Earth Observation-based Irrigation Dynamics to enhance Integrated Surface–Subsurface Hydrological Models

Sustainable groundwater management in irrigated agricultural regions demands accurate quantification of human-induced water fluxes. In the high Venetian plain (Northeast Italy), irrigation represents both a crucial agricultural input and a significant source of aquifer recharge. However, growing efforts to enhance irrigation efficiency for river ecosystem preservation may inadvertently reduce groundwater replenishment. This study addresses the pressing need to enhance integrated surface–subsurface hydrological models (ISSHMs) by improving the representation of irrigation practices and crop water use.

Our approach integrates detailed spatio-temporally variable irrigation water (IW) flux estimates into the CATHY (CATchment HYdrology) model, constrained by Earth Observation (EO) data. Specifically, the IW fluxes are obtained through a water balance approach that combines daily meteorological and NDVI data at 250-m spatial resolution. They are then used for model forcing, whereas model verification is carried out through comparison with different soil moisture remote sensing products. The aim of this study is to reduce uncertainty in water balance components and better simulate surface–groundwater interactions in agriculturally intensive landscapes.

We demonstrate that the integration of EO-driven irrigation estimates into ISSHMs provides a robust framework for evaluating the trade-offs between irrigation efficiency and aquifer recharge. The resulting models can provide critical insights into water use dynamics under varying regulatory and climatic scenarios, thus supporting more informed water governance strategies across multiple sectors.