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<p>In irrigated agriculture, the estimation of actual evapotranspiration (ETa) is significant to effective water resource management. The dearth of actual ETa observations, particularly in developing countries, is a crucial limitation in attaining water security for food production. Furthermore, ETa estimation often involves high-cost instruments and labor-intensive routines, and since ETa is highly variable over space and time, extrapolated point ETa data is insufficient in representing large areas such as irrigated cropping systems. This study used the Python-based Surface Energy Balance Algorithm for Land (PySEBAL) model assimilated with remotely sensed datasets to quantitatively assess ETa over the NIA Magat River Integrated Irrigation System (MARIIS) Division IV service areas in the Philippines during dry (DS) and wet (WS) cropping seasons from 2015 to 2023. The model estimated a higher average ETa during WS (5.54&#xa0;mm/day) compared to DS (4.23&#xa0;mm/day). The seasonal values were found to be associated with the meteorological parameters and the prevailing climate in the area. Spatial analysis indicates that areas near the boundary line and built-up areas have lower ETa values, while areas near main and lateral canals have higher ETa values, implying that areas near the water supply tend to have higher ETa values. Sound estimation of ETa is crucial in comprehensive water accounting, particularly for efficient water allocation and distribution. The results of the study are valuable for management operations and in promoting effective water utilization.</p>

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Spatiotemporal assessment of actual evapotranspiration using remote sensing-based PySEBAL model over lowland rice irrigation scheme in the Philippines

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Alvin John B. Felipe,
  • Ronaldo B. Saludes,
  • Rubenito M. Lampayan,
  • Patrick Lemuel P. Relativo

摘要

In irrigated agriculture, the estimation of actual evapotranspiration (ETa) is significant to effective water resource management. The dearth of actual ETa observations, particularly in developing countries, is a crucial limitation in attaining water security for food production. Furthermore, ETa estimation often involves high-cost instruments and labor-intensive routines, and since ETa is highly variable over space and time, extrapolated point ETa data is insufficient in representing large areas such as irrigated cropping systems. This study used the Python-based Surface Energy Balance Algorithm for Land (PySEBAL) model assimilated with remotely sensed datasets to quantitatively assess ETa over the NIA Magat River Integrated Irrigation System (MARIIS) Division IV service areas in the Philippines during dry (DS) and wet (WS) cropping seasons from 2015 to 2023. The model estimated a higher average ETa during WS (5.54 mm/day) compared to DS (4.23 mm/day). The seasonal values were found to be associated with the meteorological parameters and the prevailing climate in the area. Spatial analysis indicates that areas near the boundary line and built-up areas have lower ETa values, while areas near main and lateral canals have higher ETa values, implying that areas near the water supply tend to have higher ETa values. Sound estimation of ETa is crucial in comprehensive water accounting, particularly for efficient water allocation and distribution. The results of the study are valuable for management operations and in promoting effective water utilization.