外文摘要：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.
外文关键词：Evapotranspiration;SWAT;Data assimilation;SEBAL
作者：Mikaeili, Omidreza;Shourian, Mojtaba
作者单位：Shahid Beheshti Univ
期刊名称：WATER RESOURCES MANAGEMENT
期刊影响因子：0.0
出版年份：2024
出版刊次：38（11）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台