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During hydrological, climatic, water, and agricultural studies, accurate estimation of actual evapotranspiration (ET) plays a critical role. However, providing reliable estimates has become increasingly challenging. Precise estimation of ET is essential for predicting ecosystem responses to land use and climate changes. Remote sensing-based techniques offer effective methods and algorithms for estimating actual ET at different scales, from regional to global. In this study, we utilized the Penman–Monteith–Leuning (PML-V2) model, implemented in Google Earth Engine (GEE), to estimate 8 day and monthly global ET at a 500 m resolution from January 2000 to December 2010. The goal was to validate the actual ET data for various regions in Iran with diverse climates, during different times and seasons. We employed estimated ET data from previous climatic studies in Iran, derived from various models, observations, and global products, for validation purposes. The assessment encompassed three different climate types: hot humid, hot semi-arid, and cold semi-arid, representing the north, west, and northwest regions of Iran, respectively. The results demonstrated that the PML-V2 model exhibited good agreement with measurements at the monthly timescale. Determination coefficient (DC) values were 0.93, 0.81, and 0.94, while the correlation coefficients (CC) were 0.96, 0.90, and 0.97, and root mean square error (RMSE) values were 0.15, 0.79, and 0.21 for ET in the hot semi-arid, hot humid, and cold semi-arid regions, respectively. Our analysis indicates that the PML-V2 ET model is suitable for estimating ET over Iran at various regional and temporal scales.

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Validation of PML-V2 Evapotranspiration Model Over Multi-climatic Regions of Iran

  • 会议论文

  • 发表日期：

    2025年

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  • Vahid Nourani,
  • Ramin Ahmadi,
  • Aida Hosseini Baghanam,
  • Elnaz Bayat Khajeh,
  • Ali Gholinia

摘要

During hydrological, climatic, water, and agricultural studies, accurate estimation of actual evapotranspiration (ET) plays a critical role. However, providing reliable estimates has become increasingly challenging. Precise estimation of ET is essential for predicting ecosystem responses to land use and climate changes. Remote sensing-based techniques offer effective methods and algorithms for estimating actual ET at different scales, from regional to global. In this study, we utilized the Penman–Monteith–Leuning (PML-V2) model, implemented in Google Earth Engine (GEE), to estimate 8 day and monthly global ET at a 500 m resolution from January 2000 to December 2010. The goal was to validate the actual ET data for various regions in Iran with diverse climates, during different times and seasons. We employed estimated ET data from previous climatic studies in Iran, derived from various models, observations, and global products, for validation purposes. The assessment encompassed three different climate types: hot humid, hot semi-arid, and cold semi-arid, representing the north, west, and northwest regions of Iran, respectively. The results demonstrated that the PML-V2 model exhibited good agreement with measurements at the monthly timescale. Determination coefficient (DC) values were 0.93, 0.81, and 0.94, while the correlation coefficients (CC) were 0.96, 0.90, and 0.97, and root mean square error (RMSE) values were 0.15, 0.79, and 0.21 for ET in the hot semi-arid, hot humid, and cold semi-arid regions, respectively. Our analysis indicates that the PML-V2 ET model is suitable for estimating ET over Iran at various regional and temporal scales.