Dongyue Li

Characterizing the extreme 2015 snowpack deficit in the Sierra Nevada (USA) and the implications for drought recovery

Analysis of the Sierra Nevada (USA) snowpack using a new spatially distributed snow reanalysis data set, in combination with longer term in situ data, indicates that water year 2015 was a truly extreme (dry) year. The range-wide peak snow volume was characterized by a return period of over 600 years (95% confidence interval between 100 and 4400 years) having a strong elevational gradient with a return period at lower elevations over an order of magnitude larger than those at higher elevations. The 2015 conditions, occurring on top of three previous drought years, led to an accumulated (multiyear) snowpack deficit of ~ −22 km3, the highest over the 65 years analyzed. Early estimates based on 1 April snow course data indicate that the snowpack drought deficit will not be overcome in 2016, despite historically strong El Nino conditions. Results based on a probabilistic Monte Carlo simulation show that recovery from the snowpack drought will likely take about 4 years.

How much runoff originates as snow in the western United States, and how will that change in the future?

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 regions major reservoirs. We argue that substantial impacts on water supply are likely in a warmer climate.

Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation

This paper demonstrates improved retrieval of snow water equivalent (SWE) from spaceborne passive microwave measurements for the sparsely-forested Upper Kern watershed (511 km2) 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 (October 1st to April 1st) with the assimilation of the measurements in the dry snow season (December 1st to February 28th). 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 April 1st SWE estimates by 80.9% and 45.4%, respectively. The EnBS is expected to work well above treeline and for dry snow. This article is protected by copyright. All rights reserved.

Large-Scale High-Resolution Modeling of Microwave Radiance of a Deep Maritime Alpine Snowpack

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 (Tb) at a spatial resolution of 90 m. The modeled SWE and Tb 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.

Quantifying Spatiotemporal Variability of Controls on Microwave Emission From Snow-Covered Mountainous Regions

Using passive microwave (PM) remote sensing to estimate snow water equivalent (SWE) in mountainous areas is challenging, because the interactions between the mountain environmental variables and microwave emission are very complex; a better understanding of these interactions will be helpful to ultimately improve the estimation of mountain SWE with PM. In this study, we performed an analysis of the 36.5-GHz vertical polarization (V-Pol) microwave radiance over the Upper Kern basin in the southern Sierra Nevada that was simulated for a previous study, to interpret the spatiotemporal radiance variability and its connection with the mountain environment. The modeled radiance is close to the advanced microwave scanning radiometer for EOS (AMSR-E) observed radiance; the RMSE of the modeled dry season basin-scale brightness temperature (Tb) is 3.1 K. The modeling was conducted at a high spatial resolution (90 m) to better characterize the significant radiance variability as a result of the complex mountainous terrain. A set of environmental variables has been explicitly parameterized into each modeling pixel; these environmental variables include elevation, fractional vegetation coverage, SWE, snow grain size, aspect, and slope. We correlated these environmental variables with the modeled microwave radiance to explore the effects of each environmental control, and to quantify each variables explanatory power in terms of the radiance variability. In this study, all the correlation calculations are statistically significant with confidence level higher than 95%. We found that the controlling power of each environmental factor varies over a snow season, and the dominating environmental factors change with respect to increase in snow accumulation. Vegetation and elevation, and SWE and grain size are two sets of dominating factors for the radiance: when SWE is less than 0.01 m, vegetation and elevation explain 55% of the radiance variability, while SWE and grain size explain 29% of the spatial variability. When SWE exceeds 0.01 m, SWE and grain size take over the dominance and explain 56% of the radiance variability, while vegetation and elevation explain 25%. The distribution of snow grain size over the Upper Kern exhibits large spatial variability. Snowpack temperature gradient, snow age, and wet metamorphism are significant factors related to snow grain growth. Snow age and temperature gradient are equally dominant for spatial patterns of grain size and Tb in the areas with deep and dry snow cover, while snow age and wet metamorphism dominate the grain size and Tb in the low elevation areas with shallow snow.

Interpreting the remotely sensed microwave radiance FROm snow covered mountains via a high-resolution modeling framework

Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due to the complex interaction between the microwave radiance and the mountain environment; a better knowledge of the interaction is requisite to improve the characterization of mountain SWE via PM. In this study, we modeled the 36.5GHz V-Pol microwave radiance at 90m spatial resolution over the Upper Kern Basin. We replicated the AMSR-E observations with the modeling results, and used the modeled radiance to interpret the satellite observations, as well as to explore the impacts of the mountain environment on microwave radiance. We found snow grain size, model stratigraphic representation, liquid water content and intense snowfall event close related with the modeling accuracy; the calibration to these parameters reduced the error in the modeled radiance by 81%. We aggregated the modeled radiance to AMSR-E footprint scale using the antenna sampling pattern to facilitate the comparison between the modeled radiance and the AMSR-E observations. The RMSE of the basin-scale modeling was 3.1K during the dry snow season. Using the modeling results, we classified the environmental variables in terms of their influence on microwave radiance in the study area; from high to low: SWE (significant only when snowpack is deep), vegetation, elevation, aspect, slope.