Open AccessImproving cold-region streamflow estimation by winter precipitation adjustment using passive microwave snow remote sensing datasets
Author(s) -
Dong-In Kang,
Kyungtae Lee,
Edward Kim
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abe784
Subject(s) - streamflow , snowmelt , snow , environmental science , forcing (mathematics) , precipitation , climatology , meteorology , drainage basin , atmospheric sciences , geology , geography , cartography
Winter precipitation estimations and spatially sparse snow observations are key challenges when predicting snowmelt-driven floods. An improvement in streamflow prediction is achieved in a snowmelt-dominant basin, i.e. the Red River Basin (RRB), by adjusting the amounts of snowfall through satellite-borne passive microwave observations of snow water equivalent (SWE). A snowfall forcing dataset is scaled to minimize the difference between simulated and observed SWE over the RRB. Advanced microwave scanning radiometer-E (AMSR-E) SWE products serve as the observed SWE to obtain the solution to the linear equation between the AMSR-E and the baseline (no snowfall-forcing adjustment) SWE to yield a multiplication factor ( M factor ). In the headwaters of the RRB in the United States, a Nash–Sutcliffe efficiency (NSE) of 0.74 is obtained against observed streamflow, with M factor -adjusted streamflow during the snowmelt seasons (January to April). The baseline streamflow simulation without M factor exhibits an NSE of 0.38 owing to an underestimated SWE.