Shahmohammadi et al. (2026) A two-soil-layer agro-hydrological model for irrigation requirements: Evaluation and trends in the Po River basin

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-11-05
• Authors: Amirarsalan Shahmohammadi, Stefania Tamea
• DOI: 10.1016/j.agwat.2026.110887

Research Groups

Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Italy

Short Summary

This study introduces AGRHYD, a two-soil-layer agro-hydrological model based on the FAO 56 dual crop coefficient approach, to estimate irrigation requirements (IR) in the Po River Basin. The model reveals statistically significant increasing trends in annual IR for maize and forages from 1981–2022, primarily driven by hydroclimatic changes in June and July.

Objective

• To develop and evaluate AGRHYD, a two-soil-layer agro-hydrological model, for estimating crop actual evapotranspiration (ETc act), crop evapotranspiration (ETc), and irrigation requirements (IR) for maize and forages in the Po River Basin.
• To investigate long-term trends in IR (1981–2022) and their hydroclimatic drivers in the Po River Basin.

Study Configuration

• Spatial Scale: Po River Basin, northern Italy (drainage area of 71,327 km²), at a spatial resolution of 2.2 km.
• Temporal Scale: Daily time-step simulations over the period 1981–2022.

Methodology and Data

• Models used:
  • AGRHYD: A two-compartment (topsoil-subsoil) soil-water balance (SWB) model built on the FAO 56 dual crop coefficient approach.
  • FAO 56 Penman-Monteith (P-M) equation for reference evapotranspiration (ET₀).
  • Hargreaves-Samani (H-S) equation as an alternative for ET₀.
  • Theil-Sen estimator for trend analysis.
  • Trend-Free Pre-whitening Maan-Kendall test with Yue’s modification (TFPW-Y) for statistical significance.
  • Benjamini-Hochberg correction for false discovery rate control.
• Data sources:
  • Meteorological data: Very High-Resolution Reanalysis for Italy (VHR-REA_IT) dataset (0.02° (~2.2 km) spatial resolution), a dynamically downscaled version of ERA5.
  • Crop parameters: Regional documents, technical reports, Water Footprints for Nations, and Pereira et al. (2025).
  • Vegetation indices: Copernicus Land Monitoring Service (CLMS) for Leaf Area Index (LAI) and Fraction of green vegetation Cover (FC) (dekadal, 300 m resolution).
  • Crop type maps: High Resolution Layer Crop Type (CTY) data (10 m) and EUROCROPMAP dataset (10 m) for masking LAI and FC.
  • Soil properties: SoilGrids v2.0 (sand and clay fractions, 250 m) and HiHydroSoil v2.0 (soil hydraulic properties, 250 m).
  • Irrigated area maps: Italian National Institute of Statistics (ISTAT) agricultural census (municipal level, 2020) and irrigation channel network data.
  • Validation data: WaPOR v3 (actual evapotranspiration and interception, dekadal, 300 m) and GLEAM v4.2a (evaporation and transpiration, daily, ~11 km).

Main Results

• Model Performance: AGRHYD's daily ETc act estimates for rainfed wheat and vine showed strong agreement with independent satellite-based products (WaPOR and GLEAM) for the period 2018–2022.
  • For wheat, Pearson's correlation coefficient (r) was up to 0.88 and Root Mean Square Error (RMSE) as low as 0.91 mm day⁻¹.
  • For vine, r was up to 0.91 and RMSE as low as 0.58 mm day⁻¹.
  • The dual-Kc approach consistently outperformed the single-Kc approach, and Hargreaves-Samani (H-S) based ET₀ estimates yielded better performance than Penman-Monteith (P-M) based ET₀.
• Irrigation Requirements (IR) for Maize and Forages (1981–2022):
  • Mean annual IR for maize was 243 mm year⁻¹, with spatial variability from 3 to 353 mm year⁻¹.
  • Mean annual IR for forages was 264 mm year⁻¹, reaching over 500 mm year⁻¹ in some areas.
  • Highest IR demands were concentrated in eastern Piedmont and Emilia-Romagna, correlating with lowest precipitation and highest ET₀.
• Long-term Trends in IR (1981–2022):
  • Statistically significant increasing trends in annual IR were observed across the majority of irrigated areas.
  • Basin-wide mean annual IR increased by +0.88 mm year⁻¹ for maize and +1.17 mm year⁻¹ for forages.
  • These increasing trends were primarily driven by significant positive trends in June and July.
  • The observed IR trends were consistent with concurrent declines in precipitation and rising atmospheric evaporative demand (ET₀) during these months.
  • A sensitivity analysis confirmed the robustness of the key trend findings to uncertainties in crop calendar timing.

Contributions

• Introduces AGRHYD, a novel two-soil-layer agro-hydrological model that refines the FAO 56 dual-Kc framework with an explicit topsoil compartment, improving soil evaporation estimation and enabling future integration with satellite-based soil moisture retrievals.
• Provides a comprehensive, long-term (1981–2022) dataset of crop-specific daily irrigation requirements at 2.2 km spatial resolution for maize and forages in the Po River Basin.
• Offers a more physically consistent representation of soil-plant-atmosphere interactions by explicitly separating transpiration from evaporation, accounting for precipitation interception, incorporating vertical heterogeneity of soil hydraulic properties, and adopting a modified evaporation reduction coefficient.
• Accounts for spatial variability in crop parameters, enhancing the accuracy of regional IR estimates.
• Identifies and quantifies statistically significant increasing trends in IR across the Po River Basin, providing a robust analysis of their hydroclimatic drivers (declining precipitation and rising ET₀).
• The model's reliable performance and modest data requirements demonstrate its transferability to other intensively irrigated agricultural regions.
• The generated IR dataset and long-term trend diagnostics serve as a valuable resource for irrigation planning and water allocation strategies in the Po River Basin.

Funding

ClimaXPo project "CLIMate Adaptation for the PO river basin district" (LIFE21-IPC-IT-LIFE-CLIMAX-PO, project n.101069928).

Citation

@article{Shahmohammadi2026twosoillayer,
  author = {Shahmohammadi, Amirarsalan and Tamea, Stefania},
  title = {A two-soil-layer agro-hydrological model for irrigation requirements: Evaluation and trends in the Po River basin},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110887},
  url = {https://doi.org/10.1016/j.agwat.2026.110887}
}