Improving evapotranspiration estimation models through quantitative watershed parameter analysis and remote sensing applications

Abstract Numerous models had been developed to predict the annual evapotranspiration (ET) in vegetated lands across various spatial scales. Fu's ( Scientia Atmospherica Sinica , 5, 23–31) and Zhang's ( Water Resources Research , 37, 701–708) ET simulation models have emerged as highly effective and have been widely used. However, both formulas have the non‐quantitative parameters ( m in Fu's model and w in Zhang's model). Based on the collected 1789 samples from global long‐term hydrological studies, this study discovered significant relations between m (or w ) and vegetation coverage or greenness in collected catchments. Then, we used these relations to qualify the parameters in both Zhang's and Fu's models. Results show that the ET estimation accuracies of Fu's (or Zhang's) model are significantly improved by about 13.49 mm (or 6.74 mm) for grassland and cropland, 38.52 mm (or 29.84 mm) for forest and shrub land (coverage<40%), 19.74 mm (or 16.17 mm) for mixed land (coverage<40%), respectively. However, Zhang's model shows higher errors compared with Fu's model, especially in regions with high m (or w ) values, such as those with dense vegetations or P / E 0 (annual precipitation to annual potential ET) smaller than 1.0. Additionally, this study also reveals that for regions with vegetation cover less than 40%, the annual ET is not only determined by vegetation types, but also relates to the sizes of vegetation‐covered areas. Conversely, for regions with vegetation cover more than 40%, the annual ET is mainly determined by the vegetation density rather than vegetation types or vegetation coverage. Thus, linking m (or w ) parameters with vegetation greenness allows leveraging remote sensing for forest management in data‐scarce areas, safeguarding regional water resources. This study pioneers integrating vegetation‐related indices with basin parameters, advocating for their crucial role in more effective hydrological modelling.