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
27 citations as recorded by crossref.
- Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network A. Nannawo et al.
- Understanding groundwater dynamics of the depleted aquifer in the Indus Basin based on the downscaled GRACE data and groundwater modelling A. Khan et al.
- Terrestrial water storage changes in the Bug river transboundary catchment observed by GRACE and water balance analysis T. Solovey et al.
- A dataset of high-resolution groundwater storage changes in the Yellow River Basin (2003–2023) Y. XIE et al.
- A novel spatial downscaling algorithm based on deep learning considering geographical spatial heterogeneity and nonlinear changes: a case study of the Yangtze River Basin C. Luo et al.
- 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.
- Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al.
- Dealing with hydrologic data scarcity: the case of the Tindouf basin J. Gonçalvès et al.
- Analysis of spatio-temporal variability of groundwater storage in Ethiopia using Gravity Recovery and Climate Experiment (GRACE) data K. Arega et al.
- HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al.
- A dynamic soft-constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage M. Uz et al.
- Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model I. Kalu et al.
- Understanding source sustainability in hard rock aquifers using Gravity Recovery and Climate Experiment: A case study from Telangana, India A. Paswan et al.
- Assessment of the Effectiveness of GRACE Observations in Monitoring Groundwater Storage in Poland T. Solovey et al.
- GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE S. Pulla et al.
- Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models H. Tao et al.
- Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests Y. Wang et al.
- A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences S. Mousavimehr & M. Kavianpour
- Machine learning downscaling of GRACE satellite data for local-scale water storage assessment in South-East Queensland, Australia M. Chahal et al.
- 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.
- Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions Y. Hamou-Ali et al.
- Anthropogenic and Climate-Induced Water Storage Dynamics over the Past Two Decades in the China–Mongolia Arid Region Adjacent to Altai Mountain Y. Yan et al.
- A holistic overview of the applications of GRACE-observed terrestrial water storage in hydrology and climate science B. Khorrami & O. Gündüz
- Evaluation of GRACE and GRACE-FO derived-products for water storage assessment in Moroccan aquifers: analysis of drought and human-induced impacts M. Abbad et al.
- High-Resolution Downscaling of GRACE-Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data-Scarce Tropical Catchments S. Karunarathna et al.
- Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review V. Nourani et al.
- A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle V. Varma & F. Gandhi
27 citations as recorded by crossref.
- Analyzing groundwater storage anomalies in data‐scarce areas of Ethiopia's Rift Valley Basin using artificial neural network A. Nannawo et al.
- Understanding groundwater dynamics of the depleted aquifer in the Indus Basin based on the downscaled GRACE data and groundwater modelling A. Khan et al.
- Terrestrial water storage changes in the Bug river transboundary catchment observed by GRACE and water balance analysis T. Solovey et al.
- A dataset of high-resolution groundwater storage changes in the Yellow River Basin (2003–2023) Y. XIE et al.
- A novel spatial downscaling algorithm based on deep learning considering geographical spatial heterogeneity and nonlinear changes: a case study of the Yangtze River Basin C. Luo et al.
- 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.
- Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al.
- Dealing with hydrologic data scarcity: the case of the Tindouf basin J. Gonçalvès et al.
- Analysis of spatio-temporal variability of groundwater storage in Ethiopia using Gravity Recovery and Climate Experiment (GRACE) data K. Arega et al.
- HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al.
- A dynamic soft-constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage M. Uz et al.
- Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model I. Kalu et al.
- Understanding source sustainability in hard rock aquifers using Gravity Recovery and Climate Experiment: A case study from Telangana, India A. Paswan et al.
- Assessment of the Effectiveness of GRACE Observations in Monitoring Groundwater Storage in Poland T. Solovey et al.
- GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE S. Pulla et al.
- Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models H. Tao et al.
- Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests Y. Wang et al.
- A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences S. Mousavimehr & M. Kavianpour
- Machine learning downscaling of GRACE satellite data for local-scale water storage assessment in South-East Queensland, Australia M. Chahal et al.
- 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.
- Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions Y. Hamou-Ali et al.
- Anthropogenic and Climate-Induced Water Storage Dynamics over the Past Two Decades in the China–Mongolia Arid Region Adjacent to Altai Mountain Y. Yan et al.
- A holistic overview of the applications of GRACE-observed terrestrial water storage in hydrology and climate science B. Khorrami & O. Gündüz
- Evaluation of GRACE and GRACE-FO derived-products for water storage assessment in Moroccan aquifers: analysis of drought and human-induced impacts M. Abbad et al.
- High-Resolution Downscaling of GRACE-Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data-Scarce Tropical Catchments S. Karunarathna et al.
- Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review V. Nourani et al.
- A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle V. Varma & F. Gandhi
Saved (final revised paper)
Latest update: 04 May 2026
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...
