In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (T_s) shows marked spatial variation correlated with relative humidity; the T_s values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.

Controlled field experiments to disentangle the effect of canopy density from the effect of climate on snowpack dynamics are limited by the underlying linkage between canopy density and climate. Thus, based on observations alone, it is not well understood how variations in canopy density can affect snow processes under different climate regimes. To address this knowledge gap, this study uses a physics‐based modeling approach to evaluate the sensitivity of snowpack dynamics to variations in canopy density across the climate gradients of the Western U.S. as represented by 228 Snow Telemetry (SNOTEL) sites. Within the model, we uniformly parameterize the canopy across sites to represent an idealized forest with high, medium, and low canopy density, respectively. The results illustrate that the effect of canopy density on the peak snow water equivalent (SWE) and duration of under‐canopy snowpack is sensitive to winter climate (i.e., climatological winter precipitation and temperature). As canopy density decreases, the greatest increase in peak SWE and snowpack duration is found in wet/warm and dry/cold climates, where snowpack under low‐density forest lasts longer than that in the open. In comparison, peak SWE and snowpack duration in wet/cold climates are less sensitive to changing canopy density. Thus, forest management actions (e.g., thinning and clearing) are likely to have disparate impacts on snow depending on local winter climate. Climate sensitivity of under‐canopy snowpack suggests that snowpack duration under dense canopy in presently warm winter climates is expected to experience the greatest reduction under a warming climate.

While the advective flux from cool melt runoff can be a significant source of thermal energy to mountainous rivers, it has been a much less addressed process in river temperature modeling and thus our understanding is limited with respect to the spatiotemporal effect of melt on river temperatures at the watershed scale. In particular, the extent and magnitude of the melt cooling effect in the context of a warming climate are not yet well understood. To address this knowledge gap, we improved a coupled hydrology and stream temperature modeling system, distributed hydrology soil vegetation model and river basin model (DHSVM-RBM), to account for the thermal effect of cool snowmelt runoff on river temperatures. The model was applied to a snow-fed river basin in the Pacific Northwest to evaluate the responses of snow, hydrology, stream temperatures, and fish growth potential to future climates. Historical simulations suggest that snowmelt can notably reduce the basin-wide peak summer temperatures particularly at high-elevation tributaries, while the thermal impacts of melt water can persist through the summer along the mainstem. Ensemble climate projections suggested that a warming climate will decrease basin mean peak snow and summer streamflow by 92% and 60% by the end of the century. Due to the compounded influences of warmer temperatures, lower flows and diminished cooling from melt, river reaches in high elevation snow-dominated areas were projected to be most vulnerable to future climate change, showing the largest increases in summer peak temperatures. As a result, thermal habitat used by anadromous Pacific salmon was projected to exhibit substantially lower growth potential during summer in the future. These results have demonstrated the necessity of accounting for snowmelt influence on stream temperature modeling in mountainous watersheds.

Water management activities affect the terrestrial water cycle by increasing water supply and redistributing the water and energy budgets over irrigated agricultural land. However, the dynamics of how water management activities interact with the terrestrial water and energy budgets have not been sufficiently studied over Indian subcontinental Basins. In this study, we applied a well‐evaluated water management model, coupled with a large‐scale land surface hydrological model with irrigation and reservoir operation schemes to examine the impacts of irrigation and reservoir operations on terrestrial water and energy budgets over the Indian subcontinental basins. We conducted two simulations: NATURAL (i.e., no irrigation and no reservoirs) and water management scenario (i.e., with irrigation and with reservoir regulations, referred to as MANAGED) over 18 basins from 1951 to 2012. The water management impacts were evaluated by comparing a number of key water and energy budget terms such as evapotranspiration (ET), total runoff, land surface temperature (LST), latent heat flux (LHF), sensible heat flux (SHF), reservoir storage, and discharge. Our results show that water management activities have significant (pvalue <0.05) impacts on ET, LHF, and LST in most of the river basins during the pre‐monsoon season (February–May). Comparing to the NATURAL scenario, ET and LHF under the MANAGED scenario are significantly increased in most basins during pre‐monsoon and post‐monsoon seasons, especially in semiarid regions. Therefore, the influence of irrigation on water and energy budget can be an overestimation in comparison to the constrained irrigation, which is a more realistic scenario.