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PI-Submitted Research Highlights for
Terrestrial Ecosystem Science Program

Using Remotely Sensed Data To Advance Streamflow Forecasts In Subarctic Watersheds

Katrina E. Bennett

Highlight

15 May 2019

MODIS fractional snow cover area improves streamflow modeling in undersampled regions of Alaska.

The Science 
In the remote and understudied boreal forest of interior Alaska, scientists funded by the Next-Generation Ecosystem Experiments (NGEE)–Arctic project applied remotely sensed snow cover observations to improve snowmelt and streamflow forecasting in river basins with spatially and temporally sparse gaging networks.

The Impact
This paper highlights the challenges of modeling in subarctic environments through assimilating snow remote sensing data with the discovery that assimilation improves streamflow forecasts in undermonitored systems. The implications of this work have great value for streamflow forecasting and indicate the utility of the remotely sensed fractional snow cover data in the subarctic. Additionally, their improvements to a widely used snow model increase robustness of the hydrological simulations, in support of the U.S. National Weather Service's move toward a physically based National Water Model.

Summary
This study seeks to integrate two different strains of the Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed fractional snow cover area observations into the Alaska Pacific River Forecast Center’s modeling framework and analyze the results in four watersheds located nearby Fairbanks, Alaska. This analysis revealed that in well-instrumented systems, such as the Chena River basin, streamflow forecasts were unchanged by the data assimilation. However, for basins with poorly observed precipitation and streamflow, such as the Chatanika River, improving observations of fractional snow cover extent in the models led to a significantly better forecast of streamflow. Because systems in the Arctic are largely undermonitored, the Chatanika is representative of the challenge in understanding the hydrology of northern rivers, for which improvements in streamflow forecasting are badly needed to mitigate and plan for a changing north.

Contacts
BER Program Manager
Daniel Stover
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Terrestrial Ecosystem Science
Daniel.Stover@science.doe.gov (301-903-0289)

Principal Investigators
Katrina E. Bennett
Los Alamos National Laboratory, Earth and Environmental Sciences, Hydrologist and Team Leader
kbennett@lanl.gov

Jessica E. Cherry
Alaska Pacific River Forecast Center
jessica.cherry@noaa.gov

Funding
Alaska Climate Science Center, the Natural Science and Engineering Research Council of Canada, the GOES-R High Latitude Proving Ground award NA08OAR432075, and the Next-Generation Ecosystem Experiments (NGEE)–Arctic project of the Office of Biological and Environmental Research within the U.S. Department of Energy Office of Science.

Publications
Bennett, K.E., J.E. Cherry, B. Balk, and S. Lindsey. “Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska.” Hydrology and Earth Systems Sciences 23, 2439–59 (2019). [DOI:10.5194/hess-23-2439-2019]

Funded by the Alaska Climate Science Center, the Natural Science and Engineering Research Council of Canada, the GOES-R High Latitude Proving Ground award NA08OAR432075, and the DOE Office of Science, Biological and Environmental Research program, Next Generation Ecosystem Experiment, NGEE-Arctic project.  

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