the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Apr 2026
Incorporating irrigation effects into high-resolution daily land evaporation estimates over the Iberian Peninsula
Abstract. Land evaporation (E) links the water, energy, and carbon cycles and plays a central role in agriculture, water management, and land–climate interactions. However, estimating E at high spatial and temporal resolution remains challenging, especially in irrigated regions. This study presents a novel framework to generate daily 1 km E estimates for 2018–2022 over the Iberian Peninsula by explicitly representing irrigation in the recently released Global Land Evaporation Amsterdam Model version 4 (GLEAM4). To this end, high-resolution (1 km) meteorological forcing is combined with Sentinel-1 soil moisture and ancillary information on irrigated extent and satellite-based crop phenology. Our method constrains E below potential evaporation (Ep) even in irrigated land, leveraging observational data of vapour pressure deficit, air temperature, vegetation optical depth, leaf area index, wind speed, and shortwave radiation, which allows irrigated crops to respond realistically to diverse sources of vegetation stress, rather than assuming Ep rates. Results reveal increases in E over irrigated areas of up to 450 mm yr-1 when irrigation is explicitly considered, with spatial patterns consistent with independent irrigation estimates. Evaluation against eddy-covariance measurements demonstrates marked improvements at two irrigated sites in the Iberian Peninsula, with increases in daily Kling-Gupta Efficiency (KGE) compared to simulations without irrigation of 0.40 and 0.70, respectively. The approach is also relatively robust to false positives in the irrigation mask owing to the fractional vegetation structure of GLEAM4. Overall, the resulting high-resolution E dataset provides a realistic representation of irrigation practices and supports applications in both agricultural management and regional water-resource assessments. The approach will be extended to global scales through integration into future GLEAM releases.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 17 Jul 2026)
- RC1: 'Comment on egusphere-2026-1856', Stefanie Fischer, 09 Jun 2026 reply
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- 1
The study under review (“Incorporating irrigation effects into high-resolution daily land evaporation estimates over the Iberian Peninsula”) evaluates the GLEAM4 version at a 1 km spatial resolution over the Iberian Peninsula, comparing two approaches: (1) the default GLEAM4 framework, which implicitly accounts for irrigation through soil moisture and vegetation dynamics, and (2) an enhanced version that explicitly incorporates irrigation by integrating spatial information on irrigation extent and crop phenology (used to infer agricultural management timing) and by increasing soil water content to field capacity in the irrigated areas.
The model first computes potential evaporation (Ep) using the Penman equation, which is then reduced by a stress factor (S, ranging from 0 to 1). This stress factor is derived via a machine learning approach that accounts for environmental controls on evaporation (i.e. soil moisture, vapor pressure deficit, air temperature, global radiation, wind speed), particularly their influence on stomatal conductance.
The analysis covers the period 2018–2022, selected primarily because the underlying irrigation extent data (Meier et al., 2018) are static and based on 2018. Similarly, the crop phenology data (FAO, 2018) are also from 2018 and used to derive a daily irrigation mask. While the authors likely assume that irrigation practices remained relatively stable over this 5-year window, the rationale for choosing this specific time period is not explicitly stated in the manuscript. A key innovation lies in the dynamic application of this static irrigation mask: irrigation is only applied when the irrigation area intersects with short vegetation (as captured by the fraction of absorbed photosynthetically active radiation, fAPAR), thereby introducing a dynamic, biologically informed constraint on irrigation timing. Additionally, the model incorporates surface soil moisture data assimilation to improve soil moisture estimates.
Model performance is evaluated against daily evaporation observations from eight eddy covariance stations. Time series of modeled and observed evaporation are presented for two representative irrigated sites, with particular emphasis on the physical behavior of the stress factor and soil moisture dynamics under both irrigated and non-irrigated conditions. This allows for a meaningful assessment of the model’s ability to represent the physical mechanisms governing evaporation.
Accurate estimation of evaporation is crucial for understanding the energy balance and hydrological cycle, as it directly influences other components such as runoff, soil moisture, and groundwater storage. In agricultural regions, especially in water-scarce environments and under climate change, capturing the impact of irrigation is essential for effective water resource management. The study addresses this need by developing a method to incorporate irrigation into the GLEAM4 framework, enabling the derivation of temporally continuous, high-resolution evaporation estimates at regional scales (with potential for extension to global applications).
The main limitation of the study is the use of static information on irrigation extent and timing, which is critically addressed. The method of soil moisture assimilation using the climatology from the irrigated GLEAM4 version is not entirely clear, nor is the rationale for running the non-irrigated GLEAM4 version without soil moisture data assimilation. This needs to be explained and justified more precisely in the methods chapter. In the results chapter, certain regions of the study area are mentioned without a description of their location, which may hinder understanding for readers unfamiliar with the Iberian Peninsula or with limited background in European geography. Further details are provided in the comments attached.
Overall, the study is well designed, well structured, and clearly written. The results are presented in a clear manner and are supported by an appropriate number of figures that are well integrated into the text. I recommend the manuscript for publication after minor revisions.
Specific comments:
L112: These rivers should be indicated in the map of the study area (e.g. in Figure 1), which helps the reader to keep track of the described areas (particularly those being not familiar with the region).
L150: Some variables are abbreviated, others are not... please first introduce the variable and give abbreviation in brackets: e.g. vegetation optical depth (VOD), air temperature (Ta) ...
L193: Irrigated areas are represented on static information based on the year 2018 assuming these are still representative for 2019 - 2022... this needs to be considered in the discussion.
L216: Grid-cells
L234: This is not clear to me. What is the baseline climatology for the anomalies?
... GLEAM4 soil moisture estimates including irrigation are based on static information of 2018 (despite the modulation through short vegetation fraction)... are these used to calculate the baseline climatology? Please justify
Caption Figure 2: “(a) the model run without irrigation and soil moisture data assimilation”. This is not clear to me... without irrigation and without soil moisture data assimilation?
The original GLEAM4 Model without irrigiation (Miralles et al. 2025) includes soil water assimilation, why omitting data assimilation here?
L320: The argumentation for the limited effect of false positives on soil moisture dynamics is plausibly explained but requires further analysis, as only two stations are used as a reference (discussion).
L337: I understand that this paragraph aims to first introduce total E, before evaluating its components, but it’s a bit hard to navigate, as it introduces Figure 6 but actually describes Figure 3b.
L340: Readers from outside the Iberian Peninsula or those without a strong background in European geography might struggle to visualize or locate the mentioned regions (e.g., Aragon, Andalusia). Please use a more descriptive language (in northern and southern Spain, respectively).
L343: The placement of the reference Miralles et al. (2025) is confusing, as it may be interpreted as a dataset, whereas Miralles et al. (2025) conducted a comparative analysis of GLEAM4 against other state-of-the-art evaporation datasets.
L347: Please also add the range of Ei.
Caption Figure 5: The location of ES-Cnd (analogous to La Cendrosa) should be specified for clarity.
L357: In addition, is the fraction of bare soil increasing in winter (due to dormancy or agricultural practices), area corresponds to the cropland extent?