Laluet et al. (2026) Long-term irrigation water use datasets from multiple Earth Observation-based methods in major irrigated regions

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-07-13
• Authors: Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Gabriëlle J. M. De Lannoy, Pia Langhans, Sara Modanesi, Christian Massari, Luca Brocca, Carla Saltalippi, Renato Morbidelli, Clément Albergel, Wouter Arnoud Dorigo
• DOI: 10.5194/essd-18-4833-2026

Research Groups

• Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
• Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy
• Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
• Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
• European Space Agency Climate Office, ECSAT, Didcot, UK
• ECMWF, Research Department, Reading, UK

Short Summary

This study presents a long-term archive of monthly irrigation water use (IWU) estimates at 0.25° spatial resolution for four major irrigated regions, derived using three distinct Earth Observation-based approaches, providing a harmonized dataset for large-scale hydrological and climate studies.

Objective

• To present a long-term archive of monthly irrigation water use (IWU) estimates at 0.25° spatial resolution for four major irrigated regions (contiguous United States, India, Murray-Darling Basin, Ebro Basin) using multiple Earth Observation-based methods, and to assess their consistency and performance against available irrigation records.

Study Configuration

• Spatial Scale: 0.25° spatial resolution; four major irrigated regions: contiguous United States (CONUS), India, the Murray-Darling Basin (Australia), and the Ebro Basin (Spain).
• Temporal Scale: Monthly time step; spanning up to two decades (2003-2022, depending on input data availability).

Methodology and Data

• Models used:
  • SM-based Delta method (infers irrigation from discrepancies between satellite and model soil moisture and evapotranspiration).
  • SM-based Inversion of the soil water balance (constrained by satellite soil moisture).
  • Model-observation integration scheme (combining Noah-MP land surface model with satellite-based irrigated-area maps).
  • Noah-MP land surface model (v4.0.1) within NASA’s Land Information System (LIS).
• Data sources:
  • Satellite Soil Moisture: ESA CCI Soil Moisture (v08.1 ACTIVE, PASSIVE, COMBINED), SMOS (v700 L2), SMAP (v8 L2), ASCAT (H119).
  • Model Soil Moisture: ERA5-Land (2024 CDS release).
  • Evapotranspiration (ET): SSEBop v5, FLUXCOM (Remote Sensing version), Noah-MP ET, ERA5-Land ET.
  • Potential Evapotranspiration (PET): GLEAM v3.7b.
  • Precipitation: ERA5 forecast (2024 CDS release).
  • Irrigated Area Maps: Landsat-derived Global Rainfed and Irrigated-Cropland product (LGRIP30 v1.0, 2015), Global Map of Irrigated Areas v5 (GMIA v5, 2000-2008).
  • Soil Texture: Harmonized World Soil Database (FAO, 2023).
  • Reference Data:
    • CONUS: USDA Farm and Ranch Irrigation Survey (FRIS) annual state-level irrigation volumes (2013, 2018).
    • Ebro Basin: Automatic Hydrological Information System (SAIH) daily canal-delivery volumes (2007-2020) for four districts.
    • Murray-Darling Basin: Australian Irrigation Infrastructure Operators (IIOs) daily irrigation volumes (2009-2019) for four districts.

Main Results

• The archive provides up to six SM-based Delta products, five SM-based Inversion products, and one Model-observation integration product.
• CONUS: Several method-input combinations reproduce the order of magnitude of annual state-level irrigation volumes. Best-performing datasets show typical errors of about 4–5 km³ yr⁻¹ in root mean square deviation (RMSD) and 1–2 km³ yr⁻¹ in bias. The Model-observation integration dataset shows the strongest spatial heterogeneity, highlighting distinct irrigation hotspots.
• Ebro Basin: Products capture the main features of the seasonal irrigation cycle (peak June–August) but consistently underestimate volumes (mean bias ~ -20 mm per month). Best-performing datasets show Pearson correlation coefficients (R) around 0.60–0.63 and RMSD around 27–30 mm per month.
• Murray-Darling Basin: Products capture the main features of the seasonal irrigation cycle, with variations in spatial patterns, magnitude, and timing across methods. The Model-observation integration dataset reproduces the distinct January peak in the seasonal cycle. Performance varies considerably across districts, with mean R values up to 0.47 ± 0.11.
• India (Gangetic Plain): No observational records available for validation. Datasets reproduce expected agricultural seasons (dominant Rabi, smaller Zaid peak) but exhibit a wider inter-method spread in magnitude and timing of peaks. SM-based Delta datasets yield annual mean IWU values between 20 and 32 mm per month, while SM-based Inversion datasets range from 39 to over 80 mm per month.
• The Model-observation integration approach tends to emphasize irrigation hotspots, while SM-based approaches produce smoother spatial patterns.

Contributions

• First coordinated collection of long-term, spatially explicit irrigation water use (IWU) estimates from multiple Earth Observation (EO)-based approaches with harmonized inputs across several major irrigated regions worldwide.
• Provides a comprehensive archive for benchmarking and improving the representation of irrigation in large-scale hydrological and climate studies.
• Offers a multi-regional assessment of the consistency and behavior of different EO-based IWU retrieval approaches over extended time periods.
• Lays the groundwork for building a climate data record of IWU.

Funding

• European Space Agency (ESA) under the Climate Change Initiative – Anthropogenic Water Use (CCI–AWU) precursor project (contract number 4000142449/23/I-NB).
• TU Wien Bibliothek (Open Access Funding Programme).

Citation

@article{Laluet2026Longterm,
  author = {Laluet, Pierre and Dari, Jacopo and Busschaert, Louise and Heyvaert, Zdenko and Lannoy, Gabriëlle J. M. De and Langhans, Pia and Modanesi, Sara and Massari, Christian and Brocca, Luca and Saltalippi, Carla and Morbidelli, Renato and Albergel, Clément and Dorigo, Wouter Arnoud},
  title = {Long-term irrigation water use datasets from multiple Earth Observation-based methods in major irrigated regions},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-4833-2026},
  url = {https://doi.org/10.5194/essd-18-4833-2026}
}