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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- 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
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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
- 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
66 citations as recorded by crossref.
- Multi-sensor fusion using random forests for daily fractional snow cover at 30 m K. Rittger et al. 10.1016/j.rse.2021.112608
- Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models Q. Gu et al. 10.1016/j.jag.2024.104102
- 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
- A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients J. Yang et al. 10.1109/TGRS.2024.3479452
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- Binary and Fractional MODIS Snow Cover Mapping Boosted by Machine Learning and Big Landsat Data W. Luan et al. 10.1109/TGRS.2022.3198508
- High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model Y. Yang et al. 10.3390/rs14194931
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- Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo A. Gunnarsson & S. Gardarsson 10.3390/hydrology11010003
- 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
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- Estimating high-resolution snow depth over the North Hemisphere mountains utilizing active microwave backscatter and machine learning Z. Ni et al. 10.1016/j.jhydrol.2024.132203
- 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
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- A New Method to Simulate the Microwave Effective Snow Grain Size in the Northern Hemisphere Without Using Snow Depth Priors J. Yang et al. 10.1109/JSTARS.2024.3441817
- Midwinter Dry Spells Amplify Post‐Fire Snowpack Decline B. Hatchett et al. 10.1029/2022GL101235
- Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar V. Dharmadasa et al. 10.5194/tc-17-1225-2023
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
- Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning C. Xiong et al. 10.1109/LGRS.2022.3226204
- An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China Y. Wei et al. 10.3390/rs14061480
- 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
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- The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States E. Sumargo & D. Cayan 10.1029/2018WR022687
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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
- 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
- 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
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian 10.3390/su141811395
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
- High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut J. Meloche et al. 10.1002/hyp.14546
- Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al. 10.1016/j.envsoft.2024.106053
- 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
- Evaluating different machine learning algorithms for snow water equivalent prediction M. Vafakhah et al. 10.1007/s12145-022-00846-z
- 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
- How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? E. Bair et al. 10.5194/tc-17-2629-2023
- An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction E. Bair et al. 10.1029/2019WR024810
- Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps U. Mital et al. 10.1175/AIES-D-22-0010.1
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
- 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
- Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach D. Shao et al. 10.5194/essd-14-795-2022
- Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches K. Khosravi et al. 10.1007/s11600-022-00934-0
- 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
- Identifying and mapping the spatial distribution of regions prone to snowmelt flood hazards in the arid region of Central Asia: A case study in Xinjiang, China Y. Liu et al. 10.1111/jfr3.12947
- Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations K. Yang et al. 10.3389/feart.2023.1106621
- 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
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
- 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
- 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
Latest update: 14 Dec 2024
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...
