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Using Remotely Sensed Data to Advance Streamflow Forecasts in Subarctic Watersheds
Published: May 21, 2019
Posted: October 22, 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.

he 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.

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 near 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 Arctic systems 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.

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

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

Jessica E. Cherry
Alaska Pacific River Forecast Center

Alaska Climate Science Center, Natural Science and Engineering Research Council of Canada, 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.

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 System Sciences 23(5), 2439–59 (2019). [DOI:10.5194/hess-23-2439-2019]

Topic Areas:

  • Research Area: Terrestrial Ecosystem Science
  • Research Area: Next-Generation Ecosystem Experiments (NGEE)

Division: SC-33.1 Earth and Environmental Sciences Division, BER


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