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

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 (RMSE mean = 0.020 m 3 m −3 ; MAE mean = 0.017 m 3 m −3 ; R 2 mean = 0.676; bias = −0.008 m 3 m −3 ). The agreement with remotely-sensed SWC was moderate to weak (RMSE mean = 0.059 (0.150) m 3 m −3 , MAE mean = 0.049 (0.123) m 3 m −3 , R 2 mean = 0.208 (0.141), mean bias = 0.021 (0.108) m 3 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. • HYDRUS-1D accurately (RMSE = 0.020 m 3 m −3 ) pictures in-field soil water dynamics. • Irrigation efficiency varied strongly (40.5–65.0 %) between different crops. • Decreasing irrigation by 11.7 % is sufficient for potatoes, wheat and sugar beets. • Soil water surpluses show potential for water savings of 79,201.8 m 3 per season.

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Publication Details

Journal
Agricultural Water Management
Published
2026-02-05
DOI
https://doi.org/10.1016/j.agwat.2026.110157
Primary Topic
Soil Moisture and Remote Sensing
Type
article
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article

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

Matthias Kunz, Julia Pöhlitz, Jan Lukas Wenzel, Christopher Conrad et al.
Agricultural Water Management
Soil Moisture and Remote Sensing
article

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

Matthias Kunz, Julia Pöhlitz, Jan Lukas Wenzel, Christopher Conrad, Talha Mahmood, Martin Volk
article en

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 (RMSE mean = 0.020 m 3 m −3 ; MAE mean = 0.017 m 3 m −3 ; R 2 mean = 0.676; bias = −0.008 m 3 m −3 ). The agreement with remotely-sensed SWC was moderate to weak (RMSE mean = 0.059 (0.150) m 3 m −3 , MAE mean = 0.049 (0.123) m 3 m −3 , R 2 mean = 0.208 (0.141), mean bias = 0.021 (0.108) m 3 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. • HYDRUS-1D accurately (RMSE = 0.020 m 3 m −3 ) pictures in-field soil water dynamics. • Irrigation efficiency varied strongly (40.5–65.0 %) between different crops. • Decreasing irrigation by 11.7 % is sufficient for potatoes, wheat and sugar beets. • Soil water surpluses show potential for water savings of 79,201.8 m 3 per season.

Agricultural Water ManagementVol. 325
Helmholtz Centre for Environmental Research (DE), GFZ Helmholtz Centre for Geosciences (DE), Martin Luther University Halle-Wittenberg (DE)
Responsible consumption and production
Openalex Percentile: Top 70%
Soil Moisture and Remote Sensing
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