Articles | 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 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.
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. Bair et al.
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34 citations as recorded by crossref.
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- 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
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- Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff T. Niedzielski et al. 10.1016/j.jhydrol.2019.124046
34 citations as recorded by crossref.
- Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach J. Yang et al. 10.5194/tc-14-1763-2020
- Artificial intelligence in the water domain: Opportunities for responsible use N. Doorn 10.1016/j.scitotenv.2020.142561
- Multi-sensor fusion using random forests for daily fractional snow cover at 30 m K. Rittger et al. 10.1016/j.rse.2021.112608
- Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach J. Wang et al. 10.1016/j.jhydrol.2020.124828
- Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset Y. Liu et al. 10.5194/tc-15-5261-2021
- Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges J. Du et al. 10.3390/rs11161952
- High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area M. Liu et al. 10.3390/rs12030460
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
- Identifying climate change impacts on surface water supply in the southern Central Valley, California Z. Liu et al. 10.1016/j.scitotenv.2020.143429
- Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach J. Zhang et al. 10.1088/1748-9326/abfe8d
- Global snow drought hot spots and characteristics L. Huning & A. AghaKouchak 10.1073/pnas.1915921117
- Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations M. Durand et al. 10.1029/2021AV000455
- An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction E. Bair et al. 10.1029/2019WR024810
- Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI J. Yang et al. 10.3390/rs11080977
- Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
- A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
- Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan E. Bair et al. 10.5194/tc-14-331-2020
- Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent K. Yang et al. 10.1016/j.advwatres.2021.104075
- 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
- Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM N. Ma et al. 10.1016/j.jhydrol.2020.125221
- Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia J. Ahmad et al. 10.3389/feart.2019.00212
- Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model A. Teweldebrhan et al. 10.5194/hess-24-4641-2020
- Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China J. Yang et al. 10.1016/j.jhydrol.2020.125499
- 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
- Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy M. Pini et al. 10.1016/j.procs.2020.09.087
- Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach J. Yang et al. 10.1016/j.rse.2021.112630
- 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
- Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping K. Rittger et al. 10.1029/2019WR024914
- The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States E. Sumargo & D. Cayan 10.1029/2018WR022687
- Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements P. Broxton et al. 10.1029/2018WR024146
- Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff T. Niedzielski et al. 10.1016/j.jhydrol.2019.124046
Latest update: 05 Dec 2021
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...
