From Sensors to Irrigation Decisions: An Automated Data–Model Pipeline for Real-Time Soil Moisture Forecasting in Agriculture

Abstract. Increasing water scarcity and climate variability demand reliable, site-specific soil moisture information to guide irrigation and crop management decisions at the plot scale. Land surface models simulate the interactions between land, vegetation, and the atmosphere, and provide detailed information on hydrological states and fluxes. This study presents an automated, end-to-end modelling pipeline that connects real-time sensor observation, land surface modelling, data assimilation, and ensemble forecasts to deliver operational, site-specific soil moisture forecasts for agricultural stakeholders. The framework integrates in situ measurements from a Cosmic-Ray Neutron Sensor (CRNS) with the Community Land Model version 5 (CLM5) and the Parallel Data Assimilation Framework (PDAF). Observations are assimilated using an Ensemble Kalman Filter (EnKF) to constrain model states and reduce uncertainties. For forecasting, the system includes medium-range ensemble weather predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF), which provide probabilistic information on future atmospheric conditions by running multiple simulations with perturbed initial states. Applied at the Selhausen agricultural research site (Germany), the framework shows how data assimilation improves the agreement between simulated and observed soil moisture by approximately 20 % (e.g., reduced RMSE), and yields short-term forecasts with skill comparable to or exceeding the open-loop at short-to-intermediate lead times, with added value becoming particularly apparent when unresolved wetting or drying events occur later in the forecast horizon. By translating model outputs into threshold-based indicators, the system enables actionable insights for irrigation and field management, thereby bridging the gap between scientific modelling and practical agricultural decision-making. Overall, the proposed framework represents an important step towards an operational, site-specific decision-support tool for short-term irrigation and agricultural water management under uncertain weather conditions.