Carroll, R. W. H., Deems, J. S., Niswonger, R., Schumer, R., and Williams, K. H.: The importance of interflow to groundwater recharge in a snowmelt-dominated headwater basin, Geophys. Res. Lett., 46, 5899–5908, 2019. a
Carroll, R. W. H., Gochis, D., and Williams, K. H.: Efficiency of the summer monsoon in generating streamflow within a snow-dominated headwater basin of the Colorado river, Geophys. Res. Lett., 47, e2020GL090856,
https://doi.org/10.1029/2020GL090856, 2020.
a
Chen, D., Dai, A., and Hall, A.: The convective-to-total precipitation ratio and the “drizzling” bias in climate models, J. Geophys. Res., 126, e2020JD034198,
https://doi.org/10.1029/2020JD034198, 2021.
a
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models: DIFFERENCES BETWEEN HYDROLOGICAL MODELS, Water Resour. Res., 44, 1–14,
https://doi.org/10.1029/2007WR006735, 2008.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k
Climate Data Gateway at NCAR: Gridded Ensemble Precipitation and Temperature Estimates over the Contiguous United States Version 2.0, NCAR [data set],
https://doi.org/10.5065/D6TH8JR2, 2018.
a
Dai, A.: Precipitation Characteristics in Eighteen Coupled Climate Models, J. Climate, 19, 4605–4630, 2006. 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, 2008.
a,
b,
c
de Boer, G., White, A., Cifelli, R., Intrieri, J., Hughes, M., Mahoney, K., Meyers, T., Lantz, K., Hamilton, J., Currier, W., Sedlar, J., Cox, C., Hulm, E., Riihimaki, L. D., Adler, B., Bianco, L., Morales, A., Wilczak, J., Elston, J., Stachura, M., Jackson, D., Morris, S., Chandrasekar, V., Biswas, S., Schmatz, B., Junyent, F., Reithel, J., Smith, E., Schloesser, K., Kochendorfer, J., Meyers, M., Gallagher, M., Longenecker, J., Olheiser, C., Bytheway, J., Moore, B., Calmer, R., Shupe, M. D., Butterworth, B., Heflin, S., Palladino, R., Feldman, D., Williams, K., Pinto, J., Osborn, J.,Costa, D., Hall, E., Herrera, C., Hodges, G., Soldo, L., Stierle, S., and Webb, R. S.: Supporting Advancement in Weather and Water Prediction in the Upper Colorado River Basin: The SPLASH Campaign, B. Am. Meteorol. Soc., 1, E1853–E1874,
https://doi.org/10.1175/BAMS-D-22-0147.1, 2023.
a
DeCicco, L., Hirsch, R., Lorenz, D., Watkins, D., and Johnson, M.: dataRetrieval: R packages for discovering and retrieving water data available from U.S. federal hydrologic web services, 2.7.13, U.S. Geological Survey, Reston, VA [code],
https://doi.org/10.5066/P9X4L3GE, 2023.
a
Feldman, D., Boos, A. A. W., Chandrasekar, R. C. V., Collis, W. C. S., De Mott, J. D. P., Flores, J. F. A., Harrington, D. G. J., Kumijian LR Leung, M., Raleigh, T. O. M., Mc Kenzie Skiles J Smith R Sullivan, A. R. S., Varble, P. U. A., and Williams, K.: Surface Atmosphere Integrated Field Laboratory (SAIL) Science Plan Tech. Rep. No. DOE/SC-ARM-21-004, Tech. Rep. DOE/SC-ARM-21-004, U. S. Department of Energy, Office of Science, Biological and Environmental Research Program, USA, 2021. a
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, 2017. a
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Stat. Sci., 7, 457–472, 1992.
a,
b
Goodison, B. E., Louie, P. Y. T., and Yang, D.: WMO solid precipitation measurement intercomparison, World Meteorological Organization, Geneva, Switzerland, 212 pp., 1998. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, 2009. a
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Gochis, D. J., Clark, M. P., Dudhia, J., and Thompson, G.: A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain, J. Climate, 25, 262–281, 2012.
a,
b,
c,
d
Hamon W. R.: Estimating Potential Evapotranspiration, J. Hydr Eng. Div.-ASCE, 87, 107–120, 1961. a
Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1–22,
https://doi.org/10.5194/hess-21-1-2017, 2017.
a
Henn, B., Clark, M. P., Kavetski, D., and Lundquist, J. D.: Estimating mountain basin-mean precipitation from streamflow using Bayesian inference, Water Resour. Res., 51, 8012–8033, 2015.
a,
b,
c,
d,
e,
f
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, 2016.
a,
b,
c,
d
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, 2018.
a,
b,
c
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N., Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga, T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The East River, Colorado, watershed: A mountainous community testbed for improving predictive understanding of multiscale hydrological–biogeochemical dynamics, Vadose Zone J., 17, 1–25, 2018.
a,
b
Hughes, M., Lundquist, J. D., and Henn, B.: Dynamical downscaling improves upon gridded precipitation products in the Sierra Nevada, California, Clim. Dynam., 55, 111–129, 2020. a
Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Guttman, E.: Simulation of seasonal snowfall over Colorado, Atmos. Res., 97, 462–477, 2010.
a,
b
Janić, Z. I.: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model, Tech. rep., National Centers for Environmental Prediction Office, USA, 2001. a
Jing, X., Geerts, B., Wang, Y., and Liu, C.: Evaluating Seasonal Orographic Precipitation in the Interior Western United States Using Gauge Data, Gridded Precipitation Estimates, and a Regional Climate Simulation, J. Hydrometeorol., 18, 2541–2558, 2017.
a,
b,
c,
d
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward: CATCHMENTS AS SIMPLE DYNAMICAL SYSTEMS, Water Resour. Res., 45, W02429,
https://doi.org/10.1029/2008wr006912, 2009.
a
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18, 4261–4275,
https://doi.org/10.5194/hess-18-4261-2014, 2014.
a,
b
Koskela, J. J., Croke, B. W. F., Koivusalo, H., Jakeman, A. J., and Kokkonen, T.: Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment, Water Resour. Res., 48, W11513,
https://doi.org/10.1029/2011WR011773, 2012.
a
Le Moine, N., Hendrickx, F., Gailhard, J., Garçon, R., and Gottardi, F.: Hydrologically Aided Interpolation of Daily Precipitation and Temperature Fields in a Mesoscale Alpine Catchment, J. Hydrometeorol., 16, 2595–2618, 2015.
a,
b
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resour. Res., 51, 7309–7342, 2015. a
Lin, Y. and Mitchell, K. E.: 1.2 the NCEP stage II/IV hourly precipitation analyses: Development and applications, in: Proceedings of the 19th Conference Hydrology, American Meteorological Society, 9–13 January 2005, San Diego, CA, USA, vol. 10, Citeseer, 2005. a
Liston, G. E. and Elder, K.: A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol., 7, 217–234, 2006. a
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.: Continental-scale convection-permitting modeling of the current and future climate of North America, Clim. Dynam., 49, 71–95, 2017.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P., and Lettenmaier, D. P.: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, J. Climate, 26, 9384–9392, 2013.
a,
b
Lundquist, J., Hughes, M., 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, 2019.
a,
b,
c
Lute, A. C. and Abatzoglou, J. T.: Best practices for estimating near-surface air temperature lapse rates, Int. J. Climatol., 41, E110–E125, https://doi.org/10.1002/joc.6668, 2021.
Maddox, R. A., Zhang, J., Gourley, J. J., and Howard, K. W.: Weather Radar Coverage over the Contiguous United States, Weather Forecast., 17, 927–934, 2002. a
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474,
https://doi.org/10.5194/hess-24-3451-2020, 2020.
a
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003,
https://doi.org/10.1029/2009RG000314, 2010.
a,
b
Monin, A. S. and Obukhov, A. M.: Osnovnye zakonomernosti turbulentnogo peremeshivanija v prizemnom sloe atmosfery (Basic laws of turbulent mixing in the atmosphere near the ground), Trudy geofiz. inst. AN SSSR, 24, 163–187, 1954. a
Neale, R. B., Chen, C. C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J. F., and Marsh, D.: Description of the NCAR community atmosphere model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+ STR, 1, 1–12, 2010.
a,
b
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, 2015. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, 1–19,
https://doi.org/10.1029/2010JD015139, 2011.
a
NOAA Physical Sciences Laboratory: Livneh daily CONUS near-surface gridded meteorological and derived hydrometeorological data, NOAA PSL, Boulder, Colorado, USA [data set],
https://psl.noaa.gov/data/gridded/data.livneh.html last access: 1 January 2021. a
Oyler, J. W., Dobrowski, S. Z., Ballantyne, A. P., Klene, A. E., and Running, S. W.: Artificial amplification of warming trends across the mountains of the western United States, Geophys. Res. Lett., 42, 153–161, 2015. a
Painter, T.: ASO L4 Lidar Snow Depth 50m UTM Grid, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/STOT5I0U1WVI, 2018.
a
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, 2016.
a,
b
PRISM Climate Group: PRISM Climate Data, PRISM Climate Group, Oregon State University [data set],
https://prism.oregonstate.edu, last access: 1 January 2021. 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
Rasmussen, R. and Liu, C.: High Resolution WRF Simulations of the Current and Future Climate of North America, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory,
https://doi.org/10.5065/D6V40SXP, 2017.
a,
b
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Gutmann, E.: 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.
a,
b,
c
Rasmussen, R. M., Bernstein, B. C., Murakami, M., Stossmeister, G., Reisner, J., and Stankov, B.: The 1990 Valentine's Day Arctic Outbreak. Part I: Mesoscale and Microscale Structure and Evolution of a Colorado Front Range Shallow Upslope Cloud, J. Appl. Meteorol. Clim., 34, 1481–1511, 1995. a
Rudisill, W., Vincent, A., Nash, C., and Flores, A.: Dynamically Downscaled (WRF) 1 km, Hourly Meteorological Conditions 1987–2020. East/Taylor Watersheds. Science Area 1: Standard Award: Model-Data Fusion to Examine Multiscale Dynamical Controls on Snow Cover and Critical Zone Moisture Inputs, ESS-DIVE repository [data set],
https://doi.org/10.15485/1845448, 2022.
a,
b
Ryken, A., Gochis, D., and Maxwell, R. M.: Unraveling groundwater contributions to evapotranspiration and constraining water fluxes in a high-elevation catchment, Hydrol. Process., e14449,
https://doi.org/10.1002/hyp.14449, 2021.
a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, 2010.
a,
b,
c,
d,
e,
f
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, 2014.
a,
b
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C.: Probabilistic programming in Python using PyMC3, PeerJ Comput. Sci., 2, e55,
https://doi.org/10.7717/peerj-cs.55, 2016.
a,
b
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, 1999. a
Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., Jones, A. D., Collins, W. D., and Kaatz, L.: A low-to-no snow future and its impacts on water resources in the western United States, Nature Reviews Earth & Environment, 2, 800–819, 2021. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: G.: A description of the Advanced Research WRF version 3, in: NCAR Tech. Note NCAR/TN-475+STR, National Center for Atmospheric Research; Boulder, Colorado, USA, 2008. 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., 124, 5296–5313, 2019. a
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, 2008. a
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda, R., Vose, R. S., and Cook, R. B.: Daymet: daily surface weather data on a 1-km grid for North America, version 3. ORNL DAAC, Oak Ridge, Tennessee, USA, in: USDA-NASS, 2019. 2017 Census of Agriculture, Summary and State Data, Geographic Area Series, Part 51, AC-17-A-51, ORNL DAAC, Oak Ridge, Tennessee, USA,
https://daac.ornl.gov (last access: 31 October 2023), 2016. a
Thyer, M., Renard, B., Kavetski, D., Kuczera, G., Franks, S. W., and Srikanthan, S.: Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis, Water Resour. Res., 45, 22,
https://doi.org/10.1029/2008WR006825, 2009.
a,
b,
c
Tillman, F. D., Day, N. K., Miller, M. P., Miller, O. L., Rumsey, C. A., Wise, D. R., Longley, P. C., and McDonnell, M. C.: A Review of Current Capabilities and Science Gaps in Water Supply Data, Modeling, and Trends for Water Availability Assessments in the Upper Colorado River Basin, Water, 14, 3813,
https://doi.org/10.3390/w14233813, 022.
a
Tran, A. P., Rungee, J., Faybishenko, B., Dafflon, B., and Hubbard, S. S.: Assessment of Spatiotemporal Variability of Evapotranspiration and Its Governing Factors in a Mountainous Watershed, Water, 11, 243,
https://doi.org/10.3390/w11020243, 2019.
a,
b
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, 2003. a
Udall, B. and Overpeck, J.: The twenty-first century Colorado River hot drought and implications for the future, Water Resour. Res., 53, 2404–2418, 2017.
a,
b
Valery, A., Andréassian, V., and Perrin, C.:
Inverting the hydrological cycle: when streamflow measurements help assess altitudinal precipitation gradients in mountain areas, IAHS Publishing. Location: Wallingford, Oxfordshire, UK,
https://iahs.info/uploads/dms/14842.41-281-286-333-28-3967--VALERY-Corr.pdf, last access: 2021-4-20, 2009. a
Vögeli, C., Lehning, M., Wever, N., and Bavay, M.: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution, Front Earth Sci. Chin., 4, 108,
https://doi.org/10.3389/feart.2016.00108, 2016.
a
Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., and Lundquist, J. D.: Characterizing biases in mountain snow accumulation from global data sets, Water Resour. Res., 55, 9873–9891, 2019. a
Xu, Z., Siirila-Woodburn, E. R., Rhoades, A. M., and Feldman, D.: Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin, Hydrol. Earth Syst. Sci., 27, 1771–1789,
https://doi.org/10.5194/hess-27-1771-2023, 2023.
a
