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Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm

Author

Listed:
  • Omidreza Mikaeili

    (Technical and Engineering College, Shahid Beheshti University)

  • Mojtaba Shourian

    (Technical and Engineering College, Shahid Beheshti University)

Abstract

Evapotranspiration (ET) estimation is essential for managing agricultural water demand at the basin scale and allocating irrigation water. Many uncertainties, such as those related to the model’s structure, initial conditions, and parameter set, cascade into the ET calculation, producing unreliable results, even though water modelers and managers depend on stand-alone ET estimation models for planning and management. Utilizing an ensemble-based data assimilation (EDA) methodology, this study investigated how remotely-sensed ET can enhance simulations of the popular Surface Energy Balance Algorithm for Land (SEBAL) ET model while taking uncertainties into account. This watershed-scale study was carried out in the Maroon Basin situated in southwestern Iran. The SEBAL model was employed to simulate ET. The particle filter-based DA method was then applied to enhance the model’s performance. Afterward, the SWAT model was utilized to simulate the performance of products and runoff using ET taken from the SEBAL model. The study findings demonstrated that employing DA in SEBAL ET produced a more reliable and accurate model simulation. These findings paved the way for future research by highlighting the significance of digital farming tools in the management of water resources and sound agricultural planning and management.

Suggested Citation

  • Omidreza Mikaeili & Mojtaba Shourian, 2024. "Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4101-4122, September.
  • Handle: RePEc:spr:waterr:v:38:y:2024:i:11:d:10.1007_s11269-024-03854-4
    DOI: 10.1007/s11269-024-03854-4
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    References listed on IDEAS

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    1. 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.
    2. Philip W. Gassman & Jimmy R. Williams & Xiuying Wang & Ali Saleh & Edward Osei & Larry M. Hauck & R. César Izaurralde & Joan D. Flowers, 2009. "Agricultural Policy Environmental EXtender (APEX) Model: An Emerging Tool for Landscape and Watershed Environmental Analyses, The," Center for Agricultural and Rural Development (CARD) Publications 09-tr49, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    3. Mercedeh Taheri & Abdolmajid Mohammadian & Fatemeh Ganji & Mostafa Bigdeli & Mohsen Nasseri, 2022. "Energy-Based Approaches in Estimating Actual Evapotranspiration Focusing on Land Surface Temperature: A Review of Methods, Concepts, and Challenges," Energies, MDPI, vol. 15(4), pages 1-57, February.
    4. Li, Yan & Zhou, Qingguo & Zhou, Jian & Zhang, Gaofeng & Chen, Chong & Wang, Jing, 2014. "Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions," Ecological Modelling, Elsevier, vol. 291(C), pages 15-27.
    5. Hatice Citakoglu & Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2014. "Estimation of Monthly Mean Reference Evapotranspiration in Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 99-113, January.
    6. J. Besag & D. Higdon, 1999. "Bayesian analysis of agricultural field experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 691-746.
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