Estimating monthly evapotranspiration by assimilating remotely sensed water storage data into the extended Budyko framework across different climatic regions

Abstract The estimates of actual evapotranspiration ( E ) across basins and/or regions are still facing some difficulties because of the complex interactions amongst the components of the land-plant-atmosphere system, even though the basic physical mechanism of E is well understood. Presenting as a nonlinear relationship constrained by physical limits, the Budyko framework serves as a powerful tool used to estimate the averaged E at long-term scale. Given the ability of Gravity Recovery And Climate Experiment (GRACE) in effectively simulating the terrestrial water storage change (Δ TWS ) at monthly scale, a model to estimate E was developed based on the Budyko framework with mean monthly parameter of multi-years, and was applied to the estimation of E for the 24 selected catchments in different climatic regions across China. Results indicate that for the majority of 24 catchments, the positive or negative trends in precipitation ( P ), potential evapotranspiration ( E 0 ), runoff ( R ) and Δ TWS were not statistically significant during 2003-2013 at both annual and monthly scales, but the Δ TWS occupied a large proportion in the partitioning of P into R and E at monthly scale. The monthly Budyko parameter showed large variation within the year and also across these 24 catchments. The Budyko-modeled monthly E (Budyko-E) represented the GRACE-derived ones (GRACE-E) across these catchments well with both modeled E volumes and hydrograph shapes which were also consistent with GRACE-E series except some underestimation for peak E . Overall, the Budyko-E represented GRACE-E in the arid and semi-arid catchments comparatively better than it in the humid and semi-humid catchments due to the larger proportion of Δ TWS in the P partition. Among all the 24 selected catchments, the monthly Budyko-type E model represented the GRACE-E across Shixiali catchment (located in the Hai River basin) best, with the correlation coefficient ( r ), Nash-Sutcliffe coefficient ( NSCE ) and relative error ( RE ) between GRACE-E and simulated results being 0.996, 0.988, -0.033 in calibration and 0.994, 0.951, -0.111 in validation, respectively. Furthermore, using GRACE-E as the benchmark values, the Budyko-E outperformed four other global E products from the newly published Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS_E), MODIS (MODIS_E), Japanese 25-year reanalysis product (JRA_E), and Zhang’s method (Zhang_E). GLDAS_E was the best of four global E products at reproducing monthly variations, with relatively high r and NSCE values, small RE . While MODIS_E showed the poorest performance in reproducing the inter-annual E variations of these catchments, which was reflected by low r and NSCE values. This extended Budyko-E model can apply in other similar climatic regions and also may provide skill in evaluating the water resources since the preferable accuracy of E estimate.