Downscaling GRACE Data for Improved Groundwater Forecasting Using Artificial Neural Networks

Abdel Rahman M. Awawdeh; Hakan Yasarer; Zahra Ghaffari; Lance D. Yarbrough

doi:10.28991/CEJ-2025-011-02-01

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Abdel Rahman M. Awawdeh Civil Engineering Department, The University of Mississippi, University, 38677, MS,, United States https://orcid.org/0000-0003-3074-8143
Hakan Yasarer Civil Engineering Department, The University of Mississippi, University, 38677, MS,, United States
Zahra Ghaffari Department of Geology and Geological Engineering, The University of Mississippi, University, 38677, MS,, United States
Lance D. Yarbrough
ldyarbro@olemiss.edu
Department of Geology and Geological Engineering, The University of Mississippi, University, 38677, MS,, United States

Vol. 11 No. 2 (2025): February

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This study introduces a dual-phase approach utilizing Artificial Neural Networks (ANNs) to overcome the challenges of groundwater monitoring at regional scales. Traditional well-based methods provide limited spatial coverage, while GRACE satellite data, despite its value for large-scale hydrological analysis, suffers from low spatial resolution (~300 km), limiting its application for local-scale assessments. Existing downscaling methods such as geographically weighted regression and Random Forests are computationally intensive and often lack adaptability to complex groundwater systems. In this study, Phase 1 refines GRACE data using ANNs to achieve a 4í—4 km spatial resolution, addressing the resolution challenge for regional applications. Phase 2 integrates the downscaled GRACE data with groundwater well observations and climatic factors to predict groundwater levels with high accuracy (R² = 0.9885). This dual-phase framework demonstrates significant improvements over existing methods, providing an efficient and scalable solution for groundwater monitoring in hydrologically complex regions. The findings highlight the potential of machine learning to enhance groundwater resource management, particularly in addressing water scarcity and climate variability challenges.

Doi: 10.28991/CEJ-2025-011-02-01

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Publication Start Year:	2015
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Downscaling GRACE Data for Improved Groundwater Forecasting Using Artificial Neural Networks

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Hakan Yasarer, Civil Engineering Department, The University of Mississippi, University, 38677, MS,

Zahra Ghaffari, Department of Geology and Geological Engineering, The University of Mississippi, University, 38677, MS,

Lance D. Yarbrough, Department of Geology and Geological Engineering, The University of Mississippi, University, 38677, MS,

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