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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-96
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 May 2018

Research article | 09 May 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Using MODIS estimates of fractional snow cover extent to improve streamflow forecasts in Interior Alaska

Katrina E. Bennett1,2,a, Jessica E. Cherry1,2,3, Ben Balk4, and Scott Lindsey3 Katrina E. Bennett et al.
  • 1International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, 99775 USA
  • 2Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, 99775 USA
  • 3Alaska Pacific River Forecast Center, Anchorage, Alaska, 99502 USA
  • 4AMEC Environment and Infrastructure, Boulder, Colorado, 80302 USA
  • acurrently at: Earth and Environmental Sciences, Los Alamos National Lab, Los Alamos, NM, 87545, U.S.A

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal watersheds and where climate change is leading to rapid shifts in watershed function. In this study, the operational framework employed by the US National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of snow cover extent (SCE) to determine if these data improve streamflow forecasts in Interior Alaskan river basins. Two versions of MODIS fractional SCE are tested in this study: the MODIS 10A1 (MOD10A1), and the MODIS Snow Cover Area and Grain size (MODSCAG) product. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced runs have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG SCE version provides moderate increases in skill, but is similar to the MOD10A1 results in these watersheds. The basins with the greatest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungaged systems throughout the high latitude regions of the globe, this result is of great value and indicates the utility of the MODIS SCE data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snow pack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically-based National Water Model. Physically-based models may be more capable of adapting to changing climates than statistical models tuned to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

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Data sets

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. T. H. Painter, K. Rittger, C. McKenzie, P. Slaughter, R. E. Davis, and J. Dozier https://doi.org/10.1016/j.rse.2009.01.001

MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6. D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1.006

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Short summary
Remotely sensed snow observations may improve operational forecasting in remote and under-monitored 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 observations can improve snow estimates and streamflow forecasting. This work provides direction for forecasters to implement remote sensing in their operations.
Remotely sensed snow observations may improve operational forecasting in remote and...
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