Constraining irrigation simulation in Global Hydrological Model H08 using satellite-derived dynamic targets

Accurate simulation of irrigation water use is essential for quantifying human impacts on the global water cycle. Given that continuous large-scale in situ monitoring of irrigation is scarce, the fidelity of irrigation estimates relies heavily on how models represent soil-moisture deficits and management targets. In many global hydrological models (e.g., H08), irrigation demand is commonly computed using a soil-moisture deficit approach: water is applied to refill the soil when moisture levels fall below a prescribed target. However, this target is typically implemented as a static, empirically specified parameter. While computationally efficient, this practice introduces substantial uncertainty into simulated irrigation water use.

Here, we develop a satellite-based framework that utilizes observed surface soil moisture to constrain irrigation demand in hydrological models. We first construct a day-of-year climatology of satellite-derived surface soil moisture to capture multi-year mean irrigation conditions and management requirements. Subsequently, we employ a vertical extrapolation strategy to translate satellite-derived surface targets into a root-zone proxy compatible with the H08 model. We validate this strategy in non-irrigated regions before applying it to irrigated areas to enable dynamic, observation-constrained irrigation targets. Preliminary diagnostics indicate that this framework offers a practical pathway for integrating satellite soil-moisture data into H08, improving the spatial realism of irrigation demand and facilitating more consistent evaluations against independent benchmarks.