Abstract

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day-1) and catchment scales (mean RMSE = 1.5 mm day-1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.