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Volume 12, issue 5
The Cryosphere, 12, 1579-1594, 2018
https://doi.org/10.5194/tc-12-1579-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 12, 1579-1594, 2018
https://doi.org/10.5194/tc-12-1579-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 May 2018

Research article | 03 May 2018

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

Edward H. Bair1, Andre Abreu Calfa2,a, Karl Rittger3, and Jeff Dozier4 Edward H. Bair et al.
  • 1Earth Research Institute, University of California, Santa Barbara, CA 93106-3060, USA
  • 2Department of Computer Science, University of California, Santa Barbara, CA 93106-5110, USA
  • 3National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309-0449, USA
  • 4Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, USA
  • anow at: Arista Networks, Santa Clara CA 95054, USA

Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14% bias and 46–48mm RMSE on average. Nash–Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

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Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from...
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