In the western United States, the seasonal phase of snow storage bridges between winter-dominant precipitation and summer-dominant water demand. The critical role of snow in water supply has been frequently quantified using the ratio of snowmelt-derived runoff to total runoff. However, current estimates of the fraction of annual runoff generated by snowmelt are not based on systematic analyses. Here based on hydrological model simulations and a new snowmelt tracking algorithm, we show that 53% of the total runoff in the western United States originates as snowmelt, despite only 37% of the precipitation falling as snow. In mountainous areas, snowmelt is responsible for 70% of the total runoff. By 2100, the contribution of snowmelt to runoff will decrease by one third for the western U.S. in the Intergovernmental Panel on Climate Change Representative Concentration Pathway 8.5 scenario. Snowmelt-derived runoff currently makes up two thirds of the inflow to the region's major reservoirs. We argue that substantial impacts on water supply are likely in a warmer climate.

Snow accumulation, ablation, and runoff in mountainous areas are critical components of the hydrologic cycle, but are poorly known. Passive microwave (PM) measurements are sensitive to snow water equivalent (SWE), even in mountain regions, but vegetation masks the microwave signals and reduces this sensitivity. This study examines how the PM snow signal is affected by the forest density in fourteen basins in the Sierra Nevada, USA, and in a series of sixteen subsets of the Kern basin that have varied vegetation density. 36.5 GHz vertical polarization brightness temperature (T-b) time series for each basin were produced from the spaceborne AMSR-E operational period (water year (WY) 2003 to WY2011). For each basin, the coefficient of determination (R-2) between the annual minimum Tb and the concurrent SWE was calculated to evaluate the sensitivity of the PM to SWE. The relationship between the R-2 values and the forest density was then analyzed to assess how vegetation affect the SWE information in the observed Tb. Mean forest coverage from MODIS was used to represent forest density. The R2 between the annual minimum Tb and concurrent SWE was >0.6 for three of the basins. Consistent with previous studies, WY2006 demonstrated anomalous Tb values for many basins, apparently due to anomalous warm winter rainfall. Excluding WY2006, R-2 is significantly higher in all basins: eight of fourteen basins have R-2 >0.6. For basins with average elevation >2500 m, SWE correlates well with Tb. The R-2 decreases monotonically with decreasing elevation. Basin elevation and forest cover are highly correlated in the Sierra; a basin elevation of 2500 m generally coincides with forest cover of 20%. A total of 42% of Sierra Nevada has <20% forest cover; this corresponds to an estimated 34% of the total SWE in the Sierra. Thus, SWE and Tb are empirically correlated for over a third of the SWE in the Sierra, typically at high elevations and above treeline. (C) 2017 Elsevier Inc. All rights reserved.

This paper demonstrates improved retrieval of snow water equivalent (SWE) from spaceborne passive microwave measurements for the sparsely forested Upper Kern watershed (511 km(2)) in the southern Sierra Nevada (USA). This is accomplished by assimilating AMSR-E 36.5 GHz measurements into model predictions of SWE at 90 m spatial resolution using the Ensemble Batch Smoother (EnBS) data assimilation framework. For each water year (WY) from 2003 to 2008, SWE was estimated for the accumulation season (1 October to 1 April) with the assimilation of the measurements in the dry snow season (1 December to 28 February). The EnBS SWE estimation was validated against snow courses and snow pillows. On average, the EnBS accumulation season SWE RMSE was 77.4 mm (13.1%, relative to peak accumulation), despite deep snow (average peak SWE of 545 mm). The prior model estimate without assimilation had an accumulation season average RMSE of 119.7 mm. After assimilation, the overall bias of the accumulation season SWE estimates was reduced by 84.2%, and the RMSE reduced by 35.4%. The assimilation also reduced the bias and the RMSE of the 1 April SWE estimates by 80.9% and 45.4%, respectively. The EnBS is expected to work well above tree line and for dry snow.

Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due in part to the large PM footprints and the high subgrid spatial variability of snow properties. In this paper, we linked the land surface model Simplified Simple Biosphere version 3.0 (SSiB3) with the radiative transfer model Microwave Emission Model of Layered Snowpacks, and we forced the coupled model with the disaggregated North American Data Assimilation System phase 2 (NLDAS-2) meteorological data to simulate the snow properties and the 36.5-GHz microwave brightness temperature (T-b) at a spatial resolution of 90 m. The modeled SWE and T-b were used to interpret the radiance observed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and to explore the impact of snow spatial variability on the microwave radiance in a mountain environment. The modeling was carried out over the Upper Kern Basin, Sierra Nevada. We developed new methods for modeling the effect of large snowfall events on the snow grain size. We aggregated the modeled radiance to the satellite scale using the AMSR-E 36.5-GHz antenna sampling pattern. The methods were calibrated for water years (WYs) 2004-2006 and validated for WYs 2003, 2007, and 2008. The coefficient governing the grain growth rate was also calibrated. The modeling results showed that the new snow grain estimation scheme reduced the error in the modeled radiance by 55.2% during the calibration period. The Tb root-mean-square error was 3.1 K during the snow accumulation season for the validation period. The modeling results showed that, in the study area, the microwave signal saturated for SWE values between 0.3 and 0.5 m. It was found that the subfootprint-scale SWE variability has a significant impact on the saturation of spaceborne PM observations. The experiments demonstrate that this modeling system improves the accuracy of the radiance modeling, which is critical for estimating the mountain SWE via PM remote sensing either for informing direct retrieval algorithms or for data assimilation. We plan to use the modeling framework in future radiance assimilation studies.