Modanesi et al. (2025) Accounting for scaling effects on irrigation optimization within a land surface model using satellite observations

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Identification

• Journal: Journal of Hydrometeorology
• Year: 2025
• Date: 2025-12-26
• Authors: Sara Modanesi, Louise Busschaert, Gabriëlle De Lannoy, Domenico De Santis, Martina Natali, Jacopo Dari, Pere Quintana-Seguí, Mariapina Castelli, Fabio Massimo Grasso, Christian Massari
• DOI: 10.1175/jhm-d-25-0057.1

Research Groups

• NASA Land Information System (LIS) Team
• Research groups specializing in Mediterranean hydrology and remote sensing (Northeastern Spain study region)

Short Summary

This study optimizes the Noah-MP Land Surface Model's irrigation scheme using Sentinel-1 satellite data and a genetic algorithm to improve grid-scale irrigation estimates. The research demonstrates that incorporating a Scale Irrigation Coefficient (SIC) to account for sub-grid heterogeneity significantly outperforms traditional soil moisture threshold triggers.

Objective

• To improve the representation of sprinkler irrigation in Land Surface Models (LSMs) by calibrating model parameters against satellite-derived irrigation estimates to account for spatial heterogeneity at the kilometric scale.

Study Configuration

• Spatial Scale: Regional study in Northeastern Spain (Ebro River Basin) at a 0.01° (~1 km) grid resolution.
• Temporal Scale: Interannual analysis (specific multi-year period focused on irrigation seasons).

Methodology and Data

• Models used: Noah-MP Land Surface Model (LSM) within the NASA Land Information System (LIS) framework.
• Data sources: Sentinel-1-based irrigation estimates, satellite-derived Evapotranspiration (ET) and Gross Primary Production (GPP) datasets, and in situ soil moisture observations.
• Optimization Technique: Genetic Algorithm (GA) used to calibrate two distinct approaches: a root-zone soil moisture threshold ($Th_{irr}$) and a Scale Irrigation Coefficient (SIC).

Main Results

• The $Th_{irr}$ calibration proved inflexible, resulting in infrequent irrigation events with excessive water volumes that did not match realistic practices.
• The SIC calibration significantly improved irrigation dynamics and reduced model errors, providing a better representation of interannual surface soil moisture anomalies.
• Assuming "full irrigation" at a 1 km resolution is shown to be unrealistic because farmers cannot irrigate all fields within a grid cell simultaneously and the landscape consists of a heterogeneous field mosaic.
• While soil moisture representation improved, inconsistencies persist between model-simulated ET/GPP and satellite-based products, highlighting remaining challenges in coupling irrigation with vegetation dynamics.

Contributions

• Introduces the Scale Irrigation Coefficient (SIC) as a superior parameter for representing sub-grid spatial heterogeneity in irrigation practices within LSMs.
• Demonstrates a successful framework for using high-resolution SAR (Sentinel-1) data to calibrate regional hydrological models.
• Challenges the conventional modeling assumption of uniform irrigation at kilometric scales, providing a more physically and operationally realistic approach for hydrological simulations in human-impacted basins.

Funding

• Not specified in the provided text.

Citation

@article{Modanesi2025Accounting,
  author = {Modanesi, Sara and Busschaert, Louise and Lannoy, Gabriëlle De and Santis, Domenico De and Natali, Martina and Dari, Jacopo and Quintana-Seguí, Pere and Castelli, Mariapina and Grasso, Fabio Massimo and Massari, Christian},
  title = {Accounting for scaling effects on irrigation optimization within a land surface model using satellite observations},
  journal = {Journal of Hydrometeorology},
  year = {2025},
  doi = {10.1175/jhm-d-25-0057.1},
  url = {https://doi.org/10.1175/jhm-d-25-0057.1}
}

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