• Auer, A. H., 1974: The rain versus snow threshold temperatures. Weatherwise, 27, 67, https://doi.org/10.1080/00431672.1974.9931684.

  • Barnett, T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303309, https://doi.org/10.1038/nature04141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Biederman, J. A., P. D. Brooks, A. A. Harpold, D. J. Gochis, E. Gutmann, D. E. Reed, E. Pendall, B. E. Ewers, 2014: Multiscale observations of snow accumulation and peak snowpack following widespread, insect-induced lodgepole pine mortality. Ecohydrology, 7, 150162, https://doi.org/10.1002/eco.1342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blöschl, G., 1999: Scaling issues in snow hydrology. Hydrol. Processes, 13, 21492175, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bright, B. C., J. A. Hicke, and A. J. H. Meddens, 2013: Effects of bark beetle-caused tree mortality on biogeochemical and biogeophysical MODIS products. J. Geophys. Res. Biogeosci., 118, 974982, https://doi.org/10.1002/jgrg.20078.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Broxton, P. D., A. A. Harpold, J. A. Biederman, P. A. Troch, N. P. Molotch, and P. D. Brooks, 2015: Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed-conifer forests. Ecohydrology, 8, 10731094, https://doi.org/10.1002/eco.1565.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavarria, S. B., and D. S. Gutzler, 2018: Observed changes in climate and streamflow in the Upper Rio Grande Basin. J. Amer. Water Resour. Assoc., 54, 644659, https://doi.org/10.1111/1752-1688.12640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clow, D. W., 2010: Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming. J. Climate, 23, 22932306, https://doi.org/10.1175/2009JCLI2951.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deems, J. S., S. R. Fassnacht, and K. J. Elder, 2006: Fractal distribution of snow depth from lidar data. J. Hydrometeor., 7, 285297, https://doi.org/10.1175/JHM487.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eidenshink, J., B. Schwind, K. Brewer, Z.-L. Zhu, B. Quayle, and S. Howard, 2007: A project for monitoring trends in burn severity. Fire Ecol., 3, 321, https://doi.org/10.4996/fireecology.0301003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elder, K., J. Dozier, and J. Michaelsen, 1991: Snow accumulation and distribution in an alpine watershed. Water Resour. Res., 27, 15411552, https://doi.org/10.1029/91WR00506.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ellis, C. R., J. W. Pomeroy, and T. E. Link, 2013: Modeling increases in snowmelt yield and desynchronization resulting from forest gap-thinning treatments in a northern mountain headwater basin. Water Resour. Res., 49, 936949, https://doi.org/10.1002/wrcr.20089.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fassnacht, S., N. Venable, D. McGrath, and G. Patterson, 2018: Sub-seasonal snowpack trends in the Rocky Mountain National Park area, Colorado, USA. Water, 10, 562, https://doi.org/10.3390/w10050562.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fletcher, S. J., G. E. Liston, C. A. Hiemstra, and S. D. Miller, 2012: Assimilating MODIS and AMSR-E snow observations in a snow evolution model. J. Hydrometeor., 13, 14751492, https://doi.org/10.1175/JHM-D-11-082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frank, J. M., W. J. Massman, B. E. Ewers, and D. G. Williams, 2019: Bayesian analyses of 17 winters of water vapor fluxes show bark beetles reduce sublimation. Water Resour. Res., 55, 15981623, https://doi.org/10.1029/2018WR023054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fritze, H., I. T. Stewart, and E. Pebesma, 2011: Shifts in western North American snowmelt runoff regimes for the recent warm decades. J. Hydrometeor., 12, 9891006, https://doi.org/10.1175/2011JHM1360.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furniss, M. J., and et al. , 2010: Water, climate change, and forests: Watershed stewardship for a changing climate. General Tech. Rep. PNW-GTR-812, 75 pp., https://www.fs.fed.us/pnw/pubs/pnw_gtr812.pdf.

  • Gascoin, S., S. Lhermitte, C. Kinnard, K. Bortels, and G. E. Liston, 2013: Wind effects on snow cover in Pascua-Lama, Dry Andes of Chile. Adv. Water Resour., 55, 2539, https://doi.org/10.1016/j.advwatres.2012.11.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Girotto, M., G. J. M. De Lannoy, R. H. Reichle, M. Rodell, C. Draper, S. N. Bhanja, and A. Mukherjee, 2017: Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India. Geophys. Res. Lett., 44, 41074115, https://doi.org/10.1002/2017GL072994.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleason, K. E., and A. W. Nolin, 2016: Charred forests accelerate snow albedo decay: Parameterizing the post-fire radiative forcing on snow for three years following fire. Hydrol. Processes, 30, 38553870, https://doi.org/10.1002/hyp.10897.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleason, K. E., A. W. Nolin, and T. R. Roth, 2013: Charred forests increase snowmelt: Effects of burned woody debris and incoming solar radiation on snow ablation. Geophys. Res. Lett., 40, 46544661, https://doi.org/10.1002/grl.50896.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleason, K. E., A. W. Nolin, and T. R. Roth, 2017: Developing a representative snow-monitoring network in a forested mountain watershed. Hydrol. Earth Syst. Sci., 21, 11371147, https://doi.org/10.5194/hess-21-1137-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleason, K. E., J. R. McConnell, M. M. Arienzo, N. Chellman, and W. M. Calvin, 2019: Four-fold increase in solar forcing on snow in western US burned forests since 1999. Nat. Commun., 10, 2026, https://doi.org/10.1038/s41467-019-09935-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greene, E. M., G. E. Liston, and R. A. Pielke, 1999: Simulation of above treeline snowdrift formation using a numerical snow-transport model. Cold Reg. Sci. Technol., 30, 135144, https://doi.org/10.1016/S0165-232X(99)00008-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hammond, J. C., F. A. Saavedra, and S. K. Kampf, 2018a: How does snow persistence relate to annual streamflow in mountain watersheds of the western U.S. with wet maritime and dry continental climates? Water Resour. Res., 54, 26052623, https://doi.org/10.1002/2017WR021899.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hammond, J. C., F. A. Saavedra, and S. K. Kampf, 2018b: Global snow zone maps and trends in snow persistence 2001-2016. Int. J. Climatol., 38, 43694383, https://doi.org/10.1002/joc.5674.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harpold, A. A., and P. D. Brooks, 2018: Humidity determines snowpack ablation under a warming climate. Proc. Natl. Acad. Sci. USA, 115, 12151220, https://doi.org/10.1073/pnas.1716789115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harpold, A. A., P. Brooks, S. Rajagopal, I. Heidbuchel, A. Jardine, and C. Stielstra, 2012: Changes in snowpack accumulation and ablation in the Intermountain West. Water Resour. Res., 48, W11501, https://doi.org/10.1029/2012WR011949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harpold, A. A., and et al. , 2014: Changes in snow accumulation and ablation following the Las Conchas Forest Fire, New Mexico, USA. Ecohydrology, 7, 440452, https://doi.org/10.1002/eco.1363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hedrick, A. R., and et al. , 2018: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time series into the iSnobal energy balance snow model. Water Resour. Res., 54, 80458063, https://doi.org/10.1029/2018WR023190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henn, B., A. J. Newman, B. Livneh, C. Daly, and J. D. Lundquist, 2018: An assessment of differences in gridded precipitation datasets in complex terrain. J. Hydrol., 556, 12051219, https://doi.org/10.1016/j.jhydrol.2017.03.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hiemstra, C. A., G. E. Liston, and W. A. Reiners, 2006: Observing, modelling, and validating snow redistribution by wind in a Wyoming upper treeline landscape. Ecol. Modell., 197, 3551, https://doi.org/10.1016/j.ecolmodel.2006.03.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Homer, C., and et al. , 2015: Completion of the 2011 National Land Cover Database for the conterminous United States - Representing a decade of land cover change information. Photogramm. Eng. Remote Sens., 81, 345354.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2019: IPCC special report on the ocean and cryosphere in a changing climate. H.-O. Pörtner et al., Eds., IPCC, 755 pp., https://www.ipcc.ch/srocc/.

  • Jasinski, M. F., and et al. , 2019: NCA-LDAS: Overview and analysis of hydrologic trends for the National Climate Assessment. J. Hydrometeor., 20, 15951617, https://doi.org/10.1175/JHM-D-17-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khaki, M., and J. Awange, 2019: The application of multi-mission satellite data assimilation for studying water storage changes over South America. Sci. Total Environ., 647, 15571572, https://doi.org/10.1016/j.scitotenv.2018.08.079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khaki, M., E. Forootan, M. Kuhn, J. Awange, A. I. J. M. van Dijk, M. Schumacher, and M. A. Sharifi, 2018: Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model. Adv. Water Resour., 114, 118, https://doi.org/10.1016/j.advwatres.2018.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kinar, N. J., and J. W. Pomeroy, 2015: Measurement of the physical properties of the snowpack. Rev. Geophys., 53, 481544, https://doi.org/10.1002/2015RG000481.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knowles, J. F., and et al. , 2015: The relative contributions of alpine and subalpine ecosystems to the water balance of a mountainous, headwater catchment. Hydrol. Processes, 29, 47944808, https://doi.org/10.1002/hyp.10526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knowles, N., M. D. Dettinger, and D. R. Cayan, 2006: Trends in snowfall versus rainfall in the western United States. J. Climate, 19, 45454559, https://doi.org/10.1175/JCLI3850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., D. A. Robinson, S. Champion, X. G. Yin, T. Estilow, and R. M. Frankson, 2016: Trends and extremes in northern hemisphere snow characteristics. Curr. Climate Change Rep., 2, 6573, https://doi.org/10.1007/s40641-016-0036-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehner, F., E. R. Wahl, A. W. Wood, D. B. Blatchford, and D. Llewellyn, 2017: Assessing recent declines in Upper Rio Grande runoff efficiency from a paleoclimate perspective. Geophys. Res. Lett., 44, 41244133, https://doi.org/10.1002/2017GL073253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, D., M. L. Wrzesien, M. Durand, J. Adam, and D. P. Lettenmaier, 2017: How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett., 44, 61636172, https://doi.org/10.1002/2017GL073551.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., 1995: Local advection of momentum, heat, and moisture during the melt of patchy snow covers. J. Appl. Meteor., 34, 17051715, https://doi.org/10.1175/1520-0450-34.7.1705.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and D. K. Hall, 1995: An energy-balance model of lake-ice evolution. J. Glaciol., 41, 373382, https://doi.org/10.1017/S0022143000016245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and M. Sturm, 1998: A snow-transport model for complex terrain. J. Glaciol., 44, 498516, https://doi.org/10.1017/S0022143000002021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and K. Elder, 2006a: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). J. Hydrometeor., 7, 217234, https://doi.org/10.1175/JHM486.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and K. Elder, 2006b: A distributed snow-evolution modeling system (SnowModel). J. Hydrometeor., 7, 12591276, https://doi.org/10.1175/JHM548.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and C. A. Hiemstra, 2008: A simple data assimilation system for complex snow distributions (SnowAssim). J. Hydrometeor., 9, 9891004, https://doi.org/10.1175/2008JHM871.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., and C. A. Hiemstra, 2011: The changing cryosphere: Pan-Arctic snow trends (1979–2009). J. Climate, 24, 56915712, https://doi.org/10.1175/JCLI-D-11-00081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., R. B. Haehnel, M. Sturm, C. A. Hiemstra, S. Berezovskaya, and R. D. Tabler, 2007: Simulating complex snow distributions in windy environments using SnowTran-3D. J. Glaciol., 53, 241256, https://doi.org/10.3189/172756507782202865.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., C. A. Hiemstra, K. Elder, and D. W. Cline, 2008: Mesocell study area snow distributions for the Cold Land Processes Experiment (CLPX). J. Hydrometeor., 9, 957976, https://doi.org/10.1175/2008JHM869.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liston, G. E., C. Polashenski, A. Rosel, P. Itkin, J. King, I. Merkouriadi, and J. Haapala, 2018: A distributed snow-evolution model for sea-ice applications (SnowModel). J. Geophys. Res. Oceans, 123, 37863810, https://doi.org/10.1002/2017JC013706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • López-Moreno, J. I., S. R. Fassnacht, S. Beguería, and J. B. P. Latron, 2011: Variability of snow depth at the plot scale: Implications for mean depth estimation and sampling strategies. Cryosphere, 5, 617629, https://doi.org/10.5194/tc-5-617-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • López-Moreno, J. I., J. Revuelto, S. R. Fassnacht, C. Azorin-Molina, S. M. Vicente-Serrano, E. Moran-Tejeda, and G. A. Sexstone, 2015: Snowpack variability across various spatio-temporal resolutions. Hydrol. Processes, 29, 12131224, https://doi.org/10.1002/hyp.10245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • López-Moreno, J. I., and et al. , 2017: Different sensitivities of snowpacks to warming in Mediterranean climate mountain areas. Environ. Res. Lett., 12, 074006, https://doi.org/10.1088/1748-9326/aa70cb.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • López-Moreno, J. I., and et al. , 2020: Long-term trends (1958–2017) in snow cover duration and depth in the Pyrenees. Int. J. Climatol., https://doi.org/10.1002/joc.6571, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, C., S. R. Fassnacht, and S. K. Kampf, 2019: How temperature sensor change affects warming trends and modeling: An evaluation across the state of Colorado. Water Resour. Res., 55, 97489764, https://doi.org/10.1029/2019WR025921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacDonald, M. K., J. W. Pomeroy, and A. Pietroniro, 2010: On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Hydrol. Earth Syst. Sci., 14, 14011415, https://doi.org/10.5194/hess-14-1401-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Non-parametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • Marchetto, A., 2017: rkt: Mann-Kendall test, seasonal and regional Kendall tests, version 1.5. R package, https://CRAN.R-project.org/package=rkt.

  • Margulis, S. A., G. Cortes, M. Girotto, and M. Durand, 2016: A Landsat-era Sierra Nevada snow reanalysis (1985–2015). J. Hydrometeor., 17, 12031221, https://doi.org/10.1175/JHM-D-15-0177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazzotti, G., R. Essery, C. D. Moeser, and T. Jonas, 2020: Resolving small-scale forest snow patterns using an energy balance snow model with a one-layer canopy. Water Resour. Res., 56, e2019WR026129, https://doi.org/10.1029/2019WR026129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCabe, G. J., and M. P. Clark, 2005: Trends and variability in snowmelt runoff in the western United States. J. Hydrometeor., 6, 476482, https://doi.org/10.1175/JHM428.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meromy, L., N. P. Molotch, T. E. Link, S. R. Fassnacht, and R. Rice, 2013: Subgrid variability of snow water equivalent at operational snow stations in the western USA. Hydrol. Processes, 27, 23832400, https://doi.org/10.1002/hyp.9355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Middelkoop, H., and et al. , 2001: Impact of climate change on hydrological regimes and water resources management in the Rhine basin. Climatic Change, 49, 105128, https://doi.org/10.1023/A:1010784727448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, K. E., and et al. , 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, https://doi.org/10.1029/2003JD003823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moeser, D., G. Mazzotti, N. Helbig, and T. Jonas, 2016: Representing spatial variability of forest snow: Implementation of a new interception model. Water Resour. Res., 52, 12081226, https://doi.org/10.1002/2015WR017961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molotch, N. P., 2009: Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model. Hydrol. Processes, 23, 10761089, https://doi.org/10.1002/hyp.7206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molotch, N. P., and R. C. Bales, 2005: Scaling snow observations from the point to the grid element: Implications for observation network design. Water Resour. Res., 41, W11421, https://doi.org/10.1029/2005WR004229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molotch, N. P., and R. C. Bales, 2006: SNOTEL representativeness in the Rio Grande headwaters on the basis of physiographics and remotely sensed snow cover persistence. Hydrol. Processes, 20, 723739, https://doi.org/10.1002/hyp.6128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morán-Tejeda, E., J. I. López-Moreno, and M. Beniston, 2013: The changing roles of temperature and precipitation on snowpack variability in Switzerland as a function of altitude. Geophys. Res. Lett., 40, 21312136, https://doi.org/10.1002/grl.50463.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P. W., 2006: Climate-driven variability and trends in mountain snowpack in western North America. J. Climate, 19, 62096220, https://doi.org/10.1175/JCLI3971.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P. W., A. F. Hamlet, M. P. Clark, and D. P. Lettenmaier, 2005: Declining mountain snowpack in western North America. Bull. Amer. Meteor. Soc., 86, 3950, https://doi.org/10.1175/BAMS-86-1-39.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mote, P. W., S. Li, D. P. Lettenmaier, M. Xiao, and R. Engel, 2018: Dramatic declines in snowpack in the western US. npj Climate Atmos. Sci., 1, 2, https://doi.org/10.1038/s41612-018-0012-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mott, R., V. Vionnet, and T. Grunewald, 2018: The seasonal snow cover dynamics: Review on wind-driven coupling processes. Front. Earth Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., N. P. Molotch, and S. A. Margulis, 2017a: Snowmelt response to simulated warming across a large elevation gradient, southern Sierra Nevada, California. Cryosphere, 11, 28472866, https://doi.org/10.5194/tc-11-2847-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., M. P. Clark, C. H. Liu, K. Ikeda, and R. Rasmussen, 2017b: Slower snowmelt in a warmer world. Nat. Climate Change, 7, 214219, https://doi.org/10.1038/nclimate3225.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolin, A. W., and C. Daly, 2006: Mapping “at risk” snow in the Pacific Northwest. J. Hydrometeor., 7, 11641171, https://doi.org/10.1175/JHM543.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oyler, J. W., S. Z. Dobrowski, A. P. Ballantyne, A. E. Klene, and S. W. Running, 2015: Artificial amplification of warming trends across the mountains of the western United States. Geophys. Res. Lett., 42, 153161, https://doi.org/10.1002/2014GL062803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Painter, T. H., 2018: ASO L4 lidar snow water equivalent 50m UTM grid, version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 18 September 2019, https://doi.org/10.5067/M4TUH28NHL4Z.

    • Crossref
    • Export Citation
  • Painter, T. H., S. M. Skiles, J. S. Deems, A. C. Bryant, and C. C. Landry, 2012: Dust radiative forcing in snow of the Upper Colorado River Basin: 1. A 6 year record of energy balance, radiation, and dust concentrations. Water Resour. Res., 48, W07521, https://doi.org/10.1029/2012WR011985.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Painter, T. H., and et al. , 2016: 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, 139152, https://doi.org/10.1016/j.rse.2016.06.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penn, C. A., D. W. Clow, G. A. Sexstone, and S. F. Murphy, 2020: Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande Headwaters. J. Amer. Water Resour. Assoc., 56, 882902, https://doi.org/10.1111/1752-1688.12863.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pomeroy, J. W., D. Marks, T. Link, C. Ellis, J. Hardy, A. Rowlands, and R. Granger, 2009: The impact of coniferous forest temperature on incoming longwave radiation to melting snow. Hydrol. Processes, 23, 25132525, https://doi.org/10.1002/hyp.7325.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, K. M., and B. L. Conkling, 2016: Forest health monitoring: National status, trends, and analysis 2015. General Tech. Rep. SRS-213, 199 pp., https://www.srs.fs.usda.gov/pubs/52181.

  • Prasad, R., D. G. Tarboton, G. E. Liston, C. H. Luce, and M. S. Seyfried, 2001: Testing a blowing snow model against distributed snow measurements at Upper Sheep Creek, Idaho, United States of America. Water Resour. Res., 37, 13411356, https://doi.org/10.1029/2000WR900317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pugh, E., and E. Small, 2012: The impact of pine beetle infestation on snow accumulation and melt in the headwaters of the Colorado River. Ecohydrology, 5, 467477, https://doi.org/10.1002/eco.239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pugh, E., and E. Gordon, 2013: A conceptual model of water yield effects from beetle-induced tree death in snow-dominated lodgepole pine forests. Hydrol. Processes, 27, 20482060, https://doi.org/10.1002/hyp.9312.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team, 2019: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/.

  • Raleigh, M. S., J. D. Lundquist, and M. P. Clark, 2015: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework. Hydrol. Earth Syst. Sci., 19, 31533179, https://doi.org/10.5194/hess-19-3153-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raleigh, M. S., B. Livneh, K. Lapo, and J. D. Lundquist, 2016: How does availability of meteorological forcing data impact physically based snowpack simulations? J. Hydrometeor., 17, 99120, https://doi.org/10.1175/JHM-D-14-0235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rango, A., 2006: Snow: The real water supply for the Rio Grande Basin. New Mexico J. Sci., 44, 99118.

  • Reba, M. L., J. Pomeroy, D. Marks, and T. E. Link, 2012: Estimating surface sublimation losses from snowpacks in a mountain catchment using eddy covariance and turbulent transfer calculations. Hydrol. Processes, 26, 36993711, https://doi.org/10.1002/hyp.8372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Regonda, S. K., B. Rajagopalan, M. Clark, and J. Pitlick, 2005: Seasonal cycle shifts in hydroclimatology over the western United States. J. Climate, 18, 372384, https://doi.org/10.1175/JCLI-3272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumsey, C. A., M. P. Miller, and G. A. Sexstone, 2020: Relating hydroclimatic change to streamflow, baseflow, and hydrologic partitioning in the Upper Rio Grande Basin, 1980 to 2015. J. Hydrol., 584, 124715, https://doi.org/10.1016/j.jhydrol.2020.124715.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., M. P. Clark, R. L. Armstrong, D. A. McGinnis, and R. S. Pulwarty, 1999: Characteristics of the western United States snowpack from Snowpack Telemetry (SNOTEL) data. Water Resour. Res., 35, 21452160, https://doi.org/10.1029/1999WR900090.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sexstone, G. A., 2020: SnowModel simulations and supporting observations for the Rio Grande Headwaters (1984 - 2017). U.S. Geological Survey, https://doi.org/10.5066/P9Q8PYX1.

    • Crossref
    • Export Citation
  • Sexstone, G. A., and S. R. Fassnacht, 2014: What drives basin scale spatial variability of snowpack properties in northern Colorado? Cryosphere, 8, 329344, https://doi.org/10.5194/tc-8-329-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sexstone, G. A., D. W. Clow, D. I. Stannard, and S. R. Fassnacht, 2016: Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain. Hydrol. Processes, 30, 33733389, https://doi.org/10.1002/hyp.10864.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sexstone, G. A., D. W. Clow, S. R. Fassnacht, G. E. Liston, C. A. Hiemstra, J. F. Knowles, and C. A. Penn, 2018: Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming. Water Resour. Res., 54, 11911211, https://doi.org/10.1002/2017WR021172.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skiles, S. M., M. Flanner, J. M. Cook, M. Dumont, and T. H. Painter, 2018: Radiative forcing by light-absorbing particles in snow. Nat. Climate Change, 8, 964971, https://doi.org/10.1038/s41558-018-0296-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sproles, E. A., A. W. Nolin, K. Rittger, and T. H. Painter, 2013: Climate change impacts on maritime mountain snowpack in the Oregon Cascades. Hydrol. Earth Syst. Sci., 17, 25812597, https://doi.org/10.5194/hess-17-2581-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stewart, I. T., 2009: Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol. Processes, 23, 7894, https://doi.org/10.1002/hyp.7128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USFS, 2016: Insect and Disease Detection Survey. Forest Service, USDA, https://www.fs.fed.us/foresthealth/applied-sciences/mapping-reporting/detection-surveys.shtml.

  • Veatch, W., P. D. Brooks, J. R. Gustafson, and N. Molotch, 2009: Quantifying the effects of forest canopy cover on net snow accumulation at a continental, mid-latitude site. Ecohydrology, 2, 115128, https://doi.org/10.1002/eco.45.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vionnet, V., E. Martin, V. Masson, C. Lac, F. N. Bouvet, and G. Guyomarc’h, 2017: High-resolution large eddy simulation of snow accumulation in alpine terrain. J. Geophys. Res. Atmos., 122, 11 00511 021, https://doi.org/10.1002/2017JD026947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming and earlier spring increase western U.S. forest wildfire activity. Science, 313, 940943, https://doi.org/10.1126/science.1128834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
  • Zambrano-Bigiarini, M., 2017: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series, version 0.3-10. R package, http://hzambran.github.io/hydroGOF/.

  • Zeng, X. B., P. Broxton, and N. Dawson, 2018: Snowpack change from 1982 to 2016 over conterminous United States. Geophys. Res. Lett., 45, 12 94012 947, https://doi.org/10.1029/2018GL079621.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

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  • 1 Colorado Water Science Center, U.S. Geological Survey, Denver, Colorado
  • | 2 Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado
  • | 3 Portland State University, Portland, Oregon
  • | 4 New Mexico Water Science Center, U.S. Geological Survey, Albuquerque, New Mexico
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Abstract

This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0077.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Graham A. Sexstone, sexstone@usgs.gov

Abstract

This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0077.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Graham A. Sexstone, sexstone@usgs.gov

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