Improving mountain snow prediction requires better representation of meteorological uncertainty. This study generates meteorological ensembles accounting for topography, gauge undercatch, and large-scale atmospheric patterns. Driving a physics-based snow model calibrated using a Large Sample Emulator with these ensembles propagates forcing uncertainty into snow accumulation and melt, producing probablistic estimates for data assimilation and hydrological forecasting.
Anderson, G. S.: Hydrologic Reconnaissance of the Tanana Basin, Central Alaska, Hydrologic Atlas, U.S. Geological Survey (USGS), https://doi.org/10.3133/ha319, 1970. a
Arnal, L., Clark, M. P., Pietroniro, A., Vionnet, V., Casson, D. R., Whitfield, P. H., Fortin, V., Wood, A. W., Knoben, W. J. M., Newton, B. W., and Walford, C.: FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America, Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, 2024. a
Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Beck, H. E., McNamara, I., Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J. D., and Xuan Thinh, N.: RF-MEP: A Novel Random Forest Method for Merging Gridded Precipitation Products and Ground-Based Measurements, Remote Sens. Environ., 239, 111606, https://doi.org/10.1016/j.rse.2019.111606, 2020. a, b
Barlage, M., Chen, F., Tewari, M., Ikeda, K., Gochis, D., Dudhia, J., Rasmussen, R., Livneh, B., Ek, M., and Mitchell, K.: Noah Land Surface Model Modifications to Improve Snowpack Prediction in the Colorado Rocky Mountains, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013470, 2010. a
Bash, E. A. and Marshall, S. J.: Estimation of Glacial Melt Contributions to the Bow River, Alberta, Canada, Using a Radiation-Temperature Melt Model, Ann. Glaciol., 55, 138–152, https://doi.org/10.3189/2014AoG66A226, 2014. a
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017. a, b
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1∘ Precipitation: Methodology and Quantitative Assessment, B. Am Meteorol. Soc., https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a
Behrangi, A., Gardner, A., Reager, J. T., Fisher, J. B., Yang, D., Huffman, G. J., and Adler, R. F.: Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes, J. Climate, https://doi.org/10.1175/JCLI-D-18-0163.1, 2018. a
Bennett, A. R., Hamman, J. J., and Nijssen, B.: MetSim: A Python Package for Estimation and Disaggregation of Meteorological Data, Journal of Open Source Software, 5, 2042, https://doi.org/10.21105/joss.02042, 2020. a, b
Bennett, K. E., Cherry, J. E., Balk, B., and Lindsey, S.: Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska, Hydrol. Earth Syst. Sci., 23, 2439–2459, https://doi.org/10.5194/hess-23-2439-2019, 2019. a
Berg, J., Reynolds, D., Quéno, L., Jonas, T., Lehning, M., and Mott, R.: A Seasonal Snowpack Model Forced with Dynamically Downscaled Forcing Data Resolves Hydrologically Relevant Accumulation Patterns, Frontiers in Earth Science, 12, https://doi.org/10.3389/feart.2024.1393260, 2024. a, b, c
Bertoncini, A. and Pomeroy, J. W.: Quantifying spatiotemporal and elevational precipitation gauge network uncertainty in the Canadian Rockies, Hydrol. Earth Syst. Sci., 29, 983–1000, https://doi.org/10.5194/hess-29-983-2025, 2025. a, b, c, d, e
Blöschl, G. and Sivapalan, M.: Scale Issues in Hydrological Modelling: A Review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995. a
Bohn, T. J., Livneh, B., Oyler, J. W., Running, S. W., Nijssen, B., and Lettenmaier, D. P.: Global Evaluation of MTCLIM and Related Algorithms for Forcing of Ecological and Hydrological Models, Agr. Forest Meteorol., 176, 38–49, https://doi.org/10.1016/j.agrformet.2013.03.003, 2013. a, b
Böhner, J. and Antonić, O.: Land-Surface Parameters Specific to Topo-Climatology, Chapter 8 in: Developments in Soil Science, edited by: Hengl, T. and Reuter, H. I., vol. 33 of Geomorphometry, Elsevier, 195–226, https://doi.org/10.1016/S0166-2481(08)00008-1, 2009. a, b
BRCB: Bow River Basin State of the Watershed Summary, 2010, Bow River Council, Bow River Watershed (Alta.), https://sswm.info/sites/default/files/reference_attachments/BOW_RIVER_BASIN_COUNCIL_2010_Bow_River_Basin_State_of_the_Watershed_Summary_2010.pdf (last access: 8 October 2026), 2010. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
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. a
Bunn, P. T., Wood, A. W., Newman, A. J., Chang, H.-I., Castro, C. L., Clark, M. P., and Arnold, J. R.: Improving Station-Based Ensemble Surface Meteorological Analyses Using Numerical Weather Prediction: A Case Study of the Oroville Dam Crisis Precipitation Event, J. Hydrometeorol., https://doi.org/10.1175/JHM-D-21-0193.1, 2022. a, b, c, d
Carlson, K. A., Levin, H. K., Morris, A. L., Candela, S. G., Rivera, A. M. M., Huening, V. G., and Fredericks, J. G.: TDX-hydro: Global High-Resolution Hydrography Derived from TanDEM-x, ESS Open Archive, https://doi.org/10.22541/essoar.171629686.65893579/v1, 2024. a
Carlstein, E.: The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence, Ann. Stat., 14, 1171–1179, 1986. a
Casson, D. R.: Ensemble Meteorological Forcing for Chena, Bow and Tuolumne, Federated Research Data Repository (FRDR) [data set], https://doi.org/10.20383/103.01532, 2025a. a
Casson, D. R.: Gpep_snakemake: Workflow for Generating Probabilistic Ensemble Meteorological Forcings, Zenodo [code], https://doi.org/10.5281/zenodo.17727619, 2025b. a
Casson, D. R.: Gpep_to_summa_snakemake: Workflow for Coupling GPEP Ensemble Forcings to the Physically Based Hydrological Modelling, Zenodo [code], https://doi.org/10.5281/zenodo.17727623, 2025c. a
Casson, D. R.: SUMMA Model and GPEP Input Files, Zenodo [data set], https://doi.org/10.5281/zenodo.17740654, 2025d. a
Casson, D. R., Werner, M., Weerts, A., and Solomatine, D.: Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed, Hydrol. Earth Syst. Sci., 22, 4685–4697, https://doi.org/10.5194/hess-22-4685-2018, 2018. a
Chaney, N. W., Van Huijgevoort, M. H. J., Shevliakova, E., Malyshev, S., Milly, P. C. D., Gauthier, P. P. G., and Sulman, B. N.: Harnessing big data to rethink land heterogeneity in Earth system models, Hydrol. Earth Syst. Sci., 22, 3311–3330, https://doi.org/10.5194/hess-22-3311-2018, 2018. a
Clark, M. P. and Slater, A. G.: Probabilistic Quantitative Precipitation Estimation in Complex Terrain, J. Hydrometeorol., 7, 3–22, https://doi.org/10.1175/JHM474.1, 2006. a, b, c, d, e, f
Clark, M. P., Slater, A. G., Barrett, A. P., Hay, L. E., McCabe, G. J., Rajagopalan, B., and Leavesley, G. H.: Assimilation of Snow Covered Area Information into Hydrologic and Land-Surface Models, Adv. Water Resour., 29, 1209–1221, https://doi.org/10.1016/j.advwatres.2005.10.001, 2006. a, b
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Hreinsson, E. Ö., and Woods, R. A.: Representing Spatial Variability of Snow Water Equivalent in Hydrologic and Land-Surface Models: A Review, Water Resour. Res., 47, https://doi.org/10.1029/2011WR010745, 2011. a, b, c
Clark, M. P., Nijssen, B., Lundquist, D., Kavetski, D., Rupp, E., Woods, A., Freer, E., Gutmann, D., Wood, W., Brekke, D., Arnold, R., Gochis, J., Rasmussen, M., Tarboton, G., Mahat, V., Flerchinger, N., and Marks, G.: The Structure for Unifying Multiple Modeling Alternatives (SUMMA), Version 1.0: Technical Description, National Center for Atmospheric Research (NCAR), https://doi.org/10.5065/D6WQ01TD, 2015a. a, b, c
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A Unified Approach for Process-Based Hydrologic Modeling: 1. Modeling Concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015b. a, b
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Gochis, D. J., Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger, G. N., and Marks, D. G.: A Unified Approach for Process-Based Hydrologic Modeling: 2. Model Implementation and Case Studies, Water Resour. Res., 51, 2515–2542, https://doi.org/10.1002/2015WR017200, 2015c. a, b
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K. R., and Papalexiou, S. M.: The Abuse of Popular Performance Metrics in Hydrologic Modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021a. a
Clark, M. P., Zolfaghari, R., Green, K. R., Trim, S., Knoben, W. J. M., Bennett, A., Nijssen, B., Ireson, A., and Spiteri, R. J.: The Numerical Implementation of Land Models: Problem Formulation and Laugh Tests, J. Hydrometeorol., 22, 1627–1648, https://doi.org/10.1175/JHM-D-20-0175.1, 2021b. a
Cloke, H. L. and Pappenberger, F.: Ensemble Flood Forecasting: A Review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. a
Colli, M., Pollock, M., Stagnaro, M., Lanza, L. G., Dutton, M., and O'Connell, E.: A Computational Fluid-Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges, Water Resour. Res., 54, 779–796, https://doi.org/10.1002/2017WR020549, 2018. a
Colli, M., Stagnaro, M., Lanza, L. G., Rasmussen, R., and Thériault, J. M.: Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity, J. Hydrometeorol., https://doi.org/10.1175/JHM-D-19-0222.1, 2020. a
Collins, M.: Ensembles and Probabilities: A New Era in the Prediction of Climate Change, Philos. T. R. Soc. A, 365, 1957–1970, https://doi.org/10.1098/rsta.2007.2068, 2007. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016, Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020, 2020. a
Cristea, N. C., Lundquist, J. D., Loheide II, S. P., Lowry, C. S., and Moore, C. E.: Modelling How Vegetation Cover Affects Climate Change Impacts on Streamflow Timing and Magnitude in the Snowmelt-Dominated Upper Tuolumne Basin, Sierra Nevada, Hydrol. Process., 28, 3896–3918, https://doi.org/10.1002/hyp.9909, 2014. a
Crumley, R. L., Palomaki, R. T., Nolin, A. W., Sproles, E. A., and Mar, E. J.: SnowCloudMetrics: Snow Information for Everyone, Remote Sens., 12, 3341, https://doi.org/10.3390/rs12203341, 2020. a, b
Currier, W. R. and Lundquist, J. D.: Snow Depth Variability at the Forest Edge in Multiple Climates in the Western United States, Water Resour. Res., 54, 8756–8773, https://doi.org/10.1029/2018WR022553, 2018. a
Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., and Lawler, J. J.: Random Forests for Classification in Ecology, Ecology, 88, 2783–2792, https://doi.org/10.1890/07-0539.1, 2007. a
Daly, C., Neilson, R. P., and Phillips, D. L.: A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain, J. Appl. Meteorol. Climatol., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically Sensitive Mapping of Climatological Temperature and Precipitation across the Conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b, c
Daly, C., Doggett, M. K., Smith, J. I., Olson, K. V., Halbleib, M. D., Dimcovic, Z., Keon, D., Loiselle, R. A., Steinberg, B., Ryan, A. D., Pancake, C. M., and Kaspar, E. M.: Challenges in Observation-Based Mapping of Daily Precipitation across the Conterminous United States, J. Atmos. Ocean. Tech., 38, 1979–1992, https://doi.org/10.1175/JTECH-D-21-0054.1, 2021. a, b, c
Daly, S. F. and Vuyovich, C. M.: The Chena River Watershed Hydrology Model, Technical Report ERDC/CRREL TR-12-1, U. S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory (ERDC/CRREL), https://hdl.handle.net/11681/5364 (last access: 8 October 2026), 2012. a, b, c
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar Measurement of Snow Depth: A Review, J. Glaciol., 59, 467–479, 2013. a
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the Spatial Distribution of Snow Water Equivalent in the World's Mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016. a, b
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive Automated Quality Assurance of Daily Surface Observations, J. Appl. Meteorol. Climatol., 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1, 2010. a, b
Ensor, L. A. and Robeson, S. M.: Statistical Characteristics of Daily Precipitation: Comparisons of Gridded and Point Datasets, J. Appl. Meteorol. Climatol., 47, 2468–2476, https://doi.org/10.1175/2008JAMC1757.1, 2008. a
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A Comparison of 1701 Snow Models Using Observations from an Alpine Site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a, b, c, d
Fang, X., Pomeroy, J. W., DeBeer, C. M., Harder, P., and Siemens, E.: Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies, Earth Syst. Sci. Data, 11, 455–471, https://doi.org/10.5194/essd-11-455-2019, 2019. a
Farahani, M. A., Wood, A. W., Tang, G., and Mizukami, N.: Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments, Hydrol. Earth Syst. Sci., 29, 4515–4537, https://doi.org/10.5194/hess-29-4515-2025, 2025. a, b, c
Fleming, S. W., Zukiewicz, L., Strobel, M. L., Hofman, H., and Goodbody, A. G.: SNOTEL, the Soil Climate Analysis Network, and Water Supply Forecasting at the Natural Resources Conservation Service: Past, Present, and Future, JAWRA J. Am. Water Resour. Assoc., 59, 585–599, https://doi.org/10.1111/1752-1688.13104, 2023. a
Fox, E. W., Hoef, J. M. V., and Olsen, A. R.: Comparing Spatial Regression to Random Forests for Large Environmental Data Sets, PLOS ONE, 15, e0229509, https://doi.org/10.1371/journal.pone.0229509, 2020. a
Frei, C. and Isotta, F. A.: Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps, J. Geophys. Res.-Atmos., 2018JD030004, https://doi.org/10.1029/2018JD030004, 2019. a, b, c, d
Frey, P. J., Mueller, E. W., and Berry, E. C.: The Chena River: The Study of a Subarctic Stream, Federal Water Quality Administration, Department of the Interior, Alaska Water Laboratory, https://books.google.com/books/about/The_Chena_River.html?id=XXMg0gEACAAJ (last access: 8 October 2026), 1970. a
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA Land Processes Distributed Active Archive Center, https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019. a, b
Froidurot, S., Zin, I., Hingray, B., and Gautheron, A.: Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables, J. Hydrometeorol., 15, 685–696, https://doi.org/10.1175/JHM-D-13-073.1, 2014. a
Gascoin, S., Luojus, K., Nagler, T., Lievens, H., Masiokas, M., Jonas, T., Zheng, Z., and De Rosnay, P.: Remote Sensing of Mountain Snow from Space: Status and Recommendations, Frontiers in Earth Science, 12, https://doi.org/10.3389/feart.2024.1381323, 2024. a
Gasset, N., Fortin, V., Dimitrijevic, M., Carrera, M., Bilodeau, B., Muncaster, R., Gaborit, É., Roy, G., Pentcheva, N., Bulat, M., Wang, X., Pavlovic, R., Lespinas, F., Khedhaouiria, D., and Mai, J.: A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model, Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, 2021. a, b
Gervais, M., Tremblay, L. B., Gyakum, J. R., and Atallah, E.: Representing Extremes in a Daily Gridded Precipitation Analysis over the United States: Impacts of Station Density, Resolution, and Gridding Methods, J. Climate, 27, 5201–5218, https://doi.org/10.1175/JCLI-D-13-00319.1, 2014. a
Gharari, S., Keshavarz, K., Knoben, W. J. M., Tang, G., and Clark, M. P.: EASYMORE: A Python Package to Streamline the Remapping of Variables for Earth System Models, SoftwareX, 24, https://doi.org/10.1016/j.softx.2023.101547, 2023. a
Gil, Y., David, C. H., Demir, I., Essawy, B. T., Fulweiler, R. W., Goodall, J. L., Karlstrom, L., Lee, H., Mills, H. J., Oh, J.-H., Pierce, S. A., Pope, A., Tzeng, M. W., Villamizar, S. R., and Yu, X.: Toward the Geoscience Paper of the Future: Best Practices for Documenting and Sharing Research from Data to Software to Provenance, Earth Space Sci., 3, 388–415, https://doi.org/10.1002/2015EA000136, 2016. a
Girotto, M., Musselman, K. N., and Essery, R. L. H.: Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow, Current Climate Change Reports, 6, 81–94, https://doi.org/10.1007/s40641-020-00159-7, 2020. a, b, c, d
Goodison, B. E., Louie, P. Y. T., and Yang, D.: WMO Solid precipitation measurement intercomparison – Final report, https://library.wmo.int/es/records/item/28336-wmo-solid-precipitation-measurement-intercomparison (last access: 8 October 2026), 1998. a, b, c, d, e
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations–Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403, 2019. a, b
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61, National Snow and Ice Data Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.061, 2021. a, b
Hallouin, T., Bourgin, F., Perrin, C., Ramos, M.-H., and Andréassian, V.: EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions, Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, 2024. a, b
Hedrick, A. R., Marks, D., Marshall, H.-P., McNamara, J., Havens, S., Trujillo, E., Sandusky, M., Robertson, M., Johnson, M., Bormann, K. J., and Painter, T. H.: From Drought to Flood: A Water Balance Analysis of the Tuolumne River Basin during Extreme Conditions (2015–2017), Hydrol. Process., 34, 2560–2574, https://doi.org/10.1002/hyp.13749, 2020. a
Hedstrom, N. R. and Pomeroy, J. W.: Measurements and Modelling of Snow Interception in the Boreal Forest, Hydrol. Process., 12, 1611–1625, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4, 1998. a
Helbig, N., Mott, R., van Herwijnen, A., Winstral, A., and Jonas, T.: Parameterizing Surface Wind Speed over Complex Topography, J. Geophys. Res.-Atmos., 122, 651–667, https://doi.org/10.1002/2016JD025593, 2017. a
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global Gridded Soil Information Based on Machine Learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a, b
Henn, B., Clark, M. P., Kavetski, D., McGurk, B., Painter, T. H., and Lundquist, J. D.: Combining Snow, Streamflow, and Precipitation Gauge Observations to Infer Basin-Mean Precipitation, Water Resour. Res., 52, 8700–8723, https://doi.org/10.1002/2015WR018564, 2016. a, b
Henn, B., Clark, M. P., Kavetski, D., Newman, A. J., Hughes, M., McGurk, B., and Lundquist, J. D.: Spatiotemporal Patterns of Precipitation Inferred from Streamflow Observations across the Sierra Nevada Mountain Range, J. Hydrol., 556, 993–1012, https://doi.org/10.1016/j.jhydrol.2016.08.009, 2018a. a
Henn, B., Newman, A. J., Livneh, B., Daly, C., and Lundquist, J. D.: An Assessment of Differences in Gridded Precipitation Datasets in Complex Terrain, J. Hydrol., 556, 1205–1219, https://doi.org/10.1016/j.jhydrol.2017.03.008, 2018b. a, b
Henn, B., Painter, T. H., Bormann, K. J., McGurk, B., Flint, A. L., Flint, L. E., White, V., and Lundquist, J. D.: High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance, Water Resour. Res., 54, 746–766, https://doi.org/10.1002/2017WR020473, 2018c. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hong, Y., Xuan Do, H., Kessler, J., Fry, L., Read, L., Rafieei Nasab, A., Gronewold, A. D., Mason, L., and Anderson, E. J.: Evaluation of Gridded Precipitation Datasets over International Basins and Large Lakes, J. Hydrol., 607, 127507, https://doi.org/10.1016/j.jhydrol.2022.127507, 2022. a, b
Hotovy, O., Nedelcev, O., Seibert, J., and Jenicek, M.: Rain-on-snow events in mountainous catchments under climate change, Hydrol. Earth Syst. Sci., 29, 4199–4217, https://doi.org/10.5194/hess-29-4199-2025, 2025. a
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017. a
Huidobro, G., Chiu, C.-M., Byun, K., and Hamlet, A. F.: Meteorological Driving Datasets for the U. S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections, J. Hydrometeorol., 24, 873–895, https://doi.org/10.1175/JHM-D-22-0148.1, 2023. a, b, c, d
Hungerford, R. D., Nemani, R. R., Running, S. W., and Coughlan, J. C.: MTCLIM: A Mountain Microclimate Simulation Model, Tech. Rep. INT-RP-414, U. S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, https://doi.org/10.2737/INT-RP-414, 1989. a
Huning, L. S. and AghaKouchak, A.: Global Snow Drought Hot Spots and Characteristics, P. Natl. Acad. Sci. USA, 117, 19753–19759, https://doi.org/10.1073/pnas.1915921117, 2020. a
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and Vulnerability of the World's Water Towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a
Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A. M., Helber, R., and Arnone, R. A.: Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment, J. Mar. Syst., 76, 64–82, https://doi.org/10.1016/j.jmarsys.2008.05.014, 2009. a
Jordan, R.: A One-Dimensional Temperature Model for a Snow Cover, Special report, Cold Regions Research and Engineering Laboratory (U.S.), https://hdl.handle.net/11681/11677 (last access: 8 October 2026), 1991. a
Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., Durand, M., Barros, A., Kim, E. J., Forman, B. A., Gutmann, E. D., Wrzesien, M. L., Garnaud, C., Sandells, M., Marshall, H.-P., Cristea, N., Pflug, J. M., Johnston, J., Cao, Y., Mocko, D., and Wang, S.: Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling, The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, 2021. a, b
Kimball, J. S., Running, S. W., and Nemani, R.: An Improved Method for Estimating Surface Humidity from Daily Minimum Temperature, Agr. Forest Meteorol., 85, 87–98, https://doi.org/10.1016/S0168-1923(96)02366-0, 1997. a
Knoben, W.: Global USDA-NRCS Soil Texture Class Map, Hydroshare, https://doi.org/10.4211/hs.1361509511e44adfba814f6950c6e742, 2021. a
Knoben, W. J. M., Clark, M. P., Bales, J., Bennett, A., Gharari, S., Marsh, C. B., Nijssen, B., Pietroniro, A., Spiteri, R. J., Tang, G., Tarboton, D. G., and Wood, A. W.: Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model-Agnostic and Model-Specific Configuration Steps in Applications of Large-Domain Hydrologic Models, Water Resour. Res., 58, e2021WR031753, https://doi.org/10.1029/2021WR031753, 2022. a, b, c
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Buisan, S., Laine, T., Lee, G., Aceituno, J. L. C., Alastrué, J., Isaksen, K., Meyers, T., Brækkan, R., Landolt, S., Jachcik, A., and Poikonen, A.: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE, Hydrol. Earth Syst. Sci., 21, 3525–3542, https://doi.org/10.5194/hess-21-3525-2017, 2017a. a, b, c, d, e, f, g
Kochendorfer, J., Rasmussen, R., Wolff, M., Baker, B., Hall, M. E., Meyers, T., Landolt, S., Jachcik, A., Isaksen, K., Brækkan, R., and Leeper, R.: The quantification and correction of wind-induced precipitation measurement errors, Hydrol. Earth Syst. Sci., 21, 1973–1989, https://doi.org/10.5194/hess-21-1973-2017, 2017b. a, b, c, d, e, f
Köster, J. and Rahmann, S.: Snakemake – a Scalable Bioinformatics Workflow Engine, Bioinformatics, 28, 2520–2522, https://doi.org/10.1093/bioinformatics/bts480, 2012. a, b
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Frontiers in Earth Science, 8, https://doi.org/10.3389/feart.2020.00325, 2020. a, b, c, d
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan, G. B., and Slater, A. G.: Parameterization Improvements and Functional and Structural Advances in Version 4 of the Community Land Model, J. Adv. Model. Earth Sy., 3, https://doi.org/10.1029/2011MS00045, 2011. a
Liao, C. and Zhuang, Q.: Quantifying the Role of Snowmelt in Stream Discharge in an Alaskan Watershed: An Analysis Using a Spatially Distributed Surface Hydrology Model, J. Geophys. Res.-Earth, 122, 2183–2195, https://doi.org/10.1002/2017JF004214, 2017. a
Liu, H., Wood, A. W., Newman, A. J., and Clark, M. P.: Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets, J. Hydrometeorol., 23, 1525–1543, https://doi.org/10.1175/JHM-D-21-0176.1, 2022. a
Lundquist, J., Abel, M. R., Gutmann, E., and Kapnick, S.: Our Skill in Modeling Mountain Rain and Snow Is Bypassing the Skill of Our Observational Networks, B. Am. Meteorol. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a
Lundquist, J. D., Abel, M. R., Henn, B., Gutmann, E. D., Livneh, B., Dozier, J., and Neiman, P.: High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California, J. Hydrometeorol., 16, 1773–1792, https://doi.org/10.1175/JHM-D-15-0019.1, 2015a. a
Lundquist, J. D., Wayand, N. E., Massmann, A., Clark, M. P., Lott, F., and Cristea, N. C.: Diagnosis of Insidious Data Disasters, Water Resour. Res., 51, 3815–3827, https://doi.org/10.1002/2014WR016585, 2015b. a
Lundquist, J. D., Roche, J. W., Forrester, H., Moore, C., Keenan, E., Perry, G., Cristea, N., Henn, B., Lapo, K., McGurk, B., Cayan, D. R., and Dettinger, M. D.: Yosemite Hydroclimate Network: Distributed Stream and Atmospheric Data for the Tuolumne River Watershed and Surroundings, Water Resour. Res., 52, 7478–7489, https://doi.org/10.1002/2016WR019261, 2016. a
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018. a, b
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: Evaluating Snow Models with Varying Process Representations for Hydrological Applications, Water Resour. Res., 51, 2707–2723, https://doi.org/10.1002/2014WR016498, 2015. a
Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., and Andreadis, K.: The Utility of Infrequent Snow Depth Images for Deriving Continuous Space-Time Estimates of Seasonal Snow Water Equivalent, Geophys. Res. Lett., 46, 5331–5340, https://doi.org/10.1029/2019GL082507, 2019. a, b
Marks, D., Kimball, J., Tingey, D., and Link, T.: The Sensitivity of Snowmelt Processes to Climate Conditions and Forest Cover during Rain-on-Snow: A Case Study of the 1996 Pacific Northwest Flood, Hydrol. Process., 12, 1569–1587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L, 1998. a
Marsh, C. B., Vionnet, V., and Pomeroy, J. W.: Windmapper: An Efficient Wind Downscaling Method for Hydrological Models, Water Resour. Res., 59, e2022WR032683, https://doi.org/10.1029/2022WR032683, 2023. a
Marsh, C. B., Lv, Z., Vionnet, V., Harder, P., Spiteri, R. J., and Pomeroy, J. W.: Snowdrift-Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents, Water Resour. Res., 60, e2023WR036948, https://doi.org/10.1029/2023WR036948, 2024. a, b, c
Mekis, É. and Vincent, L. A.: An Overview of the Second Generation Adjusted Daily Precipitation Dataset for Trend Analysis in Canada, Atmos. Ocean, 49, 163–177, https://doi.org/10.1080/07055900.2011.583910, 2011. a
Mekis, E., Donaldson, N., Reid, J., Zucconi, A., Hoover, J., Li, Q., Nitu, R., and Melo, S.: An Overview of Surface-Based Precipitation Observations at Environment and Climate Change Canada, Atmos. Ocean, 56, 71–95, https://doi.org/10.1080/07055900.2018.1433627, 2018. a, b
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and Human Errors in a Snow Model Intercomparison, B. Am. Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2021. a, b
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012. a
Miller, Z. S., Peitzsch, E. H., Sproles, E. A., Birkeland, K. W., and Palomaki, R. T.: Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain, The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, 2022. a
Mortimer, C. and Vionnet, V.: Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021), Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, 2025a. a, b, c, d
Mortimer, C. and Vionnet, V.: Northern Hemisphere Historical In-Situ Snow Water Equivalent Dataset (NorSWE, 1979–2021), Zenodo [data set], https://doi.org/10.5281/zenodo.15263370, 2025b. a
Mosier, T. M., Hill, D. F., and Sharp, K. V.: How much cryosphere model complexity is just right? Exploration using the conceptual cryosphere hydrology framework, The Cryosphere, 10, 2147–2171, https://doi.org/10.5194/tc-10-2147-2016, 2016. a
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes, Frontiers in Earth Science, 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational Snow-Hydrological Modeling for Switzerland, Frontiers in Earth Science, 11, https://doi.org/10.3389/feart.2023.1228158, 2023. a, b
Mudryk, L., Mortimer, C., Derksen, C., Elias Chereque, A., and Kushner, P.: Benchmarking of snow water equivalent (SWE) products based on outcomes of the SnowPEx+ Intercomparison Project, The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, 2025. a, b
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C., Barlage, M., and Rasmussen, R.: Projected Increases and Shifts in Rain-on-Snow Flood Risk over Western North America, Nat. Clim. Change, 8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018. a
Napoli, A., Crespi, A., Ragone, F., Maugeri, M., and Pasquero, C.: Variability of Orographic Enhancement of Precipitation in the Alpine Region, Sci. Rep., 9, 13352, https://doi.org/10.1038/s41598-019-49974-5, 2019. a
Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., Mizukami, N., Brekke, L., and Arnold, J. R.: Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States, J. Hydrometeorol., 16, 2481–2500, https://doi.org/10.1175/JHM-D-15-0026.1, 2015. a, b, c, d
Newman, A. J., Clark, M. P., Wood, A. W., and Arnold, J. R.: Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon, J. Geophys. Res.-Atmos., 125, e2020JD032696, https://doi.org/10.1029/2020JD032696, 2020. a, b, c, d, e, f, g
Painter, T.: ASO L4 Lidar Snow Water Equivalent 50m UTM Grid, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.5067/M4TUH28NHL4Z, 2018. a, b
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C., and Winstral, A.: The Airborne Snow Observatory: Fusion of Scanning Lidar, Imaging Spectrometer, and Physically-Based Modeling for Mapping Snow Water Equivalent and Snow Albedo, Remote Sens. Environ., 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-Learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pflug, J. M. and Lundquist, J. D.: Inferring Distributed Snow Depth by Leveraging Snow Pattern Repeatability: Investigation Using 47 Lidar Observations in the Tuolumne Watershed, Sierra Nevada, California, Water Resour. Res., 56, e2020WR027243, https://doi.org/10.1029/2020WR027243, 2020. a, b
Pierre, A., Jutras, S., Smith, C., Kochendorfer, J., Fortin, V., and Anctil, F.: Evaluation of Catch Efficiency Transfer Functions for Unshielded and Single-Alter-Shielded Solid Precipitation Measurements, J. Atmos. Ocean. Tech., 36, 865–881, https://doi.org/10.1175/JTECH-D-18-0112.1, 2019. a
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L., Granger, R. J., and Carey, S. K.: The Cold Regions Hydrological Model: A Platform for Basing Process Representation and Model Structure on Physical Evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a, b
Pomeroy, J. W., Fang, X., and Marks, D. G.: The Cold Rain-on-Snow Event of June 2013 in the Canadian Rockies – Characteristics and Diagnosis, Hydrol. Process., 30, 2899–2914, https://doi.org/10.1002/hyp.10905, 2016. a
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a
Rice, R., Bales, R. C., Painter, T. H., and Dozier, J.: Snow Water Equivalent along Elevation Gradients in the Merced and Tuolumne River Basins of the Sierra Nevada, Water Resour. Res., 47, https://doi.org/10.1029/2010WR009278, 2011. a
Robert, S., Foresti, L., and Kanevski, M.: Spatial Prediction of Monthly Wind Speeds in Complex Terrain with Adaptive General Regression Neural Networks, Int. J. Climatol., 33, 1793–1804, https://doi.org/10.1002/joc.3550, 2013. a
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, 2018. a
Schreiner-McGraw, A. P. and Ajami, H.: Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model, Hydrol. Earth Syst. Sci., 26, 1145–1164, https://doi.org/10.5194/hess-26-1145-2022, 2022. a, b
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M., and Bajat, B.: Random Forest Spatial Interpolation, Remote Sens., 12, 1687, https://doi.org/10.3390/rs12101687, 2020. a
Smith, C. D., Mekis, E., Hartwell, M., and Ross, A.: The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019), Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, 2022. a, b, c, d, e, f, g
Stagge, J. H., Rosenberg, D. E., Abdallah, A. M., Akbar, H., Attallah, N. A., and James, R.: Assessing Data Availability and Research Reproducibility in Hydrology and Water Resources, Sci. Data, 6, 190030, https://doi.org/10.1038/sdata.2019.30, 2019. a
Stillinger, T., Rittger, K., Raleigh, M. S., Michell, A., Davis, R. E., and Bair, E. H.: Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, 2023. a, b
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010. a, b
Sturm, M., Goldstein, M. A., and Parr, C.: Water and Life from Snow: A Trillion Dollar Science Question, Water Resour. Res., 53, 3534–3544, https://doi.org/10.1002/2017WR020840, 2017. a
Sun, H. and Margulis, S. A.: Assessing the impact of meteorological forcing and its uncertainty on snow modeling and reanalysis, The Cryosphere, 20, 3345–3367, https://doi.org/10.5194/tc-20-3345-2026, 2026. a
Sun, N., Yan, H., Wigmosta, M. S., Leung, L. R., Skaggs, R., and Hou, Z.: Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States, J. Geophys. Res.-Atmos., 124, 5296–5313, https://doi.org/10.1029/2018JD030140, 2019. a, b, c, d
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., and Whitfield, P. H.: SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018, Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, 2020. a
Tang, G., Clark, M. P., and Papalexiou, S. M.: SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019, J. Climate, 34, 6493–6511, https://doi.org/10.1175/JCLI-D-21-0067.1, 2021. a, b, c, d
Tang, G., Wood, A. W., Newman, A. J., Clark, M. P., and Papalexiou, S. M.: GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications, Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, 2024. a, b, c, d, e, f, g
Tang, G., Clark, M. P., Knoben, W. J. M., Liu, H., Gharari, S., Arnal, L., Wood, A. W., Newman, A. J., Freer, J., and Papalexiou, S. M.: Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings, B. Am. Meteorol. Soc., 106, E146–E166, https://doi.org/10.1175/BAMS-D-24-0007.1, 2025a. a
Tang, G., Wood, A. W., and Swenson, S.: On Using AI-Based Large-Sample Emulators for Land/Hydrology Model Calibration and Regionalization, Water Resour. Res., 61, e2024WR039525, https://doi.org/10.1029/2024WR039525, 2025b. a, b
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, https://doi.org/10.1007/s10584-011-0221-7, 2012. a
Taylor, K. E.: Summarizing Multiple Aspects of Model Performance in a Single Diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a, b, c
Thornton, P. E., Running, S. W., and White, M. A.: Generating Surfaces of Daily Meteorological Variables over Large Regions of Complex Terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910, 2019. a
Tyson, C., Longyang, Q., Neilson, B. T., Zeng, R., and Xu, T.: Effects of Meteorological Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed, J. Hydrol., 619, 129304, https://doi.org/10.1016/j.jhydrol.2023.129304, 2023. a
Vernay, M., Lafaysse, M., Mérindol, L., Giraud, G., and Morin, S.: Ensemble Forecasting of Snowpack Conditions and Avalanche Hazard, Cold Reg. Sci. Technol., 120, 251–262, https://doi.org/10.1016/j.coldregions.2015.04.010, 2015. a
Verseghy, D. L.: Class – A Canadian Land Surface Scheme for GCMS. I. Soil Model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991. a
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. a, b
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021a. a, b, c
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, 2021b. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for Scientific Data Management and Stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a
Wilks, D. S.: Forecast Verification, Chapter 8 in: International Geophysics, edited by: Wilks, D. S., vol. 100 of Statistical Methods in the Atmospheric Sciences, Academic Press, 301–394, https://doi.org/10.1016/B978-0-12-385022-5.00008-7, 2011. a
Wolfensberger, D., Gabella, M., Boscacci, M., Germann, U., and Berne, A.: RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland, Atmos. Meas. Tech., 14, 3169–3193, https://doi.org/10.5194/amt-14-3169-2021, 2021. a, b, c
Wood, A., Hopson, T., Newman, J., Brekke, L., Arnold, J., and Clark, M.: Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill, J. Hydrometeorol., 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016. a
Wood, A. W. and Lettenmaier, D. P.: An Ensemble Approach for Attribution of Hydrologic Prediction Uncertainty, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL034648, 2008. a
Wu, W., Emerton, R., Duan, Q., Wood, A., Wetterhall, F., and Robertson, D.: Ensemble Flood Forecasting: Current Status and Future Opportunities, WIREs Water, 7, e1432, https://doi.org/10.1002/wat2.1432, 2020. a
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A High-Accuracy Map of Global Terrain Elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017. a, b
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019. a
Yang, D., Goodison, B. E., Metcalfe, J. R., Golubev, V. S., Bates, R., Pangburn, T., and Hanson, C. L.: Accuracy of NWS 8” Standard Nonrecording Precipitation Gauge: Results and Application of WMO Intercomparison, J. Atmos. Ocean. Tech., 15, 54–68, https://doi.org/10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2, 1998. a, b, c, d
Zhao, W. and Li, A.: A Review on Land Surface Processes Modelling over Complex Terrain, Adv. Meteorol., 2015, 607181, https://doi.org/10.1155/2015/607181, 2015. a