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Modifying Automatic Irrigation in SWAT for Plant Water Stress scheduling

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  • Uniyal, Bhumika
  • Dietrich, Jörg

Abstract

Automatic irrigation in the Soil and Water Assessment Tool (SWAT) is triggered by using plant water stress and soil water deficit irrigation scheduling. Auto-irrigation is important to simulate the catchment’s behavior in response to climate change and water management scenarios. However, studies have identified deficiencies in the auto-irrigation algorithms in SWAT as the irrigation water amount simulated under plant water stress scheduling shows a large deviation from the simulated irrigation water amount under soil water deficit scheduling. Therefore, the current research deals with validating and modifying the auto-irrigation scheduling under plant water stress condition using SWAT. The modified SWAT model was evaluated against the Soil-Water-Atmosphere-Plant (SWAP) model as well as observed data for irrigation and crop yield at an experimental field (Hamerstorf, Lower Saxony, Germany) during the 2008–2018 cropping seasons. The two SWAT subroutines. swu and. autoirr were modified. The existing root density distribution function was replaced with the one proposed by Li et al. (1998) and also a dynamic estimation of the plant water uptake compensation factor (EPCO) was incorporated into the modified SWAT. The results revealed that SWAP and modified SWAT were able to simulate the irrigation amount and crop yield with an acceptable bias for all the crops at the experimental site. However, the overall spread of crop yield simulated (11 years) by both the models was less compared to the observed spread for most of the crops. Furthermore, the modified SWAT code was used to simulate the irrigation amount for three different agro-climatic catchments in Germany, India and Vietnam. Results showed improved irrigation simulation in terms of long-term annual amounts compared to the default SWAT under plant water stress condition.

Suggested Citation

  • Uniyal, Bhumika & Dietrich, Jörg, 2019. "Modifying Automatic Irrigation in SWAT for Plant Water Stress scheduling," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    • Handle: RePEc:eee:agiwat:v:223:y:2019:i:c:23
    DOI: 10.1016/j.agwat.2019.105714
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    References listed on IDEAS

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    1. Albasha, Rami & Mailhol, Jean-Claude & Cheviron, Bruno, 2015. "Compensatory uptake functions in empirical macroscopic root water uptake models – Experimental and numerical analysis," Agricultural Water Management, Elsevier, vol. 155(C), pages 22-39.
    2. Peters, Andre & Durner, Wolfgang & Iden, Sascha C., 2017. "Modified Feddes type stress reduction function for modeling root water uptake: Accounting for limited aeration and low water potential," Agricultural Water Management, Elsevier, vol. 185(C), pages 126-136.
    3. Uniyal, Bhumika & Dietrich, Jörg & Vasilakos, Christos & Tzoraki, Ourania, 2017. "Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices," Agricultural Water Management, Elsevier, vol. 193(C), pages 55-70.
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    1. Wu, Di & Cui, Yuanlai & Li, Dacheng & Chen, Manyu & Ye, Xugang & Fan, Guofu & Gong, Lanqiang, 2021. "Calculation framework for agricultural irrigation water consumption in multi-source irrigation systems," Agricultural Water Management, Elsevier, vol. 244(C).
    2. Alice Bernini & Rike Becker & Odunayo David Adeniyi & Giorgio Pilla & Seyed Hamidreza Sadeghi & Michael Maerker, 2023. "Hydrological Implications of Recent Droughts (2004–2022): A SWAT-Based Study in an Ancient Lowland Irrigation Area in Lombardy, Northern Italy," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    3. Chen, Yong & Marek, Gary W. & Marek, Thomas H. & Porter, Dana O. & Brauer, David K. & Srinivasan, Raghavan, 2021. "Simulating the effects of agricultural production practices on water conservation and crop yields using an improved SWAT model in the Texas High Plains, USA," Agricultural Water Management, Elsevier, vol. 244(C).
    4. Čerkasova, Natalja & White, Michael & Arnold, Jeffrey & Bieger, Katrin & Allen, Peter & Gao, Jungang & Gambone, Marilyn & Meki, Manyowa & Kiniry, James & Gassman, Philip W., 2023. "Field scale SWAT+ modeling of corn and soybean yields for the contiguous United States: National Agroecosystem Model Development," Agricultural Systems, Elsevier, vol. 210(C).

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