Snow Accumulation Patterns Research Articles

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

What recent global atmospheric reanalyses and regional climate models can represent observed snow accumulation on Antarctica?

Recent compiled surface mass balance observations on the Antarctic ice sheet are used to examine the performance of four global reanalysis products and two regional climate models (RCMs), i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI), the fifth generation ECMWF reanalysis (ERA5), the Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), Modèle Atmosphérique Régional (MAR), and the Regional Atmospheric Climate Model (RACMO). All these models perform similarly well in reproducing spatial patterns of snow accumulation from the coast to inland plateau. But they differ in their magnitude with respective to in-situ observations with either evident overestimation (MAR) or underestimation (ERAI and CFSR) in the interior of the Antarctica. By comparison, MERRA2 and ERA5 at each elevation bin of 200 m match best with observations. Among the models, ERAI shows the smallest amount of snow accumulation averaged over the entire ice sheet. All models present some skills for reproducing the inter-annual variability in observed snow accumulation. By comparison, ERA5 probably provides the most realistic representation of variability in Antarctic snow accumulation since 1979.

Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada

Abstract. Roads constructed on permafrost can have a significant impact on the surrounding environment, potentially inducing permafrost degradation. These impacts arise from factors such as snow accumulation near the road, which affects the soil's thermal and hydrological regime, and road dust that decreases the snow's albedo, altering the timing of snowmelt. However, our current understanding of the magnitude and the spatial extent of these effects is limited. In this study we addressed this gap by using remote sensing techniques to assess the spatial effect of the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada, on snow accumulation, snow albedo and snowmelt patterns. With a new, high resolution snow depth raster from airborne laser scanning, we quantified the snow accumulation at road segments in the Trail Valley Creek area using digital elevation model differencing. We found increased snow accumulation up to 36 m from the road center. The magnitude of this snow accumulation was influenced by the prevailing wind direction and the embankment height. Furthermore, by analyzing 43 Sentinel-2 satellite images between February and May 2020, we observed reduced snow albedo values within 500 m of the road, resulting in a 12-days-earlier onset of snowmelt within 100 m from the road. We examined snowmelt patterns before, during and after the road construction using the normalized difference snow index from Landsat-7 and Landsat-8 imagery. Our analysis revealed that the road affected the snowmelt pattern up to 600 m from the road, even in areas which appeared undisturbed. In summary, our study improves our understanding of the spatial impact of gravel roads on permafrost due to enhanced snow accumulation, reduced snow albedo and earlier snowmelt. Our study underscores the important contribution that remote sensing can provide to improve our understanding of the effects of infrastructure development on permafrost environments.

Spatiotemporal dipole variations of spring snowmelt over Eurasia

Spring snowmelt in Eurasia plays an important role in the climate system and regional water resources. However, there is still a limited understanding of the spatiotemporal characteristics of spring snowmelt in Eurasia and the factors that drive its variations. This study utilized the GlobSnow v3.0 snow water equivalent (SWE) monthly bias-corrected data and defined the spring snowmelt as the difference between the spring peak SWE and the SWE in May, to investigate the spatiotemporal variations of spring snowmelt in mid- and high-latitudes of Eurasia from 1979 to 2018 and identify the factors influencing these changes. Our findings reveal a substantial increase in spring snowmelt over the past four decades, with an average annual increase of 5.76 gigatons (GT) of water equivalent across the entire study region. Approximately 64.71% of the mid-high latitudes of Eurasia exhibits a positive trend in spring snowmelt, and 22.49% of the area shows a statistically significant increase (p < 0.05). Spatially, a dipole pattern of spring snowmelt variation is observed, with an increase in the eastern regions and a decrease in the western regions. The decline in spring snowmelt in western Eurasia is attributed to a decrease in snow accumulation during the previous season, while the increase in spring snowmelt in the eastern high latitudes is driven by intensified spring melting. In the eastern mid-latitudes, the rise in spring snowmelt is associated with an increase in snow accumulation during the previous season. Furthermore, our study highlights the influence of near-surface air temperature, rainfall ratio, and total precipitation during both the spring and snow accumulation seasons on spring snowmelt variability. Our results elucidate the spatiotemporal characteristics of spring snowmelt and emphasize the role of climate factors in modulating snow accumulation and subsequent spring melting patterns in the mid- and high-latitudes of Eurasia.

Impacts of shrub removal on snow and near-surface thermal conditions in permafrost terrain adjacent to the Dempster Highway, NT, Canada

The Peel Plateau, NT, Canada, is an area underlain by warm continuous permafrost where changes in soil moisture, snow conditions, and shrub density have increased ground temperatures next to the Dempster Highway. In this study, ground temperatures, snow, and thaw depth were monitored before and after tall shrub removal (2014). A snow survey after tall shrub removal indicated that snow depth decreased by a third and lowered winter ground temperatures when compared with control tall shrub sites. The response of ground temperatures to shrub removal depended on soil type. The site with organic soils had cooler winter temperatures and no apparent change in summer temperatures following shrub removal. At sites with mineral soil, moderate winter ground cooling insufficiently counteracted increases in summer ground heat flux caused by canopy removal. Given the predominance of mineral soil along the Dempster, these observations suggest tall shrub removal is not a viable short-term permafrost management strategy. Additionally, the perpendicular orientation of the Highway to prevailing winter winds stimulates snow drift formation and predisposes the site to warmer permafrost temperatures, altered hydrology, and tall shrub proliferation. Subsequent research should explore the effectiveness of tall shrub removal at sites with colder winter conditions or different snow accumulation patterns.

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least 2 consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module named the Permafrost and Organic LayEr module for Forest Models (POLE-FM) that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61 000 ha) and over temporal scales of days to centuries. The coupled model generated intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) generally consistent with independent observations in 17 instrumented forest stands. The model also represented the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 39.3 % of forested area in the landscape as underlain by permafrost, compared to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision-making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model's utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar

Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.

Assessing the Karakoram Anomaly from long-term trends in earth observation and climate data

In contrast to the general trend of global glacier recession, several studies have reported stable or advancing glaciers in the sub-basins of the Karakoram – the so-called ‘Karakoram Anomaly’. Snow and glacier ice melt are important components of the hydrological system and represent a major water supply for the region. In the absence of reliable and comprehensive in situ measurements, Earth observation (EO) and remote sensing retrievals of snow water equivalent (SWE), water balance (WB) and hydro-meteorological variables can be used to infer changes in snow/glacier melting. We used linear regression and Mann-Kendall (MK) methods to assess trends in annual and seasonal variables derived from satellite, gridded and reanalysis datasets of the Global Land Data Assimilation System (GLDAS) and Terraclimate. The spatial and temporal pattern of snow accumulation and ablation varies across the study region. The spatial distribution of annual and winter SWE showed a significant (p < 0.05) positive trend in the western Karakoram. This snow accumulation may be attributed to a significant decrease in summertime maximum temperature (Tmax) in the western Karakoram. By contrast, in the eastern Karakoram, significant negative trends in annual WB indicate depletion of water storage. These results, using a different dataset and approach, are consistent with previous studies where glacier mass balances have been found to be stable or positive in the Karakoram, but become more negative further east and into the Himalaya. These changes in hydrology at highly glacierised catchments have considerable implications for water availability and supply to large downstream populations.

Spatial variability and post-depositional diffusion of stable isotopes in high accumulation regions of East Antarctica

Stable isotopes of Antarcticice cores are used extensively in reconstructing the past climate. Variations in snow accumulation patterns and post-depositional processes such as changes in isotope ratios due to diffusion may complicate these records. We analyzed the spatio-temporal variations of snow accumulation, stable isotopic composition, and factors controlling their distribution along two transects in the Dronning Maud Land (DML) and Princess Elizabeth Land (PEL) regions of East Antarctica. The δ18O and δD variations are dominantly influenced by snow accumulation in the DML region and temperature in the PEL region. To evaluate the extent of diffusion in isotope records, the isotope record of an ice core (IND-33) drilled after 5 years close to the DML transect was compared with the snow isotope records. A quantitative comparison of the snow core δ18O records with that of the ice core revealed signal attenuation ranging between 55 and 70%. Using a firn diffusion model, we estimated a diffusion length of 6 cm in IND-33 over 5 years. Our study revealed that while isotope diffusion exists even in high accumulation sites of coastal Antarctica, it does not significantly impact the dating and paleoclimatic interpretation of isotope records, unlike in low accumulation areas.

A Centimeter-Wavelength Snowfall Retrieval Algorithm Using Machine Learning

Abstract Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze–S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.

Snow-avalanche hazard assessment based on dendrogeomorphic reconstructions and classification tree algorithms for ski area development, Parâng Mountains, Romania

This paper explores the snow-avalanche regime based on tree-ring reconstructions and their triggering weather conditions with classification tree algorithms.The results show a significant increased frequency of avalanche events on Zăvoaie NE slope for the second half of the 20th century by comparison to the Scărița SW slope. The classification tree models highlight the weather conditions leading to avalanche release with three scenarios in each path. The first scenario underlines the wind's effect as a key weather variable on both slope aspects. The second scenario corresponds to a spring regime, while rain and warm temperatures are the main triggers. The third general weather condition favouring snow avalanche activity are persistent low temperatures and important snowfall throughout the winter season. However, this triggering condition was mainly found on the NE avalanche path, probably related to the pattern of snow accumulation, the prevailing winds, but above all the lower solar radiation which favours a slower and later melting of the snow cover. Finally, the return periods and runout distances calculated from tree ring analysis show a high risk for the location of the infrastructure planned for the ski area expansion, showing once again the usefulness of dendrogeomorphology in natural hazard assessment where historical data are lacking.

Inferring watershed‐scale mean snowfall magnitude and distribution using multidecadal snow reanalysis patterns and snow pillow observations

AbstractThe magnitude and spatial heterogeneity of snow are difficult to model in mountainous terrain. We investigated how historic snow patterns from a 32‐year (1985–2016) snow reanalysis, which uses the timing and rate of remotely‐sensed snow disappearance to reconstruct snow water equivalent in past years, could be used to improve current year simulations of snowfall in the Upper Tuolumne, Upper Kings, and Sagehen Creek watersheds in California's Sierra Nevada. Building on past research linking observations of fractional snow‐covered area (fSCA) to spatial patterns of snow water equivalent, fSCA maps were used to identify pairs of dates from historic years in the reanalysis with similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to extrapolate snow accumulation observed by snow pillows to 90 m snowfall fields. These 90 m snowfall fields were used as input for snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water‐year 2015, which had record low snow in the Sierra Nevada and unique snow patterns, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fSCA agreed with each other well, with mean absolute differences of approximately 10%. Domain mean winter snowfall inferred from the relationship between historic snow accumulation patterns and current year snow pillow observations had a ±13% interquartile range of biases, relative to the snow reanalysis. Finally, snow depth simulations using snow pillow observed snowfall extrapolated with historic snow accumulation patterns had 70% better coefficients of correlations, and 27% better mean absolute errors, as compared to simulations using more common snowfall forcing. This work demonstrates the benefits of repeatable snowfall patterns and satellite‐era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

Snow Contamination of Simplified Automotive Bluff Bodies: A Comparison Between Wind Tunnel Experiments and Numerical Modeling

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;We describe experiments and numerical modeling of snow surface contamination on two simplified automotive bluff bodies: The Ahmed body and a wedge. The purpose was twofold: 1) To obtain well defined experimental results of snow contamination on simple geometries; 2) To propose a numerical modeling approach for snow contamination. The experiments were performed in a climatic wind tunnel using a snow cannon at −15 °C and the results show that the snow accumulation depends on the aerodynamics of the studied bluff bodies. Snow accumulates on surfaces in proximity to the aerodynamic wakes of the bodies and characteristic snow patterns are obtained on side surfaces. The numerical modeling approach consisted of an aerodynamic setup coupled with Lagrangian particle tracking. Particles were determined to adhere or rebound depending on an adhesion model combined with a resuspension criterion. The adhesion model was based on adhesive-elastic contact theory and the resuspension criterion is derived from the balance between the aerodynamic forces acting on a particle and the critical force for onset of resuspension. The results show that the numerical method can predict certain characteristic snow patterns obtained from the experiments and we also highlight deviations obtained between experimental and simulation results. The simulation results show that the snow accumulation patterns on a bluff body will depend on the smallest ice particles in a snow sample which implies that samples with larger ice particle (for example natural snow) could produce different snow patterns than the fine machine-made snow used in this study.&lt;/div&gt;&lt;/div&gt;

Analysis of the influence of snow fences on snow redistribution under snow-drifting in railway cuttings

In order to reduce the amount of snow deposited in railway cuttings under the action of snow-drifting, this paper arranged three forms of snow fence and related wind and snow flow field monitoring equipment in the field test area, and the effect of different structural parameters of snow fences on snow redistribution was studied by wind tunnel test and numerical simulation. The results showed that there was an approximately 20-meter-long erosion area on the top of the slope, and the snow fences could deposit snow particles on both sides of the fences which reduced erosion on the top of the cutting slope. Compared with Single-3 and Single-5 fences, the amounts of deposited snow were increased by about 50% and 20%, respectively, under the action of Double-3 fences. When the snow was deposited in the cutting, it tended to accumulate on the leeward side of the snow deposition platform; and the snow amount on the subgrade surface rapidly increased when the snow platform reaches its carrying limit. The bottom gap of the snow fence accelerates the flow field near the ground and the snow particles extend to the leeward side, the height of the snow fence will affect the snow deposition range on the leeward side of the fence, and the porosity will affect the snow accumulation pattern of both sides of the fence.

Comparison of Curved Roofs of Various Configurations

Curved roofs of various configurations have been compared considering the snow load distribution pattern. The dependence of the roof slope, which is the determining factor affecting the snow accumulation, on the relative roof height has been determined. The pattern of snow accumulation on curved roofs with circumferential, square parabolic, sinusoidal, and elliptical shapes, most widespread in construction, have been considered. The snow deposition patterns on curved roofs of various shapes have been analyzed. Recommendations are given for choosing the most effective roof configurations, considering the snow load distribution.

Local-scale deposition of surface snow on the Greenland ice sheet

Abstract. Ice cores from polar ice sheets and glaciers are an important climate archive. Snow layers, consecutively deposited and buried, contain climatic information from the time of their formation. However, particularly low-accumulation areas are characterised by temporally intermittent precipitation, which can be further redistributed after initial deposition, depending on the local surface features at different spatial scales. Therefore, the accumulation conditions at an ice core site influence the quantity and quality of the recorded climate signal in proxy records. This study aims to characterise the local accumulation patterns and the evolution of the snow height to describe the contribution of the snow (re-)deposition to the overall noise level in climate records from ice cores. To this end, we applied a structure-from-motion photogrammetry approach to generate near-daily elevation models of the surface snow for a 195 m2 area in the vicinity of the deep drilling site of the East Greenland Ice-core Project in northeast Greenland. Based on the snow height information we derive snow height changes on a day-to-day basis throughout our observation period from May to August 2018 and find an average snow height increase of ∼ 11 cm. The spatial and temporal data set also allows an investigation of snow deposition versus depositional modifications. We observe irregular snow deposition and erosion causing uneven snow accumulation patterns, a removal of more than 60 % of the deposited snow, and a negative relationship between the initial snow height and the amount of accumulated snow. Furthermore, the surface roughness decreased by approximately a factor of 2 throughout the spring and summer season at our study site. Finally, our study shows that structure from motion is a relatively simple method to demonstrate the potential influences of depositional processes on proxy signals in snow and ice.

Snow Water Equivalent Accumulation Patterns from a Trajectory Approach over the U.S. Southern Rocky Mountains

The spatial characteristics and patterns of snow accumulation and ablation inform the amount of water stored and subsequently available for runoff and the timing of snowmelt. This paper characterizes the snow accumulation phase to investigate the spatiotemporal snow water equivalent (SWE) distribution by fitting a function to the trajectory plot of the standard deviation versus mean SWE across a domain. Data were used from 90 snow stations for a 34-year period across the Southern Rocky Mountains in the western United States. The stations were divided into sub-sets based on elevation, latitude, and the mean annual maximum SWE. The best function was a linear fit, excluding the first 35 mm of SWE. There was less variability with SWE data compared to snow depth data. The trajectory of the accumulation phase was consistent for most years, with limited correlation to the amount of accumulation. These trajectories are more similar for the northern portion of the domain and for below average snow years. This work could inform where to locate new stations, or be applied to other earth system variables.

A portable lightweight in situ analysis (LISA) box for ice and snow analysis

Abstract. There are enormous costs involved in transporting snow and ice samples to home laboratories for “simple” analyses in order to constrain annual layer thicknesses and identify accumulation rates of specific sites. It is well known that depositional noise, incurred from factors such as wind drifts, seasonally biased deposition and melt layers can influence individual snow and firn records and that multiple cores are required to produce statistically robust time series. Thus, at many sites, core samples are measured in the field for densification, but the annual accumulation and the content of chemical impurities are often represented by just one core to reduce transport costs. We have developed a portable “lightweight in situ analysis” (LISA) box for ice, firn and snow analysis that is capable of constraining annual layers through the continuous flow analysis of meltwater conductivity and hydrogen peroxide under field conditions. The box can run using a small gasoline generator and weighs less than 50 kg. The LISA box was tested under field conditions at the East Greenland Ice-core Project (EastGRIP) deep ice core drilling site in northern Greenland. Analysis of the top 2 m of snow from seven sites in northern Greenland allowed the reconstruction of regional snow accumulation patterns for the 2015–2018 period (summer to summer).

Evidence of eutrophication in Arctic lakes

Lakes and ponds are dominant components of Arctic landscapes and provide food and water for northern communities. In the Greiner Lake watershed, in Cambridge Bay (Nunavut, Canada), water bodies are small (84% &lt;5 ha) and shallow (99% &lt;4 m deep). Such characteristics make them vulnerable to eutrophication as temperatures rise and nutrient concentrations from the greening landscape increase. Here, we investigated and compared 35 lakes and ponds in the Greiner watershed in August 2018 and 2019 to determine their current trophic states based on their chemical composition and phytoplankton communities. The ponds had higher trophic status than the lakes, but overall, most sites were oligotrophic. Lake ERA5, located upstream of any direct human influence was classified as eutrophic due to high total phosphorus (32.3 μg·L−1) and a high proportion of Cyanobacteria (42.9% of total phytoplankton biovolume). Satellite imagery suggests the lake may have been eutrophic for the last 30 years. We hypothesize that the coupled effects of catchment characteristics and elevated local snow accumulation patterns promote higher nutrient leaching rates from the soils. We recommend further analysis and monitoring as eutrophication could become more widespread with ongoing climate change and the associated increases in temperature, precipitation, and catchment–lake coupling.

Evaluating Wind Fields for Use in Basin‐Scale Distributed Snow Models

AbstractMountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (&gt;50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.