the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How to account for irrigation withdrawals in a watershed model
Elisabeth Brochet
Sabine Sauvage
Youen Grusson
Ludovic Lhuissier
Valérie Demarez
Abstract. In agricultural areas, the downstream flow can be highly influenced by human activities during low flow periods, especially dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for the growth, whereas the second relies on high resolution data from Sentinel-2 satellite to monitor plant growth. Both are applied at plot scale in a watershed of 800 km2 characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed to simulate the downstream flow with a good daily accuracy, especially during low flow periods.
Elisabeth Brochet et al.
Status: open (until 21 Jun 2023)
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RC1: 'Comment on egusphere-2023-494', Anonymous Referee #1, 19 May 2023
reply
The present work details accounting for human activities i.e irrigation withdrawals and (maybe) dam releases in a hydrological model (SWAT+). It further compares two model set ups with two different crop phenological growth approaches i.e default approach using the heat units and another approach utilizing NDVI from remote sensing. The paper is well written and the diverse aspects crop representation, irrigation withdrawals, calibration approaches as well as, the adopted model setups are well explained and justified. Some limitations are also well recognized. To enhance the paper's significance and potential for inspiring future modeling efforts, it would benefit from providing additional information on the following points:
- Utilizing NDVI as a proxy for phenological development is a commendable approach, and it has been employed in previous studies as well as the Leaf Area Index from remote sensing (Ma et al., 2019; Chen et al., 2023). However, it is crucial to acknowledge the limitations associated with this method in the manuscript. For instance, it is important to note that NDVI may not be able to distinguish between different crops within the same field in cases of mixed cropping. Additionally, it can be challenging to differentiate between crops and other types of vegetation, such as shrubs. Consequently, while NDVI may be effective in agricultural catchments with predominantly single crops, its effectiveness might be limited in mixed or intercropping fields. Further elaboration and information regarding these limitations would be beneficial.
- The authors' preference for utilizing NDVI to derive phenology raises a question regarding the presence of cropping calendars from PKGC, as indicated in the "plant phenology" section. Nkwasa et al. (2022) have emphasized the recommendation of using crop calendars, which include plant and harvest dates. By using crop calendars, the authors show that the model can properly simulate the LAI in comparison to remote sensing LAI. It would be beneficial to have a discussion regarding the relative strengths of remote sensing over crop calendars. This discussion could highlight the advantages of remote sensing in situations where crop calendars are not available or feasible to use.
- Given the focus of this study on crop modeling, it would have been valuable to assess the model's performance in terms of crop yields. It raises the question of whether the two different implementations, utilizing the default approach with heat units versus the approach incorporating NDVI from remote sensing, would have a significant impact on crop yield estimates. This would enhance the understanding of their potential implications on agricultural assessments.
- There is a lack of information regarding the representation of the two dams in the study. It is important to clarify whether this was achieved through the use of decision tables. If decision tables were employed, it would be beneficial to provide further elaboration, possibly in the supplementary material, to enhance the understanding of the dam representation method. Furthermore, it is crucial to elaborate how water was abstracted from the rivers in the model. Did this process involve the utilization of irrigation decision tables or water transfer tables? This information holds value for the scientific community, particularly in terms of reproducibility and ensuring the ability to replicate the study's methodology.
Editing and language comments
Line 4: Do you mean “Two different crop models” or “Two different model setups of SWAT+”?
Line 5: Which high resolution data from Sentinel-2?
Line 18: “accounts”
Line 20: “on spatial extents”
Line 23: “scenarios” – check through text.
Line 27: “forecasts”
Line 27: Are the names of the software and links to the software necessary here? Please, reference properly.
Line 31: “users of the SWAT”
Line 39: “crop dynamics”
Line 44: “data have been”
Line 45: “and have bloomed”
Line 67: “decision rules with remote sensing”
Line 82: “irrigation has”
Line 86: What Sentinel-2 data is used? Please clarify and rephrase sentence.
Line 356: “summer crops”
Line 399: What is an “unusual accuracy?”
Line 115: ……..high resolution remote sensing data of what? Please clarify on this and make clear through out text.
Line 119: What gap filled time series were created? Please clarify.
Line 120: How long was the discharge data timeseries?
Line 150: Please clarify on what you mean by “true crop rotation”? Was this seasonal or annual? And what was the crop rotation (At least add this information to SI)? Also, this could be misleading I that it’s interpreted as you are feeding the model with the actual plant and harvest dates for the different crops in the right rotations. Maybe clarify on this.
References
Chen, S., Fu, Y.H., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, Xuan, Tang, J., Singh, V.P., Zhang, Xuesong, 2023. Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes. J. Hydrol. 616, 128817. https://doi.org/10.1016/j.jhydrol.2022.128817
Ma, T., Duan, Z., Li, R., Song, X., 2019. Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics. J. Hydrol. 570, 802–815. https://doi.org/10.1016/j.jhydrol.2019.01.024
Nkwasa, A., Chawanda, C.J., Jägermeyr, J., van Griensven, A., 2022. Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+. Hydrol. Earth Syst. Sci. 26, 71–89. https://doi.org/10.5194/hess-26-71-2022
Citation: https://doi.org/10.5194/egusphere-2023-494-RC1
Elisabeth Brochet et al.
Model code and software
Customized SWAT+ model to include remote sensing NDVI data Elisabeth Brochet https://github.com/ElisabethJustin/SWATplus-NDVI
Elisabeth Brochet et al.
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