the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Oct 2024
Modeling irrigation and land surface dynamics: comparing AquaCrop and Noah-MP over the Po Valley
Abstract. In this study, 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 shows on average higher annual irrigation rates (434 mm yr-1) compared to AquaCrop (268 mm yr-1), mainly because more irrigation water losses (not consumed by transpiration) are simulated and compensated for in Noah-MP (runoff, interception, and soil evaporative losses), whereas AquaCrop only accounts for soil evaporative losses. When taking into account representative application water losses for AquaCrop, and conveyance water losses for both models, the irrigation estimates 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 compared to 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 the generalization of vegetation parameters for AquaCrop and a possible sub-optimal vegetation parametrization for Noah-MP. The results of this study highlight the differences between the models which have been created for distinct original purposes and scales, as well as the complexity of irrigation modeling due to its anthropogenic nature, emphasizing the need for observations.
Status: open (until 15 Jan 2025)
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CC1: 'Comment on egusphere-2024-3205', Nima Zafarmomen, 06 Nov 2024
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The paper does an excellent job comparing AquaCrop and Noah-MP, two models with distinct goals and methodologies, to evaluate their performance in estimating irrigation in the Po Valley. This side-by-side analysis provides valuable insights into the advantages and limitations of crop models versus land surface models when applied to complex irrigation systems.
1) How might data assimilation of satellite soil moisture or vegetation index data affect the model's ability to capture interannual variability in irrigation accurately? Would this approach potentially reduce some of the overestimations observed?2) Given the paper's findings on the variability of irrigation rates and timing, do the authors see data assimilation as a viable approach for operational large-scale irrigation modeling, or do they foresee challenges in scalability?
3) Noah-MP includes canopy interception and runoff losses, whereas AquaCrop does not. Could the authors discuss how these differing approaches to irrigation losses might affect their model outputs, especially in the context of large-scale regional studies?
4) I highly recommend the authors consider citing recent work on data assimilation in hydrological modeling for irrigation, specifically studies that integrate satellite-based vegetation indices to improve model accuracy. For example, studies like 'Assimilation of Sentinel‐based Leaf Area Index for Modeling Surface‐Groundwater Interactions in Irrigation Districts'and 'Multivariate Assimilation of Satellite-based Leaf Area Index and Ground-based River Streamflow for Hydrological Modeling of Irrigated Watersheds using SWAT+' showcase how data assimilation can enhance the irrigation districts.
Citation: https://doi.org/10.5194/egusphere-2024-3205-CC1
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