Inferring groundwater overdraft in data-scarce arid agro-ecosystems: A semi-empirical remote sensing framework applied to the Elfeija watershed, Morocco

Abstract

Introduction: Small-scale irrigated agriculture in arid regions relies heavily on unmetered groundwater, creating an "invisible pumping" threat to aquifer sustainability. Monitoring these diffuse withdrawals remains a critical challenge for water governance. This study proposes a synergistic multi-sensor remote sensing workflow to infer groundwater abstraction and assess its impact on local water stress using a semi-empirical thermal-phenological model. Methods: We leveraged high-resolution vegetation phenology (NDVI) from Sentinel-2 and thermal data (LST) from Landsat 8/9 over the Elfeija watershed, Morocco (2020-2024). By isolating the thermal-phenological anomaly (ΔLST) between irrigated plots and natural reference areas, we translated the surface cooling effect into evapotranspiration fluxes and pumping volumes. To ensure physical plausibility and avoid circularity, the approach was constrained by independent bottom-up agronomic benchmarks alongside a multi-tier cross-comparison against global models (MOD16, ERA5-Land, GLEAM), and a rigorous Monte Carlo uncertainty propagation integrating both parametric and recharge uncertainties. Results: The analysis reveals a distinct seasonal signature of irrigation. Probabilistic modeling indicates that the inferred annual pumping for 2022 (central estimates: 5.85–6.79 Mm³) strongly suggests a state of severe overdraft, with a high probability (P > 80%) of exceeding the estimated renewable aquifer recharge (4.70 Mm³). Furthermore, the framework successfully disentangled climatic triggers from anthropogenic forcing, capturing an anomalous, water-intensive late-season agricultural cycle in 2024. Discussion: While the lack of in-situ piezometric data limits absolute ground-truthing, the multi-source convergence (global models and local infrastructure data) robustly brackets the overdraft scenario. The proposed semi-empirical inference workflow provides a scalable, cost-effective framework for data-scarce regions, offering river basin agencies a proactive tool to identify over-extraction hotspots and negotiate sustainable water quotas.