Spatio-temporal HYDRUS-1D soil water balance simulations as support for precision irrigation in North-Eastern Germany

Abstract

Accurate spatio-temporal information on the soil water balance is critical for an efficient and sustainable irrigation. Large effort requirements limit the applicability of complex simulations for precision irrigation. The spatially distributed application of one-dimensional models can reconcile the need for precise soil water balance simulations with the complexity of root-zone water flow processes. This study uses HYDRUS-1D to simulate the daily depth-specific (0 cm to 60 cm, in 10 cm increments) soil water balance from 1st April to 30th September 2021 (2022). Simulations at 70 m spatial resolution covered a 1600 ha farm in Mecklenburg-Western Pomerania, Germany. Results were validated against in-situ soil water content (SWC) and two remotely-sensed SWC data sets (“Soil Moisture Active Passive”, SMAP; Sentinel-1, S1-SWC). Further analysis explored crop-specific irrigation efficiencies and potential farm-scale water savings. Spatially distributed HYDRUS-1D simulations showed good accuracy compared to in-situ SWC (RMSEmean = 0.020 m3 m􀀀 3; MAEmean = 0.017 m3 m􀀀 3; R2mean = 0.676; bias = 􀀀 0.008 m3 m􀀀 3). The agreement with remotely-sensed SWC was moderate to weak (RMSEmean = 0.059 (0.150) m3 m􀀀 3, MAEmean = 0.049 (0.123) m3 m􀀀 3, R2mean = 0.208 (0.141), mean bias = 0.021 (0.108) m3 m􀀀 3 for SMAP (S1-SWC)). 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 total irrigation water; 2021) and 71,396.6 m³ (10.4 %; 2022). The proposed approach reduces the trade-offs between accurately representing the soil water balance in the root-zone and keeping the practical effort reasonable.