Evapotranspiration Reconstruction Based on Land Surface Models and Observed Water Budget Components While Considering Irrigation

Abstract The accurate estimation of evapotranspiration (ET) is essential for understanding the land surface–atmosphere interaction; however, current ET products have large uncertainties, and irrigation effects on ET are not well represented. In this study, the monthly ET was reconstructed (ETrecon) from GLDAS land surface models (LSMs) over the Yellow River basin of China, which was achieved by using observation-based precipitation, naturalized streamflow, and downscaled consumed irrigation water from the census annual data via an irrigation scheme. The results showed that the monthly ETrecon series were generally improved relative to the original LSM-based ET, with improvements in the correlation coefficient, Nash–Sutcliffe efficiency, mean absolute error, and root-mean-square error by 0.6%–1.8%, 1.2%–14.6%, 1.3%–21.0%, and 2.1%–20.4%, respectively. The ETrecon results were also superior to the collected ET synthesis products in terms of statistics, with generally higher peak values occurring in ETrecon. Regarding the annual time scale, the ETrecon values were close to the water balance ET values, which have been widely used as benchmark data. The interannual variability in ETrecon was good overall and was associated with the LSM precipitation variability and partitioning of precipitation into ET and runoff. The reconstruction method can provide an alternative ET estimate for other river basins. This study will also be valuable for studies and applications in climate change evaluation, drought assessment, and water resources management.