the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jan 2026
Long-term irrigation water use datasets from multiple Earth Observation-based methods in major irrigated regions
Abstract. Irrigation water use (IWU) is the largest direct human intervention in the terrestrial water cycle, yet it remains poorly characterized at the spatial and temporal scales required for climate research. We present a long-term archive of monthly IWU estimates at 0.25° spatial resolution for four major irrigated regions — the contiguous United States (CONUS), India, the Murray–Darling Basin in Australia, and the Ebro Basin in Spain — spanning up to two decades depending on input data availability. The datasets are derived using three distinct approaches: (i) a Soil Moisture (SM)–based Delta method that infers irrigation from discrepancies between satellite and model SM and evapotranspiration, (ii) an SM–based inversion of the soil water balance constrained by satellite SM, and (iii) a model–observation integration scheme combining a land surface model with satellite-based irrigated-area maps. Across regions, these approaches yield up to six SM-based Delta products, five SM-based Inversion products, and one Model–observation integration product. Validation against available irrigation records shows that several method–input combinations reproduce the order of magnitude of annual state-level irrigation volumes in the CONUS, with typical errors for the best-performing datasets of about 4–5 km3 yr−1 in root mean square deviation and 1–2 km3 yr−1 in bias. In the Murray–Darling and Ebro basins, the products capture the main features of the seasonal irrigation cycle, with variations in spatial patterns, magnitude, and timing across methods. In India, where no observational records are available, the datasets reproduce the expected agricultural seasons while exhibiting a wider inter-method spread. This coordinated dataset collection, produced with multiple Earth Observation-based approaches and harmonized inputs across regions, provides long-term, spatially explicit IWU estimates and a basis for better quantifying and representing irrigation in large-scale hydrological and climate studies. The complete archive of datasets is freely available at https://doi.org/10.5281/zenodo.14988197 (Laluet et al., 2025b).
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-737', Samuel Zipper, 26 Feb 2026
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RC2: 'Comment on essd-2025-737', Anonymous Referee #2, 28 Feb 2026
The manuscript presents a harmonized archive of long-term irrigation water use (IWU) datasets derived from three Earth Observation–based approaches across four major irrigated regions. By comparing three retrieval methods, it provides a valuable resource for hydrological and climate research and advances large-scale representation of irrigation processes. However, several issues need to be addressed to strengthen scientific rigor, transparency, and practical applicability.
- The three methods rely on different assumptions, and their performance varies across regions. It would be helpful if the authors could further clarify how these assumptions influence uncertainty and dataset differences. A more systematic comparison linking methodological assumptions to regional climate, irrigation practices, soil conditions, and water sources would strengthen the manuscript.
- In several regions, correlations are moderate and biases remain noticeable. It would be beneficial for the authors to further discuss whether the current level of accuracy is sufficient for intended applications, such as climate modeling or trend analysis.
- The study applies static irrigated-area maps over long time periods. In regions where irrigated area has changed over time, this may introduce bias. A clearer discussion of how this limitation may influence long-term analysis would improve the paper.
- The results vary depending on the soil moisture, ET, and irrigation datasets used. The manuscript would benefit from a clearer explanation of why certain input datasets perform better than others, including a discussion of their respective strengths and limitations.
- Several key thresholds and parameters are applied uniformly across all regions. The authors may consider evaluating the sensitivity of the results to these settings and discussing whether region-specific calibration could further improve performance.
- The discussion section could be strengthened by providing a more integrated cross-regional synthesis that highlights common patterns, key differences, and broader implications of the results.
- The readability of the heatmaps in Figures 2, 5, and 7 could be improved by enlarging axis labels, legends, and color scales.
Citation: https://doi.org/10.5194/essd-2025-737-RC2 -
RC3: 'Comment on essd-2025-737', Anonymous Referee #3, 02 Mar 2026
This manuscript submitted to ESSD presents a much-needed dataset of irrigation water use (IWU) over key irrigated regions of the world. The authors implement and compare three estimation approaches through a well-designed methodological framework. Overall I find the manuscript well written, timely, and suitable for the scope of ESSD. However, I have a few concerns and suggestions that, if addressed, would further enhance the clarity, readability, and rigor of the manuscript. I therefore recommend that the following points be considered prior to acceptance.
Major Comments
#1. My first concern is regarding snow-dominated basins and how the delayed effect of snow melt on soil moisture (SM) is tackled. Some of the study regions such as CONUS and Ebro Basin (as mentioned by the authors) contain snow dominated basins. Since the Delta and Inversion approaches attribute SM anomalies to irrigation after accounting for precipitation, it is important to clarify how snowmelt-driven soil moisture increases are distinguished from irrigation signals. Some clarity on how the various methods deal with quantifying snow melt is warranted.#2. In line with comment #1, how do the assumptions made in each method affect the impact of snow-melt? For instance, the SM-based inversion method assumed runoff is negligible. Will this assumption hold for such basins?
Further, is the precipitation dataset used the total accumulated precipitation, containing both rainfall and snowfall components?#3. In the Results/Technical Validation section, the author’s spend considerable length reiterating values that readers can infer by looking at the visual figures. Not much is mentioned about why these patterns or discrepancies across methods occur.
For example, in section 3.1.2, lines 267-274, numerous IWU values are stated for specific regions in CONUS, without accompanying discussion of the possible underlying drivers. These values are easily inferred from Figure 3. However, the readers are left with questions such as why IWU in California is significantly higher in Method 3 vs Methods 2 and 1?
I realize that the authors might have touched upon the reasons in section 4 and 5. However, in its present form, it is difficult to infer the scientific explanations of the observations in Section 3. I therefore encourage the authors to modify Section 3 to include some discussion of the mechanistic or structural reasons behind various observations. This will transform it from primarily descriptive reporting into a mix of reporting observations and interpretive analysis, which I believe will improve readability.#4. The manuscript contains several figures that convey similar statistical comparisons (Figures 2,5,7). Consider consolidating them into one multi-panel figure to reduce repetitiveness.
#5. In section 2.2.1, the authors assume that EO-based and model-simulated ET agree under non-irrigated conditions. How important is this assumption? Consider adding a plot of bias values or descriptive statistics table for a sample case in the supplementary to support.
Minor Comments:
#1 Eq. 1 and 2 have repeating P(t) terms. Did the author’s mean R(t)?
#2. Section 1 Line 27 ”With global demand for irrigation projected to rise due to population growth, dietary shifts, and climate change, ..” requires a supporting reference.
#3. Are any precipitation datasets used. If so, append Table 1 with relevant details.
#4. Line 231-233. Details about what the dash indicates can move to Table 2 description.Citation: https://doi.org/10.5194/essd-2025-737-RC3
Data sets
Regional datasets of long-term and coarse resolution irrigation estimates from space Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Luca Brocca, Christian Massari, Sara Modanesi, Wouter Dorigo, Pia Langhans, Gabrielle De Lannoy, Michel Bechtold, Carla Saltalippi, Renato Morbidelli, Maria Cristina Rulli, Davide Danilo Chiarelli, Nikolas Galli https://zenodo.org/records/14988198
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This manuscript describes a newly-developed and publicly released irrigation water use dataset. The dataset spans four regions and includes three different estimation methods. The authors compare their estimates to observational data in three regions where data are available. Irrigation water use is a critically important flux for water management, and one that is challenging to obtain reliable data. Since the true value of irrigation is rarely known, having a range of estimates from different sources is valuable. Therefore, studies such as this one describing the development of new scientific products quantifying irrigation water use are needed and complement existing remotely sensed and modeled irrigation datasets that already exist.
I am usually a pretty detailed reviewer, but I have little to suggest here. The manuscript is effectively motivated and clearly written, with useful figures. Each of the individual irrigation water use estimation approaches uses a well-established method and relies on reasonable publicly available datasets. The assessments via comparison to observations are clearly presented, and the authors do a good job discussing each dataset and highlighting the approaches that perform the best, which will provide guidance to future researchers. I recommend publication, unless other reviewers identify issues that I missed.
-Sam Zipper