Abstract
Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Nash–Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.
Highlights
Accurate estimates of snow water equivalent (SWE) in mountain watersheds pose a longstanding, unsolved problem. Lettenmaier et al (2015) note that “retrieval of snow water equivalent from space remains elusive especially in mountain areas” and argue that “this area deserves more strategic thinking from the hydrology community.” Dozier et al (2016) identify five approaches to the problem, but point out that all are problematic in some way
Collinearity does not degrade the performance of the models, but makes assessment of the importance of the correlated predictors independently more difficult (Dormann et al, 2013), an issue we address in the results
Given that the bias only ranged from 0–1 % for the bagged trees and was 0 % for the neural networks, it is clear that the higher root mean squared error (RMSE) is the result of different mean SWE in the basins
Summary
Accurate estimates of snow water equivalent (SWE) in mountain watersheds pose a longstanding, unsolved problem. Lettenmaier et al (2015) note that “retrieval of snow water equivalent from space remains elusive especially in mountain areas” and argue that “this area deserves more strategic thinking from the hydrology community.” Dozier et al (2016) identify five approaches to the problem, but point out that all are problematic in some way. Accurate estimates of snow water equivalent (SWE) in mountain watersheds pose a longstanding, unsolved problem. Lettenmaier et al (2015) note that “retrieval of snow water equivalent from space remains elusive especially in mountain areas” and argue that “this area deserves more strategic thinking from the hydrology community.” Dozier et al (2016) identify five approaches to the problem, but point out that all are problematic in some way. April to July runoff forecasts in the well instrumented American River Basin, in California’s Sierra Nevada, have a median error of 18 % and a 90th percentile error (1 year out of 10) exceeding 60 % (Dozier, 2011). Uncertainty stems from the heterogeneous distribution of mountain snow. Low snowpack years lead to humanitarian crises with little warning, as rivers and streams run dry in the fall and crops fail (e.g., USAID, 2008)
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