Articles | Volume 12, issue 5
The Cryosphere, 12, 1579–1594, 2018
The Cryosphere, 12, 1579–1594, 2018

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. Bair et al.

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Cited articles

Adam, J. C., Clark, E. A., Lettenmaier, D. P., and Wood, E. F.: Correction of global precipitation products for orographic effects, J. Climate, 19, 15–38,, 2006.
Bair, E. H., Dozier, J., Davis, R. E., Colee, M. T., and Claffey, K. J.: CUES – A study site for measuring snowpack energy balance in the Sierra Nevada, Front. Earth Sci., 3, 58,, 2015.
Bair, E. H., Rittger, K., Davis, R. E., Painter, T. H., and Dozier, J.: Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory, Water Resour. Res., 52, 8437–8460,, 2016.
Bair, E. H., Rittger, K., and Dozier, J.: Reconstructed SWE for MODIS tile h23v05, calendar years 2003–2012, data available at:, last access: 1 June 2016.
Barrett, A.: National Operational Hydrologic Remote Sensing Center SNOw Data Assimiliation System (SNODAS) products at NSIDC, National Snow and Ice Data Center, Boulder, Special Report 11, 19, 2003.
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.