Articles | Volume 12, issue 5
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 Creative Commons Attribution 4.0 License.
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 Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2018
Research article |
| 03 May 2018
| 03 May 2018
Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
Earth Research Institute, University of California, Santa Barbara, CA 93106-3060, USA
Andre Abreu Calfa
Department of Computer Science, University of California, Santa Barbara, CA 93106-5110, USA
now at: Arista Networks, Santa Clara CA 95054, USA
Karl Rittger
National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309-0449, USA
Jeff Dozier
Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, USA
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- Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan J. Ahmad et al. 10.1109/JSTARS.2021.3102965
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- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. 10.1080/17538947.2023.2299208
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- Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction A. Meyal et al. 10.3389/frwa.2020.574917
- Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska T. Douglas & C. Zhang 10.1088/1748-9326/ac04d8
- Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran H. Ghanjkhanlo et al. 10.1007/s11629-018-4875-8
- Distributed modelling of snow and ice melt in the Naltar Catchment, Upper Indus basin M. Usman Liaqat & R. Ranzi 10.1016/j.jhydrol.2024.131935
- Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data H. Elyoussfi et al. 10.1016/j.ejrh.2024.102085
- Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy M. Pini et al. 10.1016/j.procs.2020.09.087
- Trends in Snow Cover Duration Across River Basins in High Mountain Asia From Daily Gap-Filled MODIS Fractional Snow Covered Area C. Ackroyd et al. 10.3389/feart.2021.713145
- Interannual snow accumulation variability on glaciers derived from repeat, spatially extensive ground-penetrating radar surveys D. McGrath et al. 10.5194/tc-12-3617-2018
- Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
- Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements P. Broxton et al. 10.1029/2018WR024146
- Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. 10.1109/JSTARS.2025.3562216
Latest update: 30 Jun 2025
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...
