Fallah-Mehdipour et al. (2026) Evaluation of agro-hydrological subseasonal-to-seasonal forecasts for tactical decision making in crop irrigation

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-03-30
• Authors: Elahe Fallah-Mehdipour, Jörg Dietrich
• DOI: 10.1016/j.agwat.2026.110315

Research Groups

• Institute for Hydrology and Water Resources Management, Leibniz University Hannover, Germany

Short Summary

This study investigates the utility of sub-seasonal to seasonal (S2S) forecasts (up to six weeks lead time) for estimating future crop irrigation at the field scale. It found that while forecast accuracy decreases with longer lead times, using a reliability-weighted median of S2S forecasts can potentially reduce irrigation water use and maintain or enhance crop water productivity in irrigated years.

Objective

• To investigate the use of sub-seasonal to seasonal (S2S) forecasts with lead times of up to six weeks for estimating future irrigation at the field scale.
• To test the hypothesis that climate forecast products can enhance crop water productivity (WPc) as an indicator for sustainable agricultural water management practices.

Study Configuration

• Spatial Scale: Field scale (experimental field in Hamerstorf, Lower Saxony, Northern Germany).
• Temporal Scale: Sub-seasonal to seasonal (up to six weeks lead time), with forecasts updated weekly. The study period covers 2006–2022, focusing on the irrigation season (April to September).

Methodology and Data

• Models used:
  • Soil-Water-Atmosphere-Plant (SWAP) model (version 4.0.1) for agro-hydrological simulations of winter wheat and sugar beet.
  • WOrld FOod STudies (WOFOST) model (version 7.1) embedded in SWAP for dynamic crop growth simulation.
  • Rosetta pedotransfer model (version 1) for estimating soil hydraulic parameters.
  • Quantile Mapping (QM) method for bias correction of climate variables.
• Data sources:
  • ECMWF S2S ensemble climate reforecast data (model version CY49R1, 11 ensemble members) for precipitation, dew point temperature, solar radiation, minimum and maximum temperature, and wind speed (2006–2022).
  • Observed ground data from nearby stations of the German Meteorological Service (DWD) for bias correction.
  • Experimental field records from Hamerstorf (Fachverband Feldberegnung, FVF) for observed irrigation scheduling, soil moisture measurements (0–30 cm depth), and crop yield (2006–2022).
  • Soil texture data from sampling and sedimentation analysis (LBEG).

Main Results

• Irrigation forecast skill decreases with increasing lead time. The mean absolute error (MAE) of cumulative seasonal irrigation using first-week forecasts is approximately 40 mm for winter wheat and 30 mm for sugar beet, increasing to 70 mm and 80 mm, respectively, with a six-week S2S lead time.
• The reliability of weekly irrigation estimation decreases from approximately 79% at a one-week lead time to 67% at six weeks for winter wheat, and from 72% to 55% for sugar beet.
• The median of the forecasted irrigation ensemble is a good representative, often showing slightly higher reliability than individual ensemble members.
• MAE of weekly precipitation forecasts ranges from less than 5 mm in the first week to up to 20 mm for longer S2S lead times.
• Applying reliability-weighted S2S forecasts (using a utility function) in years with high irrigation demand reduced average predicted irrigation from 123 mm to 41 mm for winter wheat and from 204 mm to 150 mm for sugar beet.
• Water use efficiency (WPc) for winter wheat decreased slightly (from 2.62 kg/m³ to 2.50 kg/m³) with S2S-informed irrigation, while for sugar beet, it remained stable (4.91 kg/m³ to 4.93 kg/m³).

Contributions

• This study addresses a gap in the literature by investigating the application of S2S forecasts for field-scale irrigation management at the tactical (within-season) decision-making timescale.
• It proposes and evaluates a novel approach for combining short-term and S2S forecasts using a reliability-weighted utility function to guide irrigation decisions, balancing accuracy with longer-term tactical insight.
• The research demonstrates the potential of S2S forecasts to support adaptive water management by reducing seasonal irrigation amounts while maintaining reasonable crop water productivity, particularly in dry years or regions with water allocation limits.
• The use of a long-term experimental dataset (Hamerstorf, 2006–2022) provides a robust basis for evaluating reforecasts and developing procedures applicable to other irrigated areas.

Funding

• Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 437320596.

Citation

@article{FallahMehdipour2026Evaluation,
  author = {Fallah-Mehdipour, Elahe and Dietrich, Jörg},
  title = {Evaluation of agro-hydrological subseasonal-to-seasonal forecasts for tactical decision making in crop irrigation},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110315},
  url = {https://doi.org/10.1016/j.agwat.2026.110315}
}