This study is a meta-analysis of global articles on hydrological nutrient dynamics to determine trends and consensus on: (1) the effects of climate change-induced hydrological and temperature drivers on nutrient dynamics and how these effects vary along the catchment continuum from land to river to lake; (2) the convergence of climate change impacts with other anthropogenic pressures (agriculture, urbanization) in nutrient dynamics; and (3) regional variability in the effects of climate change on nutrient dynamics and water-quality impairment across different climate zones. An innovative web crawler tool was employed to help critically synthesize the information in the literature. The literature suggests that climate change will impact nutrient dynamics around the globe and exacerbate contemporary water-quality challenges. Nutrient leaching and overland flow transport are projected to increase globally, promoted by extreme precipitation. Seasonal variations in streamflow are expected to emulate changing precipitation patterns, but the specific local impacts of climate change on hydrology and nutrient dynamics will vary both seasonally and regionally. Plant activity may reduce some of this load in nonagricultural soils if the expected increase in plant uptake of nutrients prompted by increased temperatures can compensate for greater nitrogen (N) and phosphorus (P) mineralization, N deposition, and leaching rates. High-temperature forest and grass fires may help reduce mineralization and microbial turnover by altering N speciation via the pyrolysis of organic matter. In agricultural areas that are at higher risk of erosion, extreme precipitation will exacerbate existing water-quality issues, and greater plant nutrient uptake may lead to an increase in fertilizer use. Future urban expansion will amplify these effects. Higher ambient temperatures will promote harmful cyanobacterial blooms by enhancing thermal stratification, increasing nutrient load into streams and lakes from extreme precipitation events, decreasing summer flow and thus baseflow dilution capacity, and increasing water and nutrient residence times during increasingly frequent droughts. Land management decisions must consider the nuanced regional and seasonal changes identified in this review (realized and predicted). Such knowledge is critical to increasing international cooperation and accelerating action toward the United Nations’s global sustainability goals and the specific objectives of the Conference of Parties (COP) 26.
Abstract. Systematic tile drainage is used extensively in agricultural lands to remove excess water and improve crop growth; however, tiles can also transfer nutrients from farmlands to downstream surface water bodies, leading to water quality problems. There is a need to simulate the hydrological behaviour of tile drains to understand the impacts of climate or land management change on agricultural runoff. The Cold Regions Hydrological Model (CRHM) is a physically based, modular modelling system that enables the creation of comprehensive models appropriate for cold regions by including a full suite of winter, spring, and summer season processes and coupling these together via mass and energy balances. A new tile drainage module was developed for CRHM to account for this process in tile-drained landscapes that are increasingly common in cultivated basins of the Great Lakes and northern Prairies regions of North America. A robust multi-variable, multi-criteria model performance evaluation strategy was deployed to examine the ability of the module with CRHM to capture tile discharge under both winter and summer conditions. Results showed that soil moisture is largely regulated by tile flow and lateral flow from adjacent fields. The explicit representation of capillary rise for moisture interactions between the rooting zone and groundwater greatly improved model simulations, demonstrating its significance in the hydrology of tile drains in loam soils. Water level patterns revealed a bimodal behaviour that depended on the positioning of the capillary fringe relative to the tile. A novel aspect of this module is the use of field capacity and its corresponding pressure head to provide an estimate of drainable water and thickness of the capillary fringe, rather than a detailed soil retention curve that may not always be available. Understanding the bimodal nature of soil water levels provides better insight into the significance of dynamic water exchange between soil layers below drains to improve tile drainage representation in models.
The cold regions hydrological modelling platform for hydrological diagnosis and prediction based on process understanding
John W. Pomeroy,
Thomas A. Brown,
Christopher B. Marsh,
Sebastian A. Krogh,
Holly J. Annand,
Journal of Hydrology, Volume 615
• 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.
Influence of climate, topography, and soil type on soil extractable phosphorus in croplands of northern glacial‐derived landscapes
Janina M. Plach,
Merrin L. Macrae,
Henry F. Wilson,
David A. Lobb,
Kevin W. King
Journal of Environmental Quality, Volume 51, Issue 4
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.
• Application of popular catchment nutrient models is problematic in cold regions. • New nutrient modules have been developed for the Cold Regions Hydrological Model. • The model was applied to a sub-basin of the increasingly eutrophic Lake Winnipeg, Canada. • Simulated SWE, discharge, NO3, NH4, SRP and partP were compared against observations. • Typical ∼9 day-freshet accounted for 16–31% of the total annual nutrient load. Excess nutrients in aquatic ecosystems is a major water quality problem globally. Worsening eutrophication issues are notable in cold temperate areas, with pervasive problems in many agriculturally dominated catchments. Predicting nutrient export to rivers and lakes is particularly difficult in cold agricultural environments because of challenges in modelling snow, soil, frozen ground, climate, and anthropogenic controls. Previous research has shown that the use of many popular small basin nutrient models can be problematic in cold regions due to poor representation of cold region hydrology. In this study, the Cold Regions Hydrological Modelling Platform (CRHM), a modular modelling system, which has been widely deployed across Canada and cold regions worldwide, was used to address this problem. CRHM was extended to simulate biogeochemical and transport processes for nitrogen and phosphorus through a complex of new process-based modules that represent physicochemical processes in snow, soil and freshwater. Agricultural practices such as tillage and fertilizer application, which strongly impact the availability and release of soil nutrients, can be explicitly represented in the model. A test case in an agricultural basin draining towards Lake Winnipeg shows that the model can capture the extreme hydrology and nutrient load variability of small agricultural basins at hourly time steps. It was demonstrated that fine temporal resolutions are an essential modelling requisite to capture strong concentration changes in agricultural tributaries in cold agricultural environments. Within these ephemeral and intermittent streams, on average, 30%, 31%, 20%, and 16% of the total annual load of nitrate (NO 3 ), ammonium (NH 4 ), soluble reactive phosphorus (SRP), and particulate phosphorous (partP)NO 3 , NH 4 , SRP and partP occurred during the episodic snowmelt freshet ( ∼ 9 days, accounting for 21% of the annual flow), but shows extreme temporal variation. The new nutrient modules are critical tools for predicting nutrient export from small agricultural drainage basins in cold climates via better representation of key hydrological processes, and a temporal resolution more suited to capture dynamics of ephemeral and intermittent streams.
Predicting Variable Contributing Areas, Hydrological Connectivity, and Solute Transport Pathways for a Canadian Prairie Basin
J. M. Elliott,
Helen M. Baulch,
Henry F. Wilson,
John W. Pomeroy
Water Resources Research, Volume 56, Issue 12
In cold agricultural regions, seasonal snowmelt over frozen soils provides the primary source of runoff and transports large nutrient loads downstream. The postglacial landscape of the Canadian Prairies and Northern Plains of the United States creates challenges for hydrological and water quality modeling. Here, the application of conventional hydrological models is problematic because of cold regions hydrological and chemical processes, the lack of fluvially eroded drainage systems, large noncontributing areas to streamflow and level topography. A new hydrodynamic model was developed to diagnose overland flow from snowmelt in this situation. The model was used to calculate the effect of variable contributing areas on (1) hydrological connectivity and the development of (2) tipping points in streamflow generation and (3) predominant chemical transport pathways. The agricultural Steppler Basin in Manitoba, Canada, was used to evaluate the model and diagnose snowmelt runoff. Relationships were established between contributing area and (1) snowmelt runoff intensity, (2) seasonal snowmelt volumes and duration, and (3) inundated, active and connected areas. Variations in the contributing area depended on terrain and snowmelt characteristics including wind redistribution of snow. Predictors of hydrological response and the size of the contributing area were developed which can be used in larger scale hydrological models of similar regions
Abstract The hydrology of cold regions has been studied for decades with substantial progress in process understanding and prediction. Simultaneously, work on nutrient yields from agricultural land in cold regions has shown much slower progress. Advancement of nutrient modelling is constrained by well-documented issues of spatial heterogeneity, climate dependency, data limitations and over-parameterization of models, as well as challenges specific to cold regions due to the complex (and often unknown) behaviour of hydro-biogeochemical processes at temperatures close to and below freezing where a phase change occurs. This review is a critical discussion of these issues by taking a close look at the conceptual models and methods behind used catchment nutrient models. The impact of differences in model structure and the methods used for the prediction of hydrological processes, erosion and biogeochemical cycles are examined. The appropriateness of scale, scope, and complexity of models are discussed to propose future research directions.
The preferential elution of ions from melting snowpacks is a complex problem that has been linked to temporary acidification of water bodies. However, the understanding of these processes in snowpacks around the world, including the polar regions that are experiencing unprecedented warming and melting, remains limited despite being instrumental in supporting climate change adaptation. In this study, data collected from a snowmelt lysimeter and snowpits at meadow and forest-gap sites in a high elevation watershed in Colorado were combined with the PULSE multi-phase snowpack chemistry model to investigate the controls of meltwater chemistry and preferential elution. The snowdepth at the meadow site was 64% of that at the forest-gap site, and the snowmelt rate was greater there (meadow snowpit) due to higher solar irradiance. Cations such as Ca2+ and NH4+ were deposited mostly within the upper layers of both the meadow and forest-gap snowpacks, and acid anions such as NO3- and SO42- were more evenly distributed. The snow ion concentrations were generally greater at the forest-gap snowpit, except for NH4+, which indicates that wind erosion of wet and dry deposited ions from the meadow may have reduced concentrations of residual snow. Furthermore, at the forest-gap site, snow interception and scavenging processes such as sublimation, ventilation, and throughfall led to particular ion enrichment of Ca2+, Mg2+, K+, Cl-, SO42- and NO3-. Model simulations and observations highlight that preferential elution is enhanced by low snowmelt rates, with the model indicating that this is due to lower dilution rates and increased contact time and area between the percolating meltwater and the snow. Results suggest that low snowmelt rates can cause multiple early meltwater ionic pulses for ions subject to lower ion exclusion. Ion exclusion rates at the grain-size level have been estimated for the first time.
Abstract Freshwater ecosystems, particularly those in agricultural areas, remain at risk of eutrophication due to anthropogenic inputs of nutrients. While community-based monitoring has helped improve awareness and spur action to mitigate nutrient loads, monitoring is challenging due to the reliance on expensive laboratory technology, poor data management, time lags between measurement and availability of results, and risk of sample degradation during transport or storage. In this study, an easy-to-use smartphone-based application (The Nutrient App) was developed to estimate NO 3 and PO 4 concentrations through the image-processing of on-site qualitative colorimetric-based results obtained via cheap commercially-available instantaneous test kits. The app was tested in rivers, wetlands, and lakes across Canada and relative errors between 30% (filtered samples) and 70% (unfiltered samples) were obtained for both NO 3 and PO 4 . The app can be used to identify sources and hotspots of contamination, which can empower communities to take immediate remedial action to reduce nutrient pollution.
Reducing eutrophication in surface water is a major environmental challenge in many countries around the world. In cold Canadian prairie agricultural regions, part of the eutrophication challenge arises during spring snowmelt when a significant portion of the total annual nutrient export occurs, and plant residues can act as a nutrient source instead of a sink. Although the total mass of nutrients released from various crop residues has been studied before, little research has been conducted to capture fine-timescale temporal dynamics of nutrient leaching from plant residues, and the processes have not been represented in water quality models. In this study, we measured the dynamics of P and N release from a cold-hardy perennial plant species, alfalfa ( L.), to meltwater after freeze-thaw through a controlled snowmelt experiment. Various winter conditions were simulated by exposing alfalfa residues to different numbers of freeze-thaw cycles (FTCs) of uniform magnitude prior to snowmelt. The monitored P and N dynamics showed that most nutrients were released during the initial stages of snowmelt (first 5 h) and that the magnitude of nutrient release was affected by the number of FTCs. A threshold of five FTCs was identified for a greater nutrient release, with plant residue contributing between 0.29 (NO) and 9 (PO) times more nutrients than snow. The monitored temporal dynamics of nutrient release were used to develop the first process-based predictive model controlled by three potentially measurable parameters that can be integrated into catchment water quality models to improve nutrient transport simulations during snowmelt.
Seasonal snowcovers release nutrients accumulated over the winter during spring snowmelt and this can be an important part of the annual biogeochemical cycling of chemicals and their loading to soils and water bodies. The characteristics of this load are controlled by snowmelt dynamics and the physical and chemical properties of the snowpack, which are affected by overwinter and snowmelt metamorphism, refreezing of meltwater, and ion exclusion from snow crystals. Rain-on-snow (ROS) events can accelerate and modify the snowpack discharge process. The interplay of these processes can cause microscale flow heterogeneity and preferential flow pathways (PFP). Previous experimental work has examined PFP and ion elution processes in snowpacks, but their combined effect on the spatial and temporal characteristics of snowmelt ion elution remains uncertain. In this research, two controlled laboratory experiments were performed to investigate the role of PFP and ROS in controlling snow ion release to runoff. These involved the high frequency monitoring of flow and meltwater concentrations during snowmelt induced by radiation-convection (RC) processes and rain-on-snow (ROS). Results showed that when ROS was included, PFP was responsible for the transport of 68% and 73% of the total NO3 and PO4 load discharged during the early snowmelt phase recorded by the experiment. However, this initial load increased to 95% and 75% when ROS was removed, causing the release of more than 20% of the total snowpack NO3 and PO4 during the first 1.5% of melt. Small intensity ROS may refreeze in the snowpack, which may affect the ability of lateral flow to deliver snow ions located beyond the leading edge of PFP.
There is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO₃ but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO₃ run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.
Abstract Early ionic pulse during spring snowmelt can account for a significant portion of the total annual nutrient load in seasonally snow-covered areas. Ionic pulses are a consequence of snow grain core to surface ion segregation during metamorphism, a process commonly referred to as ion exclusion. While numerous studies have provided quantitative measurements of this phenomenon, very few process-based mathematical models have been proposed for diagnostic and prognostic investigations. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity. However, this process is represented in models in ways that misalign with the mechanistic view of the process described in the literature. In this research, a process-based model is proposed that can simulated ionic pulses in runoff by emulating solute leaching from snow grains during melt and the subsequent vertical solute transport by meltwater through the snowpack. To facilitate its use without the need for snow-physics’ models, simplified alternative methods are proposed to estimate some of the variables required by the model. The model was applied to two regions, and a total of 4 study sites, that are subject to significantly different winter climatic and hydrological conditions. Comparison between observations and simulation results suggest that the model can capture well the overall snow melt runoff concentration pattern, including both the timing and magnitude of the early melt ionic pulse. The model enables the prediction of concentration profiles of the dry (snow) and liquid (wet) fractions within the snow matrix for the first time. Although there is a computational cost associated with the proposed modelling framework, this study demonstrates that it can provide more detailed information about the reallocation and transport of ions through snowpacks, which can ultimately be used to improve nutrient transport predictions during snowmelt.
Modeling nutrient transport during snowmelt in cold regions remains a major scientific challenge. A key limitation of existing nutrient models for application in cold regions is the inadequate representation of snowmelt, including hydrological and biogeochemical processes. This brief period can account for more than 80% of the total annual surface runoff in the Canadian Prairies and Northern Canada and processes such as atmospheric deposition, over-winter redistribution of snow, ion exclusion from snow crystals, frozen soils, and snowcovered area depletion during melt influence the distribution and release of snow and soil nutrients, thus affecting the timing and magnitude of snowmelt runoff nutrient concentrations.Research in cold regions suggests that nitrate (NO3) runoff at the field scale can be divided into five phases during snowmelt. In the first phase, water and ions originating from ion-rich snow layers travel and diffuse through the snowpack. This process causes ion concentrations in runoff to gradually increase. The second phase occurs when this snow ion meltwater front has reached the bottom of the snowpack and forms runoff to the edge-of-the-field (EOF). During the third and fourth phases, the main source of NO3 transitions from the snowpack to the soil. Finally, the fifth and last phase occurs when the snow has completely melted, and the thawing soil becomes the main source of NO3 to the stream.In this research, a process-based model was developed to simulate hourly export based on this five-phase approach. Results from an application in the Red River Basin of southern Manitoba, Canada shows that the model can adequately capture the dynamics and rapid changes of NO3 concentrations during this period at relevant temporal resolutions. This is a significant achievement to advance the current nutrient modeling paradigm in cold climates, which is generally limited to satisfactory results at monthly or annual resolutions. The approach can inform catchment-scale nutrient models to improve simulation of this critical snowmelt period.Nutrient exports Winter Snow Nitrate Agriculture Nutrient model