[1] Recent observations have documented declining snow water equivalent (SWE) and earlier melt in the coastal Cascade and Sierra Nevada mountain ranges, and climate models suggest that warming temperatures will decrease snowpack storage in the higher-elevation mountain ranges of interior western North America. To date, however, observations of changing SWE or snowmelt have been limited to the state of Colorado in the intermountain west (IMW), defined here as the Rio Grande, Colorado River, and Great Basins, which supply water to the driest regions of North America. We used daily SNOTEL data collected between 1984 and 2009 combined with the nonparametric regional Kendall test to demonstrate significant and widespread changes in the duration of snow cover in these river basins. Daily SNOTEL data demonstrated that basin average maximum SWE occurred as early as 7 March (Lower Colorado River Basin) and as late as 13 April (Upper Colorado, Yampa, and White River Basins). Although significant increases in winter temperature (T) were widespread, there were minimal changes in the day of maximum accumulation and no indications from SWE to winter precipitation ratios (SWE:P) and winter T observations that a transition from snow to rain had occurred. While there was little change in day of maximum accumulation, the duration of snow cover decreased in 11 of 13 drainage regions, and snowmelt center of mass (SM50) advanced 1 to 4 days per decade in 6 of 13 regions. There were significant trends toward a faster SM50 and shorter duration of snow cover in the highest-elevation regions (>2800 m) of the Colorado River Basin, suggesting that winter T and P may not be the primary driver of change. Our results show that the IMW hydroclimate is both spatially and temporally variable, with few changes in winter T and P in the Great Basin and drier and warmer winters in the Colorado River and Rio Grande Basins. The changes in snowmelt timing also were variable, with a shorter SM50 and less maximum SWE in the Colorado River and Rio Grande Basins. The variable response of snowpacks in the IMW to widespread warming highlights the need for additional research into the mass and energy balance of these continental snowpacks.Citation: Harpold, 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,

Swings from snow drought to extreme winter rainfall make managing reservoirs, like the Oroville Dam, incredibly difficult. But what exactly is "snow drought"?

Abstract. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.

Predictions of hydrologic system response to natural and anthropogenic forcing are highly uncertain due to the heterogeneity of the land surface and subsurface. Landscape heterogeneity results in spatiotemporal variability of hydrological states and fluxes, scale‐dependent flow and transport properties, and incomplete process understanding. Recent community activities, such as Prediction in Ungauged Basins of International Association of Hydrological Sciences, have recognized the impasse current catchment hydrology is facing and have called for a focused research agenda toward new hydrological theory at the watershed scale. This new hydrological theory should recognize the dominant control of landscape heterogeneity on hydrological processes, should explore novel ways to account for its effect at the watershed scale, and should build on an interdisciplinary understanding of how feedback mechanisms between hydrology, biogeochemistry, pedology, geomorphology, and ecology affect catchment evolution and functioning.

Abstract. The phase of precipitation as snow or rain controls numerous hydrologic processes that are fundamental to effective hydrological modeling. Despite its foundational importance to terrestrial hydrology, typical phase prediction methods (PPM) use overly simplistic estimates based on near-surface air temperature. The review conveys the diversity of tools available for PPM in hydrological modeling and the advancements needed to improve predictions in complex terrain characterized by large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There are a wide range of options for field observations of precipitation phase, but a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require underlying assumptions and field validation before they are operational. We review the types and accuracy of common PPM to show accuracy is generally increased at finer time steps and by including humidity. One important tool for PPM development is atmospheric modeling, which offers numerous models and microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation phase observations. One important tool for PPM development is atmospheric modeling, which offers numerous models and microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation phase observations. The review concludes by describing key research gaps and recommendations to improve PPM. Recommendations include incorporate humidity information and atmospheric information into models, develop observation networks at high temporal resolutions, compare and validate different PPM, develop spatially resolved products, and characterize regional variability. PPM is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers with field hydrologists.