Articles | Volume 26, issue 15
https://doi.org/10.5194/hess-26-4169-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4169-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Aug 2022
Research article |
| 10 Aug 2022
| 10 Aug 2022
Evaluating downscaling methods of GRACE (Gravity Recovery and Climate Experiment) data: a case study over a fractured crystalline aquifer in southern India
Claire Pascal
CORRESPONDING AUTHOR
Centre d'Étude Spatiale de la BIOsphère, CESBIO-UPS-CNRS-IRD-CNES-INRAE, 18 av. Ed. Belin, Toulouse CEDEX 9, 31401, France
Sylvain Ferrant
Centre d'Étude Spatiale de la BIOsphère, CESBIO-UPS-CNRS-IRD-CNES-INRAE, 18 av. Ed. Belin, Toulouse CEDEX 9, 31401, France
Adrien Selles
Bureau de Recherches Géologiques et Minières (BRGM), Université de Montpellier, 1039 rue de Pinville, Montpellier, 34000, France
Jean-Christophe Maréchal
Bureau de Recherches Géologiques et Minières (BRGM), Université de Montpellier, 1039 rue de Pinville, Montpellier, 34000, France
Abhilash Paswan
National Geophysical Research Institute, CSIR, Hyderabad, India
Olivier Merlin
Centre d'Étude Spatiale de la BIOsphère, CESBIO-UPS-CNRS-IRD-CNES-INRAE, 18 av. Ed. Belin, Toulouse CEDEX 9, 31401, France
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Cited
13 citations as recorded by crossref.
- Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models H. Tao et al. 10.1016/j.atmosres.2023.106815
- Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network A. Nannawo et al. 10.1002/wwp2.12190
- Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests Y. Wang et al. 10.1016/j.jhydrol.2024.131708
- Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model I. Kalu et al. 10.1038/s41598-024-60366-2
- Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China J. Wang et al. 10.3390/rs15112913
- A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly G. Zhang et al. 10.1016/j.rse.2024.114359
- Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al. 10.1016/j.jhydrol.2024.132194
- Dealing with hydrologic data scarcity: the case of the Tindouf basin J. Gonçalvès et al. 10.5802/crgeos.202
- Analysis of spatio-temporal variability of groundwater storage in Ethiopia using Gravity Recovery and Climate Experiment (GRACE) data K. Arega et al. 10.1007/s12665-024-11508-2
- GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE S. Pulla et al. 10.3390/rs15092247
- HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al. 10.1016/j.jhydrol.2024.131591
- Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review V. Nourani et al. 10.1016/j.gsd.2024.101113
- HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al. 10.1016/j.jhydrol.2024.131591
12 citations as recorded by crossref.
- Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models H. Tao et al. 10.1016/j.atmosres.2023.106815
- Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network A. Nannawo et al. 10.1002/wwp2.12190
- Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests Y. Wang et al. 10.1016/j.jhydrol.2024.131708
- Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model I. Kalu et al. 10.1038/s41598-024-60366-2
- Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China J. Wang et al. 10.3390/rs15112913
- A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly G. Zhang et al. 10.1016/j.rse.2024.114359
- Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al. 10.1016/j.jhydrol.2024.132194
- Dealing with hydrologic data scarcity: the case of the Tindouf basin J. Gonçalvès et al. 10.5802/crgeos.202
- Analysis of spatio-temporal variability of groundwater storage in Ethiopia using Gravity Recovery and Climate Experiment (GRACE) data K. Arega et al. 10.1007/s12665-024-11508-2
- GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE S. Pulla et al. 10.3390/rs15092247
- HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al. 10.1016/j.jhydrol.2024.131591
- Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review V. Nourani et al. 10.1016/j.gsd.2024.101113
Latest update: 22 Nov 2024
Short summary
This paper presents a new validation method for the downscaling of GRACE (Gravity Recovery and Climate Experiment) data. It measures the improvement of the downscaled data against the low-resolution data in both temporal and, for the first time, spatial domains. This validation method offers a standardized and comprehensive framework to interpret spatially and temporally the quality of the downscaled products, supporting future efforts in GRACE downscaling methods.
This paper presents a new validation method for the downscaling of GRACE (Gravity Recovery and...
