Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 23, issue 5
Articles | Volume 23, issue 5
https://doi.org/10.5194/hess-23-2439-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2439-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 5
Research article
 | 
21 May 2019
Research article |  | 21 May 2019

Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Katrina E. Bennett, Jessica E. Cherry, Ben Balk, and Scott Lindsey

Supplement

https://doi.org/10.5194/hess-23-2439-2019-supplement

Data sets

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. Snow Cover. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1.006

National Water Information System data available on the World Wide Web U.S. Geological Survey https://waterdata.usgs.gov/nwis/rt

Snow Water Equivalent (SWE) and Snow Depth Products National Resources Conservation Service https://www.wcc.nrcs.usda.gov/snow/snotel-wereports.html

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Short summary
Remotely sensed snow observations may improve operational streamflow forecasting in remote regions, such as Alaska. In this study, we insert remotely sensed observations of snow extent into the operational framework employed by the US National Weather Service’s Alaska Pacific River Forecast Center. Our work indicates that the snow observations can improve snow estimates and streamflow forecasting. This work provides direction for forecasters to implement remote sensing in their operations.
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