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Improving cold-region streamflow estimation by winter precipitation adjustment using passive microwave snow remote sensing datasets

Bibliographic Data

ID15548673
AuthorsDo‐Hyuk Kang (0000-0002-8764-8883, Goddard Space Flight Center, corresponding author), Kyungtae Lee (0000-0003-4670-7522, University of Maryland, College Park), E J Kim (0000-0001-6700-9833), Edward Kim (0000-0002-9828-4624, Goddard Space Flight Center)
Year2021
Volume16
Issue4
Pages044055-044055
Publication date2021-02-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/abe784
OpenAlexW3131321558
LanguageEN
References cited36

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

Atmospheric sciences · Climatology · Drainage basin · Forcing (mathematics · Geography · Meteorology · Precipitation · Snow · Snowmelt · Streamflow · Climate change and permafrost · Cryospheric studies and observations · Environmental Science · Precipitation Measurement and Analysis · Geology

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