Uncertainty-aware irrigation scheduling based on probabilistic site-specific soil moisture predictions with SWIM2: A case study in Flanders

This study presents and evaluates a real-time decision support system (DSS) for site-specific irrigation scheduling based on soil moisture forecasting with SWIM2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model). The SWIM2 framework integrates a soil water balance model with in situ sensor data and soil moisture samples through Bayesian inverse modeling to generate probabilistic 10-day soil moisture forecasts. We assess the performance of the soil moisture forecasts and the irrigation DSS by integrating the model parameter ensemble with either deterministic or ensemble-based probabilistic weather forecasts, providing insights into their benefits and trade-offs in real-time irrigation management. Both approaches resulted in high detection rate and accuracy in predicting water stress triggering the irrigation threshold. The full ensemble yielded slightly better reliability at longer lead times whereas the probability distribution of the soil moisture predictions at short lead times was dominated by the SWIM2 parameter uncertainty. Simulation of different irrigation treatments using the calibrated SWIM2-based model illustrated and confirmed its potential for evaluating water use efficiency and crop response. Overall, this work illustrates the application and practical advantages of a probabilistic, ensemble-based modeling framework in supporting site-specific, data-informed irrigation strategies.