On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley

Busschaert, Louise; Bechtold, Michel; Modanesi, Sara; Massari, Christian; Raes, Dirk; Kumar, Sujay V.; De Lannoy, Gabriëlle J. M.

doi:https://doi.org/10.5194/egusphere-2025-2550

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https://doi.org/10.5194/egusphere-2025-2550

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01 Jul 2025

| 01 Jul 2025

Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley

Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy

Abstract. Land surface and crop models both simulate irrigation, but they differ in their approaches, primarily because they were originally developed for distinct purposes and scales. Through an example case study in a highly irrigated region, this research helps to better understand the gap between these models and the complexity of irrigation modeling. More specifically, irrigation was estimated over the Po Valley (Italy) at a 1-km² spatial resolution using (i) a crop model, AquaCrop, and (ii) a land surface model, Noah-MP. Both models were run with sprinkler irrigation using a similar setup within NASA's Land Information System. Irrigation estimates were evaluated at the pixel and basin scale, using in situ and satellite-based reference data. In addition, surface soil moisture (SSM), vegetation, and evapotranspiration (ET) estimates were compared with satellite retrievals.

Noah-MP has on average higher annual irrigation rates (434 mm yr^-1) than AquaCrop (268 mm yr^-1), mainly because Noah-MP simulates more irrigation water losses (not consumed by transpiration) via runoff, interception, and soil evaporative losses, whereas AquaCrop only accounts for soil evaporative losses. When adding representative application water losses to irrigation estimates from AquaCrop, and conveyance water losses to the estimates from both models, the irrigation estimates from both models fall within reported ranges of 500–600 mm yr^-1. For the field-based evaluation, Noah-MP presents large irrigation events (> 100 mm per event) and less interannual variability than AquaCrop. Two-week averaged SSM estimates from both models agree well with downscaled estimates from the Soil Moisture Active Passive (SMAP) mission, with spatially averaged unbiased root mean square differences of 0.05 and 0.04 m³ m^-3 for AquaCrop and Noah-MP, respectively. Both models show limitations in terms of vegetation and ET modeling, mainly due to simplistic vegetation modules and suboptimal parameterization in both models. The results highlight the complexity of irrigation modeling due to its anthropogenic nature, and also show the need for better observations to validate and guide model estimates: reference irrigation data are sparse and satellite retrievals under irrigated conditions are quite uncertain.

Received: 30 May 2025 – Discussion started: 01 Jul 2025

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 preprint. The responsibility to include appropriate place names lies with the authors.

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Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy

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'Comment on egusphere-2025-2550', Nima Zafarmomen, 05 Jul 2025 reply
The preprint "On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley" by Busschaert et al. (2025) compares two distinct modeling approaches—crop model (AquaCrop) and land surface model (Noah-MP)—for estimating irrigation, soil moisture, vegetation, and evapotranspiration (ET) in the highly irrigated Po Valley, Italy. Using a 1-km² resolution within NASA’s Land Information System, the study evaluates model performance against in situ and satellite-based data, highlighting differences in irrigation estimates (Noah-MP: 434 mm yr⁻¹, AquaCrop: 268 mm yr⁻¹) due to varying treatments of water losses, vegetation dynamics, and soil parameters.
The study highlights that Noah-MP simulates higher irrigation rates (434 mm yr⁻¹) than AquaCrop (268 mm yr⁻¹) due to differences in accounting for losses (e.g., runoff, interception, and soil evaporation). However, the manuscript could better clarify how these losses are quantified and modeled in each system. For instance, the absence of canopy interception in AquaCrop is noted, but the implications for irrigation efficiency and model performance are not thoroughly discussed.

The study uses default soil hydraulic parameters (SHPs) for both models, which differ significantly (e.g., Noah-MP’s higher total available water, TAW). While Section 4.2 acknowledges this limitation, the manuscript lacks a sensitivity analysis to assess how variations in SHPs (e.g., θ_FC, θ_WP) impact irrigation estimates.

The comparison with satellite-based irrigation estimates from Dari et al. (2023) shows discrepancies, particularly in interannual variability (Section 3.1.2). The manuscript suggests potential inaccuracies in the satellite data but does not explore this further or provide evidence to support this claim.

Both models show significant overestimation of vegetation (DMP) compared to CGLS data, attributed to simplistic vegetation modules and suboptimal parameterization (Section 3.1.3). The manuscript could improve by discussing specific parameterization choices (e.g., maximum canopy cover in AquaCrop, vegetation module options in Noah-MP) and their impact on results.

While the authors briefly mention data assimilation (DA) as a potential approach for integrating observations (e.g., Abolafia-Rosenzweig et al., 2019; Busschaert et al., 2024; Igder et al., 2022; Maina et al., 2024; Modanesi et al., 2022; Nie et al., 2022), the discussion could benefit from the inclusion of additional relevant studies. For example, the work titled “Assimilation of Sentinel-Based Leaf Area Index for Modeling Surface-Ground Water Interactions in Irrigation Districts” presents a valuable application of DA in a coupled surface–subsurface hydrological context, specifically within agricultural settings. Including such studies would help strengthen the section by illustrating the diversity of DA techniques and their relevance to integrated hydrologic modeling in real-world systems.

The study finds that irrigation estimates correlate better with in situ data at longer temporal aggregations (e.g., monthly, two-monthly; Section 3.2). However, the manuscript does not adequately address how this aggregation might mask short-term inaccuracies in irrigation timing, which is critical for real-world applications.

The models rely on MERRA2 meteorological forcings, downscaled via bilinear interpolation (Section 2.2.1). The manuscript does not address potential uncertainties introduced by this downscaling or the coarse resolution of MERRA2 (0.5° × 0.625°). A brief analysis or reference to studies evaluating MERRA2’s accuracy in the Po Valley would enhance confidence in the input data.

The study excludes paddy rice areas and clay soils due to AquaCrop’s limitations in simulating sprinkler irrigation on low-conductivity soils (Section 2.1). This exclusion reduces the study’s scope, as rice paddies are significant in the Po Valley. The manuscript should discuss the implications of this exclusion for regional irrigation estimates and propose how future studies could incorporate these areas (e.g., by adapting AquaCrop for flood irrigation).

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Citation: https://doi.org/10.5194/egusphere-2025-2550-CC1

Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy

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Short summary

This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.


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