Large-Scale Groundwater Monitoring in Brazil Assisted With Satellite-Based Artificial Intelligence Techniques

Here, we develop and test an artificial intelligence (AI)-based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground-based hydrogeological measurements from Brazil's Integrated Groundwater Monitoring Network at hundreds of wells distributed in 12 aquifers across the country. We tested model ensembles based on three AI approaches: Extreme Gradient Boost, Light Gradient Boosting Model and CatBoost, followed by a Linear Regression step. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the AI-based model's sensitivity to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage (GWS) estimates from the Global Land Data Assimilation System (GLDAS) are used as benchmark. A sensitivity analysis shows that the LR-based model ensemble is the best suited and to reproduce GWS change in all studied Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an root mean square error (RMSE) value of 2.68 cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in RMSE = 6.76 cm. Using our AI model outputs, we quantified the GWS change of two major aquifers, Urucuia and Bauru-Caiu & aacute;, over the past two decades: -31 and -6 km(3), respectively. Water loss is driven by a prolonged drought across most of the country and intensification of groundwater pumping for irrigation. This study demonstrates that combining satellite data and AI can be a cost-effective alternative to monitor poorly equipped aquifers at the continental scale, with possible global replicability.