nutrients export from affected soils, possibly altering soil health and nearby water quality. These impacts are relevant for agricultural soils and practices in cold regions as they are critical in governing water flows and quality within agroecosystems. In this study, a soil column experiment was conducted to assess the leaching of nutrients from fertilized agricultural soil during the non-growing season. Four soil columns were exposed to a non-growing season temperature and precipitation model and fertilizer amendments were made to two of the columns to determine the efficacy of fall-applied fertilizers and compared to other two unfertilized control columns. Leachates from the soil columns were collected and analyzed for cations and anions. The experiment results showed that a transition from a freeze period to a thaw period resulted in significant loss of chloride (Cl-), sulfate (SO42-) and nitrate (NO3-). Even with low NO3- concentrations in the applied artificial rainwater and fertilizer, high NO3- concentrations (~150 mg l-1) were observed in fertilized column leachates. Simple plug flow reactor model results indicate the high NO3- leachates are found to be due to active nitrification occurring in the upper oxidized portion of the soil columns mimicking overwinter NO3- losses via nitrification in agricultural fields. The low NO3- leachates in unfertilized columns suggest that freeze-thaw cycling had little effect on N mineralization in soil. Findings from this study will ultimately be used to bolster winter soil biogeochemical models by elucidating nutrient fluxes over changing winter conditions to refine best management practices for fertilizer application.

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.

Abstract. Increasing hydrologic variability, accelerating population growth, and resurgence of water resources development projects have all indicated increasing tensions among the riparian countries of transboundary rivers. This article aims to review the existing knowledge on conflict and cooperation in transboundary rivers from a multidisciplinary perspective and propose a socio-hydrological framework that integrates the slow and less visible societal processes with existing hydrological-economic models, revealing the hidden feedbacks between changes in societal processes and hydrological changes. This framework contributes to understanding the mechanism that drives conflict and cooperation in transboundary river management.

Stream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream, and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. A tracer test was carried out to estimate hyporheic exchange along the study reach. This study presents a unique example of an indirectly lake-headed stream i.e., where the interaction of groundwater and lake water, and the hydraulic gradient determine the resulting stream temperature. Energy balance of the stream is mainly controlled by radiation. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. Hyporheic exchange is present but plays only a minor role in the energy balance. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.

Abstract The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed millions of lives to date. Antigenic drift has resulted in viral variants with putatively greater transmissibility, virulence, or both. Early and near real-time detection of these variants of concern (VOC) and the ability to accurately follow their incidence and prevalence in communities is wanting. Wastewater-based epidemiology (WBE), which uses nucleic acid amplification tests to detect viral fragments, is a faithful proxy of COVID-19 incidence and prevalence, and thus offers the potential to monitor VOC viral load in a given population. Here, we describe and validate a primer extension PCR strategy targeting a signature mutation in the N gene of SARS-CoV-2. This allows quantification of the proportional expression of B.1.1.7 versus non-B.1.1.7 alleles in wastewater without the need to employ quantitative RT-PCR standard curves. We show that the wastewater B.1.1.7 profile correlates with its clinical counterpart and benefits from a near real-time and facile data collection and reporting pipeline. This assay can be quickly implemented within a current SARS-CoV-2 WBE framework with minimal cost; allowing early and contemporaneous estimates of B.1.1.7 community transmission prior to, or in lieu of, clinical screening and identification. Our study demonstrates that this strategy can provide public health units with an additional and much needed tool to rapidly triangulate VOC incidence/prevalence with high sensitivity and lineage specificity.

Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).

Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Abstract. The Arctic is warming at two to three times the rate of the global average, significantly impacting snow accumulation and melt. Unfortunately, conventional methods to measure snow water equivalent (SWE), a key aspect of the Arctic snow cover, have numerous limitations that hinder our ability to document annual cycles, the impact of climate change, or to test predictive models. As a result, there is an urgent need for improved methods that measure Arctic SWE; allow for continuous, unmanned measurements over the entire winter; and allow measurements that are representative of spatially variable, Arctic snow covers. In-situ, or invasive, cosmic ray neutron sensors (CRNSs) may fill this observational gap, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/17 and 2017/18 we tested an in-situ CRNS system at two locations in Canada; a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in Southern Ontario that allowed easier access for validation purposes. CRNS moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. Pearson correlation coefficients ranged from −0.89 to −0.98, while regression analyses provided R2 values from 0.79 to 0.96. RMSE of the CRNS-measured SWE averaged 2 mm at the southern Ontario site and ranged from 28 to 40 mm at the Arctic site. These data show that in-situ CRNS instruments are able to continuously measure SWE with sufficient accuracy, and have important applications for measuring SWE in a variety of environments, including remote Arctic locations. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote-sensing applications.

Abstract Ice jam floods (IJF) are a major concern for many riverine communities, government and non-government authorities and companies in the higher latitudes of the northern hemisphere. Ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Ice jam flood forecasting is challenging as its formation mechanism is chaotic and depends on numerous unpredictable hydraulic and river ice factors. In this study, Modélisation environnementale communautaire – surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast, an important boundary condition for any modelling of river ice-jam flood forecasting. A stochastic modelling approach was then applied to simulate hundreds of possible ice-jam scenarios using the hydrodynamic river ice model RIVICE within a Monte-Carlo Analysis (MOCA) framework for the Saint John River from Fort Kent to Grand Falls. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season.

Abstract Changes in land surface albedo and land surface evaporation modulate the atmospheric energy budget by changing temperatures, water vapor, clouds, snow and ice cover, and the partitioning of surface energy fluxes. Here idealized perturbations to land surface properties are imposed in a global model to understand how such forcings drive shifts in zonal mean atmospheric energy transport and zonal mean tropical precipitation. For a uniform decrease in global land albedo, the albedo forcing and a positive water vapor feedback contribute roughly equally to increased energy absorption at the top of the atmosphere (TOA), while radiative changes due to the temperature and cloud cover response provide a negative feedback and energy loss at TOA. Decreasing land albedo causes a northwards shift in the zonal mean intertropical convergence zone (ITCZ). The combined effects on ITCZ location of all atmospheric feedbacks roughly cancel for the albedo forcing; the total ITCZ shift is comparable to that predicted for the albedo forcing alone. For an imposed increase in evaporative resistance that reduces land evaporation, low cloud cover decreases in the northern mid-latitudes and more energy is absorbed at TOA there; longwave loss due to warming provides a negative feedback on the TOA energy balance and ITCZ shift. Imposed changes in land albedo and evaporative resistance modulate fundamentally different aspects of the surface energy budget. However, the pattern of TOA radiation changes due to the water vapor and air temperature responses are highly correlated for these two forcings because both forcings lead to near-surface warming.

Abstract Motivated by the hemispheric asymmetry of land distribution on Earth, we explore the climate of Northland, a highly idealized planet with a Northern Hemisphere continent and a Southern Hemisphere ocean. The climate of Northland can be separated into four distinct regions: the Southern Hemisphere ocean, the seasonally wet tropics, the midlatitude desert, and the Great Northern Swamp. We evaluate how modifying land surface properties on Northland drives changes in temperatures, precipitation patterns, the global energy budget, and atmospheric dynamics. We observe a surprising response to changes in land surface evaporation, where suppressing terrestrial evaporation in Northland cools both land and ocean. In previous studies, suppressing terrestrial evaporation has been found to lead to local warming by reducing latent cooling of the land surface. However, reduced evaporation can also decrease atmospheric water vapor, reducing the strength of the greenhouse effect and leading to large-scale cooling. We use a set of idealized climate model simulations to show that suppressing terrestrial evaporation over Northern Hemisphere continents of varying size can lead to either warming or cooling of the land surface, depending on which of these competing effects dominates. We find that a combination of total land area and contiguous continent size controls the balance between local warming from reduced latent heat flux and large-scale cooling from reduced atmospheric water vapor. Finally, we demonstrate how terrestrial heat capacity, albedo, and evaporation all modulate the location of the ITCZ both over the continent and over the ocean.

Drylines are atmospheric boundaries separating dry from moist air that can initiate convection. Potential changes in the location, frequency, and characteristics of drylines in future climates are unknown. This study applies a multi-parametric algorithm to objectively identify and characterize the dryline in North America using convection-permitting regional climate model simulations with 4-km horizontal grid spacing for 13-years under a historical and a pseudo-global warming climate projection by the end of the century. The dryline identification is successfully achieved with a set of standardized algorithm parameters across the lee side of the Rocky Mountains from the Canadian Rockies to the Sierra Madres in Mexico. The dryline is present 27% of the days at 00 UTC between April and September in the current climate, with a mean humidity gradient magnitude of 0.16 g−1 kg−1 km−1. The seasonal cycle of drylines peak around April and May in the southern Plains, and in June and July in the northern Plains. In the future climate, the magnitude and frequency of drylines increase 5% and 13%, correspondingly, with a stronger intensification southward. Future drylines strengthen during their peak intensity in the afternoon in the Southern U.S. and Northeast Mexico. Drylines also show increasing intensities in the morning with future magnitudes that are comparable to peak intensities found in the afternoon in the historical climate. Furthermore, an extension of the seasonality of intense drylines could produce end-of-summer drylines that are as strong as mid-summer drylines in the current climate. This might affect the seasonality and the diurnal cycle of convective activity in future climates, challenging weather forecasting and agricultural planning.

A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modeled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this study is twofold: (1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and (2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation such as that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation. In this experiment, a large number of hydrographs are generated with the modular modeling framework FUSE (Framework for Understanding Structural Errors), using eight numerical techniques across a variety of forcing data sets. All constructed models are conceptual and lumped. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational cost and numerical error associated with each hydrograph were recorded. Numerical error is assessed via root mean square error and normalized root mean square error. It was found that the root mean square error usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both a small numerical error and a low computational cost. A small literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be suboptimal. We conclude that relatively large numerical errors may be common in current models, highlighting the need for robust numerical techniques, in particular in the face of increasing precipitation extremes.

ABSTRACT One of the most prominent sources of error and uncertainty in water quality modelling results is the input data. In this study, data from three meteorological databases were used to test the performance of a water temperature model of Lake Diefenbaker: the data from Environment and Climate Change Canada (ECCC) had long-term quality control history (>20 years); the data from the AccuWeather had short-term quality control history (<10 years), and the data from the MeteoBlue database were modelled values. The CE-QUAL-W2 hydrodynamic and water quality model was used for this study. The model was calibrated by adjusting model coefficients controlling the amounts of measured solar radiation and wind that reach the surface of the water. The sensitivity results showed very similar performances, with slightly better performances (root mean square root difference of ± 0.1) with the ECCC data followed by the MeteoBlue data and thereafter by the AccuWeather data.

Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.