Abstract Arctic wetlands are known methane (CH 4 ) emitters but recent studies suggest that the Arctic CH 4 sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH 4 using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH 4 occurred at all sites at rates of 0.092 ± 0.011 mgCH 4 m −2 h −1 (mean ± s.e.), CH 4 uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH 4 uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH 4 uptake by Arctic soils, providing a negative feedback to global climate change.

Long-term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.

Abstract. Thermokarst lake water balances are becoming increasingly vulnerable to change in the Arctic as air temperature increases and precipitation patterns shift. In the tundra uplands east of the Mackenzie Delta in the Northwest Territories, Canada, previous research has found that lakes responded non-uniformly to changes in precipitation, suggesting that lake and watershed properties moderate the response of lakes to climate change. To investigate how lake and watershed properties and meteoro5 logical conditions influence the water balance of thermokarst lakes in this region, we sampled 25 lakes for isotope analysis five times in 2018, beginning before snowmelt on May 1 and ending on September 3. Water isotope data were used to calculate the ratio of evaporation-to-inflow (E/I) and the average isotope composition of lake source water (δI). We identified four distinct water balance phases as lakes responded to seasonal shifts in meteorological conditions and hydrological processes. During the freshet phase from May 1 to June 15, the median E/I ratio of lakes decreased from 0.20 to 0.13 in response to freshet runoff 10 and limited evaporation due to lake ice presence that persisted for the duration of this phase. During the following warm, dry, and ice-free period from June 15 to July 26, designated the evaporation phase, the median E/I ratio increased to 0.19. During the brief soil wetting phase, E/I ratios did not respond to rainfall between July 26 and August 2, likely because watershed soils absorbed most of the precipitation which resulted in minimal runoff to lakes. The median E/I ratio decreased to 0.11 after an unseasonably cool and rainy August, identified as the recharge phase. Throughout the sampling period, δI remained relatively 15 stable and most lakes contained a greater amount of rainfall-sourced water than snow-sourced water, even after the freshet phase due to snowmelt bypass. The range of average E/I ratios we observed at lakes (0.00–0.43) was relatively narrow and low compared to thermokarst lakes in other regions, likely owing to the large watershed area to lake area (WA/LA), efficient preferential flow pathways for runoff, and a shorter ice-free season. WA/LA strongly predicted average lake E/I ratio (R2 = 0.74), as lakes with smaller WA/LA tended to have higher E/I ratios because they received relatively less inflow. We used this 20 relationship to predict the average E/I ratio of 7340 lakes in the region, finding that lakes are not vulnerable to desiccation in this region, given that all predicted average E/I values were <0.33. If future permafrost thaw and warming cause less runoff to flow into lakes, we expect that lakes with smaller WA/LA will be more influenced by increasing evaporation, while lakes with larger WA/LA will be more resistant to lake-level drawdown. However under wetter conditions, lakes with larger WA/LA will likely experience greater increases in lake level and could be more susceptible to rapid drainage as a result.

Abstract. Thermokarst lake water balances are becoming increasingly vulnerable to change in the Arctic as air temperature increases and precipitation patterns shift. In the tundra uplands east of the Mackenzie Delta in the Northwest Territories, Canada, previous research has found that lakes responded non-uniformly to year-to-year changes in precipitation, suggesting that lake and watershed properties mediate the response of lakes to climate change. To investigate how lake and watershed properties and meteorological conditions influence the water balance of thermokarst lakes in this region, we sampled 25 lakes for isotope analysis five times in 2018, beginning before snowmelt on 1 May and sampling throughout the remainder of the ice-free season. Water isotope data were used to calculate the average isotope composition of lake source water (δI) and the ratio of evaporation to inflow (E/I). We identified four distinct water balance phases as lakes responded to seasonal shifts in meteorological conditions and hydrological processes. During the freshet phase from 1 May to 15 June, the median E/I ratio of lakes decreased from 0.20 to 0.13 in response to freshet runoff and limited evaporation due to lake ice presence that persisted for the duration of this phase. During the following warm, dry, and ice-free period from 15 June to 26 July, designated the evaporation phase, the median E/I ratio increased to 0.19. During the brief soil wetting phase, E/I ratios did not respond to rainfall between 26 July and 2 August, likely because watershed soils absorbed most of the precipitation which resulted in minimal runoff to lakes. The median E/I ratio decreased to 0.11 after a cool and rainy August, identified as the recharge phase. Throughout the sampling period, δI remained relatively stable and most lakes contained a greater amount of rainfall-sourced water than snow-sourced water, even after the freshet phase, due to snowmelt bypass. The range of average E/I ratios that we observed at lakes (0.00–0.43) was relatively narrow and low compared with thermokarst lakes in other regions, likely owing to the large ratio of watershed area to lake area (WA/LA), efficient preferential flow pathways for runoff, and a shorter ice-free season. Lakes with smaller WA/LA tended to have higher E/I ratios (R2 = 0.74). An empirical relationship between WA/LA and E/I was derived and used to predict the average E/I ratio of 7340 lakes in the region, which identified that these lakes are not vulnerable to desiccation, given that E/I ratios were < 0.33. If future permafrost thaw and warming cause less runoff to flow into lakes, we expect that lakes with a smaller WA/LA will be more influenced by increasing evaporation, while lakes with a larger WA/LA will be more resistant to lake-level drawdown. However under wetter conditions, lakes with a larger WA/LA will likely experience a greater increases in lake level and could be more susceptible to rapid drainage.

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.

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 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.

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, 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. 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. Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded 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. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. 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. The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society yet limited by lack of understanding of changes in cold-region process responses and interactions, along with their representation in most current-generation land-surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold-region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold-region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late 21st century.

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UAS SD ) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UAS SD was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion of factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.

Aufeis, also known as an icing or naled, is an accumulation of ice that forms primarily during winter when water is expelled onto frozen ground or ice surfaces and freezes in layers. Process‐oriented aufeis research initially expanded in the 20th century, but recent interest in changing hydrological conditions in permafrost regions has rejuvenated this field. Despite its societal relevance, the controls on aufeis distribution and dynamics are not well defined and this impedes projections of variation in aufeis size and distribution expected to accompany climate change. This paper reviews the physical controls on aufeis development, current broad‐scale aufeis distribution and anticipated change, and approaches to aufeis investigation. We propose an adjustment to terminology to better distinguish between the formation process and resulting ice bodies, a clarification of the aufeis classification approach based on source water, and a size threshold for broad‐scale aufeis inventory to facilitate collaborative research. We identify additional objectives for future research including advancing process knowledge at fine spatial scales, describing broad‐scale distribution using current remote sensing capabilities, and improving our understanding and predictive capacity over the interactions between aufeis and landscape‐scale permafrost, hydrogeological, geotectonic, and climate conditions.

Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests - the dominant boreal forest type - and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining. © 2020 The Author(s). Published by IOP Publishing Ltd. (Less)

This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence.

Thaw slumps in ice‐rich permafrost can retreat tens of metres per summer, driven by the melt of subaerially exposed ground ice. However, some slumps retain an ice‐veneering debris cover as they retreat. A quantitative understanding of the thermal regime and geomorphic evolution of debris‐covered slumps in a warming climate is largely lacking. To characterize the thermal regime, we instrumented four debris‐covered slumps in the Canadian Low Arctic and developed a numerical conduction‐based model. The observed surface temperatures 20°C and steep thermal gradients indicate that debris insulates the ice by shifting the energy balance towards radiative and turbulent losses. After the model was calibrated and validated with field observations, it predicted sub‐debris ice melt to decrease four‐fold from 1.9 to 0.5 m as the thickness of the fine‐grained debris quadruples from 0.1 to 0.4 m. With warming temperatures, melt is predicted to increase most rapidly, in relative terms, for thick (~0.5‐1.0 m) debris covers. The morphology and evolution of the debris‐covered slumps were characterized using field and remote sensing observations, which revealed differences in association with morphology and debris composition. Two low‐angle slumps retreated continually despite their persistent fine‐grained debris covers. The observed elevation losses decreased from ~1.0 m/yr where debris thickness ~.2 m to 0.1 m/yr where thickness ~1.0 m. Conversely, a steep slump with a coarse‐grained debris veneer underwent short‐lived bursts of retreat, hinting at a complex interplay of positive and negative feedback processes. The insulative protection and behaviour of debris vary significantly with factors such as thickness, grain size and climate: debris thus exerts a fundamental, spatially variable influence on slump trajectories in a warming climate.

To advance monitoring of surface water resources, new remote sensing technologies including the forthcoming Surface Water and Ocean Topography (SWOT) satellite (expected launch 2022) and its experimental airborne prototype AirSWOT are being developed to repeatedly map water surface elevation (WSE) and slope (WSS) of the world’s rivers, lakes, and reservoirs. However, the vertical accuracies of these novel technologies are largely unverified; thus, standard and repeatable field procedures to validate remotely sensed WSE and WSS are needed. To that end, we designed, engineered, and operationalized a Water Surface Profiler (WaSP) system that efficiently and accurately surveys WSE and WSS in a variety of surface water environments using Global Navigation Satellite Systems (GNSS) time-averaged measurements with Precise Point Positioning corrections. Here, we present WaSP construction, deployment, and a data processing workflow. We demonstrate WaSP data collections from repeat field deployments in the North Saskatchewan River and three prairie pothole lakes near Saskatoon, Saskatchewan, Canada. We find that WaSP reproducibly measures WSE and WSS with vertical accuracies similar to standard field survey methods [WSE root mean squared difference (RMSD) ∼8 cm, WSS RMSD ∼1.3 cm/km] and that repeat WaSP deployments accurately quantify water level changes (RMSD ∼3 cm). Collectively, these results suggest that WaSP is an easily deployed, self-contained system with sufficient accuracy for validating the decimeter-level expected accuracies of SWOT and AirSWOT. We conclude by discussing the utility of WaSP for validating airborne and spaceborne WSE mappings, present 63 WaSP in situ lake WSE measurements collected in support of NASA’s Arctic-Boreal and Vulnerability Experiment, highlight routine deployment in support of the Lake Observation by Citizen Scientists and Satellites project, and explore WaSP utility for validating a novel GNSS interferometric reflectometry LArge Wave Warning System.

Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.

Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2 ) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010-2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon-climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes.

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

The overall spatial and temporal influence of shrub expansion on permafrost is largely unknown due to uncertainty in estimating the magnitude of many counteracting processes. For example, shrubs shade the ground during the snow-free season, which can reduce active layer thickness. At the same time, shrubs advance the timing of snowmelt when they protrude through the snow surface, thereby exposing the active layer to thawing earlier in spring. Here, we compare 3056 in situ frost table depth measurements split between mineral earth hummocks and organic inter-hummock zones across four dominant shrub–tundra vegetation types. Snow-free date, snow depth, hummock development, topography, and vegetation cover were compared to frost table depth measurements using a structural equation modeling approach that quantifies the direct and combined interacting influence of these variables. Areas of birch shrubs became snow free earlier regardless of snow depth or hillslope aspect because they protruded through the snow surface, leading to deeper hummock frost table depths. Projected increases in shrub height and extent combined with projected decreases in snowfall would lead to increased shrub protrusion across the Arctic, potentially deepening the active layer in areas where shrub protrusion advances the snow-free date.

Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.

Use of isotopes to quantify the temporal dynamics of the transformation of precipitation into run-off has revealed fundamental new insights into catchment flow paths and mixing processes that influence biogeochemical transport. However, catchments underlain by permafrost have received little attention in isotope-based studies, despite their global importance in terms of rapid environmental change. These high-latitude regions offer limited access for data collection during critical periods (e.g., early phases of snowmelt). Additionally, spatio-temporal variable freeze-thaw cycles, together with the development of an active layer, have a time variant influence on catchment hydrology. All of these characteristics make the application of traditional transit time estimation approaches challenging. We describe an isotope-based study undertaken to provide a preliminary assessment of travel times at Siksik Creek in the western Canadian Arctic. We adopted a model-data fusion approach to estimate the volumes and isotopic characteristics of snowpack and meltwater. Using samples collected in the spring/summer, we characterize the isotopic composition of summer rainfall, melt from snow, soil water, and stream water. In addition, soil moisture dynamics and the temporal evolution of the active layer profile were monitored. First approximations of transit times were estimated for soil and streamwater compositions using lumped convolution integral models and temporally variable inputs including snowmelt, ice thaw, and summer rainfall. Comparing transit time estimates using a variety of inputs revealed that transit time was best estimated using all available inflows (i.e., snowmelt, soil ice thaw, and rainfall). Early spring transit times were short, dominated by snowmelt and soil ice thaw and limited catchment storage when soils are predominantly frozen. However, significant and increasing mixing with water in the active layer during the summer resulted in more damped steam water variation and longer mean travel times (~1.5 years). The study has also highlighted key data needs to better constrain travel time estimates in permafrost catchments.

Abstract A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.