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The Cryosphere An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/tc-2017-196
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
11 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).
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 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 Environ mental Science & Management, University of California, Santa Barbara, CA 93106-5131, USA
anow at: Arista Networks, Santa Clara, CA 95054, USA
Abstract. In many mountains, snowmelt provides most of the runoff. In Afghanistan, few ground-based measurements of the snow resource exist. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance, but reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE early in 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 3.125 km, 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 46–48 mm RMSE on average. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the methods can accurately estimate SWE during the snow season in remote mountains.

Citation: Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-196, in review, 2017.
Edward H. Bair et al.
Edward H. Bair et al.
Edward H. Bair et al.

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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 for 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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