12 citations as recorded by crossref.

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. Dealing with hydrologic data scarcity: the case of the Tindouf basin J. Gonçalvès et al. 10.5802/crgeos.202
8. 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
9. GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE S. Pulla et al. 10.3390/rs15092247
10. 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
11. 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
12. 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