Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016, Atmos. Ocean, 57, 149–156, https://doi.org/10.1080/07055900.2019.1598843, 2019.
Clark, M. P., J. Hendrikx, A. G. Slater, D. Kavetski, B. Anderson, N. J. Cullen, T. Kerr, E. Orn Hreinsson, and R. A. Woods.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water. Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011.
Clow, D. W., Nanus, L., Verdin, K. L., and Schmidt, J.: Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA., Hydrol. Process., 26, 2583–2591, 2012.
Cui, G., Anderson, M., and Bales, R.: Mapping of snow water equivalent by a deep-learning model assimilating snow observations, J. Hydrol., 616, 128835, https://doi.org/10.1016/j.jhydrol.2022.128835, 2023.
Diaconescu, E. P., Gachon, P., Laprise, R., and Scinocca, J. F.: Evaluation of precipitation indices over North America from various configurations of regional climate models, Atmos. Ocean., 54, 418–439, 2016.
Dixon, D. and Boon, S.: Comparison of the SnowHydro snow sampler with existing snow tube designs, Hydrol. Process., 26, 2555–2562, 2012.
Dudhia, J.: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.
ECCC: Canadian Environmental Sustainability Indicators: Snow cover,
https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/snow-cover.html, last access: 12 January 2025.
Hauer, F. R., Baron, J. S., Campbell, D. H., Fausch, K. D., Hostetler, S. W., Leavesley, G. H., Leavitt, P. R., McKnight, D. M., and Stanford, J. A.: Assessment of climate change and freshwater ecosystems of the Rocky Mountains, USA and Canada, Hydrol. Process., 11, 903–924, https://doi.org/10.1002/(SICI)1099-1085(19970630)11:8<903::AID-HYP511>3.0.CO;2-7, 1997.
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and Rasmussen, R.: Can convection-permitting modeling provide decent precipitation for offline high-resolutio
n snowpack simulations over mountains?, J. Geophys. Res.-Atmos., 124, 12631–12654, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Holtzman, N. M., Pavelsky, T. M., Cohen, J. S., Wrzesien, M. L., and Herman, J. D.: Tailoring WRF and Noah-MP to improve process representation of Sierra Nevada runoff: Diagnostic evaluation and applications, J. Adv. Model. Earth. Sy., 12, e2019ms001832, https://doi.org/10.1029/2019MS001832, 2020.
Jin, J. and Wen, L.: Evaluation of snowmelt simulation in the Weather Research and Forecasting model, J. Geophys. Res.-Atmos., 117, D10110, https://doi.org/10.1029/2011JD016980, 2012.
Johnson, J. B. and Marks, D.: The detection and correction of snow water equivalent pressure sensor errors, Hydrol. Process., 18, 3513–3525, 2004.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Kain, J. S. and Fritsch, J. M.: Convective parameterization for mesoscale models: The Kain-Fritsch scheme, in: The representation of cumulus convection in numerical models, American Meteorological Society, Boston, MA, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16, 1993.
King, F., Erler, A. R., Frey, S. K., and Fletcher, C. G.: Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada, Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, 2020.
Klehmet, K., Geyer, B., and Rockel, B.: A regional climate model hindcast for Siberia: analysis of snow water equivalent, The Cryosphere, 7, 1017–1034, https://doi.org/10.5194/tc-7-1017-2013, 2013.
Leung, L. R. and Qian, Y.: The sensitivity of precipitation and snowpack simulations to model resolution via nesting in regions of complex terrain, J. Hydrometeorol., 4, 1025–1043, 2003.
Leung, L. R., Qian, Y., Han, J., and Roads, J. O.: Intercomparison of global reanalyses and regional simulations of cold season water budgets in the western United States, J. Hydrometeorol., 4, 1067–1087, 2003.
Li, Y. and Li, Z.: High-Resolution Weather Research Forecasting (WRF) Modeling and Projection Over Western Canada, Including Mackenzie Watershed, Arctic Hydrology, Permafrost and Ecosystems, Springer, Cham, 815–847, https://doi.org/10.1007/978-3-030-50930-9_28, 2021.
Li, Y., Li, Z., Zhang, Z., Chen, L., Kurkute, S., Scaff, L., and Pan, X.: High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach, Hydrol. Earth Syst. Sci., 23, 4635–4659, https://doi.org/10.5194/hess-23-4635-2019, 2019.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., and Dudhia, J.: Continental-scale convection-permitting modeling of the current and future climate of North America, Clim. Dynam, 49, 71–95, 2017.
López-Moreno, J. I., Fassnacht, S. R., Beguería, S., and Latron, J. B. P.: Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies, The Cryosphere, 5, 617–629, https://doi.org/10.5194/tc-5-617-2011, 2011.
Martz, L., Bruneau, J., Rolfe, J. T., Toth, B., Armstrong, R., Kulshreshtha, S., Thompson, W., Pietroniro, E., and Wagner, A.: Climate change and water: SSRB final technical report, GIServices, University of Saskatchewan, Saskatoon, Canada,
https://www.parc.ca/wp-content/uploads/2019/05/SSRB-2007-Climate_change_and_water.pdf (last access: February 2025), 2007.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Mortezapour, M., Menounos, B., Jackson, P. L., Erler, A. R., and Pelto, B. M.: The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation, Columbia River basin, Canada, Hydrol. Process., 34, 5085–5103, 2020.
Mott, R., Vionnet, V., and Grünewald, T.: The seasonal snow cover dynamics: review on wind-driven coupling processes, Front. Earth. Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., and Tewari, M.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Pan, M., Sheffield, J., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake, J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., and Luo, L.: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent, J. Geophys. Res.-Atmos., 108, 8850, https://doi.org/10.1029/2003JD003994, 2003.
Pavelsky, T. M., Kapnick, S., and Hall, A.: Accumulation and melt dynamics of snowpack from a multiresolution regional climate model in the central Sierra Nevada, California, J. Geophys. Res.-Atmos., 116, D16115, https://doi.org/10.1029/2010JD015479, 2011.
Raparelli, E., Tuccella, P., Colaiuda, V., and Marzano, F. S.: Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models, The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, 2023.
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., and Miller, K.: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate, J. Climate., 24, 3015–3048, 2011.
Rice, R. and Bales, R. C.: Embedded-sensor network design for snow cover measurements around snow pillow and snow course sites in the Sierra Nevada of California, Water Resour. Res., 46, W03537, https://doi.org/10.1029/2008WR007318, 2010.
Sabetghadam: The importance of model horizontal resolution_codes_data, Zenodo [code], https://doi.org/10.5281/zenodo.14884649, 2025.
Schirmer, M. and Jamieson, B.: Verification of analysed and forecasted winter precipitation in complex terrain, The Cryosphere, 9, 587–601, https://doi.org/10.5194/tc-9-587-2015, 2015.
Skamarock, W. C.: A description of the advanced research WRF version 3, Tech. Note, 1–96, 2008.
Taheri, M. and Mohammadian, A.: An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook, Sustainability, 14, 11395, https://doi.org/10.3390/su141811395, 2022.
Tanzeeba, S. and Gan, T. Y.: Potential impact of climate change on the water availability of South Saskatchewan River Basin, Climatic Change, 112, 355–386, 2012.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
Vionnet, V., Fortin, V., Gaborit, E., Roy, G., Abrahamowicz, M., Gasset, N., and Pomeroy, J. W.: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins, Hydrol. Earth Syst. Sci., 24, 2141–2165, https://doi.org/10.5194/hess-24-2141-2020, 2020.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.
WMO: Guide to instruments and methods of observation: Volume II – Measurement of Cryospheric Variables, 2018th edn., World Meteorological Organization, Geneva, WMO-No., 8, 52 pp., ISBN 978-92-63-10008-5,
https://articles.unesco.org/sites/default/files/medias/fichiers/2024/11/8_II-2023_en.pdf (last access: February 2025), 2018.
Wrzesien, M. L., Pavelsky, T. M., Kapnick, S. B., Durand, M. T., and Painter, T. H.: Evaluation of snow cover fraction for regional climate simulations in the Sierra Nevada, Int. J. Climatol., 35, 2472–2484, 2015.
Wrzesien, M. L., Durand, M. T., Pavelsky, T. M., Howat, I. M., Margulis, S. A., and Huning, L. S.: Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada, J. Hydrometeorol., 18, 1101–1119, 2017.
Wrzesien, M. L., Durand, M. T., Pavelsky, T. M., Kapnick, S. B., Zhang, Y., Guo, J., and Shum, C. K.: A new estimate of North American mountain snow accumulation from regional climate model simulations, Geophys. Res. Lett., 45, 1423–1432, 2018.
Xu, Y., Jones, A., and Rhoades, A.: A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets, Sci. Rep.-UK., 9, 16520, https://doi.org/10.1038/s41598-019-52880-5, 2019.
