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Reconstructing snowmelt runoff in the Yukon River basin using the SWEHydro model and AMSR‐E observations
Author(s) -
Ramage Joan,
Semmens Kathryn A.
Publication year - 2012
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.9226
Subject(s) - snowmelt , snow , environmental science , surface runoff , snowpack , water year , hydrology (agriculture) , meltwater , hydrograph , climatology , atmospheric sciences , drainage basin , geology , geomorphology , ecology , cartography , geotechnical engineering , geography , biology
Snowmelt timing and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) are used as inputs to the SWEHydro model to simulate spring snowmelt runoff in high‐latitude, snow‐dominated drainages. AMSR‐E data from 2003 to 2010 are used to determine the timing of melt onset and snow saturation on the basis of changes in brightness temperature ( T b ) and diurnal amplitude variations (DAV). Pre‐melt SWE data are combined with terrain information and melt rate estimates to calculate runoff. After melt onset, there is a ‘melt transition period’ with daytime melt and nocturnal refreeze. The melt transition is characterized by high T b oscillations (high DAV). At the end of high DAV, the snowpack is melting at a higher rate. The model uses four parameters: snowmelt rate during and after melt transition (defined by T b and DAV thresholds) and flow timing during and after melt transition. The model effectively simulates spring freshet, peak timing and magnitude, and volume (between days 50 and 180) in basins lacking sufficient meteorological measurements for conventional models. We compare the model response in the Pelly and Stewart Rivers, tributaries to the Yukon River, to evaluate model parameters in broadly similar basins under varying conditions. Simulated freshet timing is strongly related to snowmelt timing, and the modeled hydrograph is most sensitive to the flow timing parameter. This observationally based model has potential as a module for quantifying spring snowmelt runoff and timing in physically based models. Copyright © 2012 John Wiley & Sons, Ltd.