Snowdrift-Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents
Date
2024-08-17
Authors
Marsh, Christopher
Lv, Zhibang
Vionnet, Vincent
Harder, Phillip
Spiteri, Raymond
Pomeroy, John
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Water Resources Research
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Article
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Abstract
The melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift-permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift-permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land-surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow-covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The CHM model code (Marsh, Pomeroy, & Wheater, 2020) and the Mesher code (Marsh et al., 2018) are both open-source. The high-resolution wind library has been generated using the WindNinja diagnostic wind model (Forthofer et al., 2014), via the Windmapper tool (Marsh, Vionnet, & Pomeroy, 2023). HRDPS forecasts are distributed on the Canadian Surface Prediction Archive (CaSPAr; Mai et al., 2019) is available at https://caspar-data.ca. The US SNOTEL data (snow and precipitation) are available at https://www.nrcs.usda.gov/wps/portal/wcc/home/snowClimateMonitoring/ and have been downloaded using the soilDB R package (http://ncss-tech.github.io/AQP/soilDB/soilDB-Intro.html). The CanSWE data set (Vionnet, Mortimer, et al., 2021) and lidar snow-depth data sets (Harder et al., 2020) are freely available.
Keywords
snowdrift-permitting simulations, seasonal snowpacks, SCA, lidar, hydrology, mountains
Citation
Marsh, C. B., Lv, Z., Vionnet, V., Harder, P., Spiteri, R. J., & Pomeroy, J. W. (2024). Snowdrift-permitting simulations of seasonal snowpack processes over large mountain extents. Water Resources Research, 60, e2023WR036948. https://doi.org/10.1029/2023WR036948
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DOI
10.1029/2023WR036948