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 4×4 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 Full Text: PDF

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R² for each model was 0.88. Large R² and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.

In situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in situ and GRACE-derived comparisons occur, limited research considers agroecological dependencies. Herein, we examined differences in groundwater monitoring approaches (observed [in situ, O] vs. predicted [GRACE-derived, P]) within the Yazoo–Mississippi Delta (YMD), an agroecosystem in the southeastern USA. We compared variations in modeled groundwater hydrology, land cover, and irrigation dynamics of the YMD within the upper-quartile (UQ) area of interest (AOI) (highest groundwater levels) and lower-quartile (LQ) AOI (lowest groundwater levels) every year from 2008 to 2020. Spatially, OUQ and PUQ were in northern portions of the YMD, with the OLQ and PLQ in southern portions. Groundwater levels between OUQ:PUQ and OLQ:PLQ each had correlations > 0.85. Regarding land cover, most categories varied within ±2.50% between model estimates over time. Relatedly, we documented 14 instances where correlations between land use category and groundwater level were inverted across models (OLQ:PLQ (5), OUQ:OLQ (6), PUQ:PLQ (3)). Irrigation results were not statistically different among all models. Overall, our results highlight the importance of quantifying model incongruences for groundwater and land cover management.

Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R² values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions.