Deep Learning Based Modeling of Groundwater Storage Change

Abstract

The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003–2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003–2020 with a rate ranging from −5.88 ± 1.2 mm/year to −14.12 ± 1.2 mm/year and −3.5 ± 1.5 to −10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from −7.78 ± 1.2 to −15.6 ± 1.2 for TWSC and −4.97 ± 1.5 to −12.21 ± 1.5 for GWSC from 2020–2025. An interesting observation was a minor increase in rainfall during the study period for three basins.