Supporting precision irrigation scheduling in the heterogeneous landscape of North-Eastern Germany by spatio-temporally distributed HYDRUS-1D soil water balance simulations and remote sensing data

Accurate spatio-temporal information on the soil water balance is critical for an efficient and sustainable irrigation. Recent irrigation scheduling approaches are often limited to a representation of (i) the local or point scale soil water balance by in-situ measurements, (ii) solely surface soil water contents at a coarse spatial resolution by microwave remote sensing technologies, or (iii) only selected components of the soil water balance by simple crop evapotranspiration models. To reconcile the need for accurate estimates of different components of the soil water balance with feasible effort, this study proposes the application of physically-based one-dimensional soil water balance models in a spatially-distributed manner.

The HYDRUS-1D software environment is applied at 70 m spatial resolution across a 1,600 ha study farm in Mecklenburg-Western Pomerania, Germany, with heterogeneous soil textures and different crops. Depth-specific (0 cm to 60 cm, in 10 cm increments) soil water balance simulations were conducted from 1^st April to 30^th September 2021 and 2022 to estimate the soil water content, plant available water content, infiltration, crop evapotranspiration, root water uptake, and deep percolation, at daily intervals. Simulated soil water contents were validated against in-situ measurements and two microwave remote sensing surface soil water content datasets (“Soil Moisture Active Passive”, SMAP; Sentinel-1, S1-SWC). Spatially distributed irrigation demands and irrigation timings at daily intervals, crop-specific irrigation efficiencies and potential farm-scale water savings are estimated using the simulated soil water balance to explore the contribution of this simulation framework for precision irrigation.

The average simulation performance metrices were Root Mean Square Error (RMSE) = 0.020 m³ m^-3, Mean Absolute Error (MAE) = 0.017 m³ m^-3, coefficient of determination (R²) = 0.676, and bias = -0.008 m³ m^-3, showing a good accuracy of spatially-distributed HYDRUS-1D simulations. The agreement with remotely-sensed data was moderate to weak (RMSE_mean = 0.059 (0.150) m³ m^-3, MAE_mean = 0.049 (0.123) m³ m^-3, R²_mean = 0.208 (0.141), mean bias = 0.021 (0.108) m³ m^-3 for SMAP (S1-SWC)). Average crop specific irrigation efficiencies were 65.0% (potato), 47.3% (wheat), 40.5% (rye), and 58.2% (sugar beet). Potential water savings amounted to 87,006.9 m³ (11.2 % of the applied irrigation water; 2021) and 71,396.6 m³ (10.4 %; 2022).

The proposed simulation framework offers an easy-to-adopt and physically-based foundation for the estimation of crop-specific irrigation demands and irrigation timings at high spatial resolution. Further accuracy improvements by using depth-specific remote-sensing derived soil water contents (“Soil Water Index”) for model calibration are under ongoing investigation.