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Graphical abstract Highlights • Water storage change occupies a large proportion in the precipitation partitioning at monthly scale. • Estimate monthly evapotranspiration by combining GRACE data into extended Budyko framework. • Budyko-E represents GRACE-E in arid regions comparatively better than it in humid regions. • Budyko monthly evapotranspiration outperforms four other global evapotranspiration products. 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. [ABSTRACT FROM AUTHOR]

This study established a climate elasticity method based on Budyko hypothesis and enhanced it by selecting the most effective Budyko-type formula to strengthen the runoff change prediction reliability. The spatiotemporal variations in hydrologic variables (i.e., runoff, precipitation and potential evaporation) during historical period were revealed first and the climate elasticities of runoff were investigated. The proposed climate elasticity method was also applied to project the spatiotemporal variations in future runoff and its key influencing factors in 35 watersheds across China. Wherein, the future climate series were retrieved by consulting the historical series, informed by four global climate models (GCMs) under representative concentration pathways from phase five of the Coupled Model Intercomparison Project. Wang-Tang equation was selected as the optimal Budyko-type equation for its best ability in reproducing the runoff change (with a coefficient of determination and mean absolute error of 0.998 and 1.36 mm, respectively). Observed runoff presents significant decreasing trends in the northern and increasing trends in the southern regions of China, and generally its change is identified to be more sensitive to climatic variables in Hai River Basin and lower Yellow River Basin. Compared to the runoff during the reference period, positive change rates in the north and negative change rates in the south of China in the mid-21st century can be practically generalized from the majority of GCMs projections. This maybe resulted from the increasing precipitation, especially in parts of northern basins. Meanwhile, GCMs project a consistently upward trend in potential evaporation although significant decreasing trends occur in the majority of catchments for the historical period. The results indicate that climate change will possibly bring some changes to the water resources over China in the mid-21st century and some countermeasures of water resources planning and management should be taken. [ABSTRACT FROM AUTHOR]

Quantifying precipitation (P) partition into evapotranspiration (E) and runoff (Q) is of great importance for global and regional water availability assessment. Budyko framework serves as a powerful tool to make simple and transparent estimation for the partition, using a single parameter, to characterize the shape of the Budyko curve for a “specific basin”, where the single parameter reflects the overall effect by not only climatic seasonality, catchment characteristics (e.g., soil, topography and vegetation) but also agricultural activities (e.g., cultivation and irrigation). At the regional scale, these influencing factors are interconnected, and the interactions between them can also affect the single parameter of Budyko-type equations’ estimating. Here we employ the multivariate adaptive regression splines (MARS) model to estimate the Budyko curve shape parameter ( n in the Choudhury’s equation, one form of the Budyko framework) of the selected 96 catchments across China using a data set of long-term averages for climatic seasonality, catchment characteristics and agricultural activities. Results show average storm depth (ASD), vegetation coverage (M), and seasonality index of precipitation (SI) are three statistically significant factors affecting the Budyko parameter. More importantly, four pairs of interactions are recognized by the MARS model as: The interaction between CA (percentage of cultivated land area to total catchment area) and ASD shows that the cultivation can weaken the reducing effect of high ASD (>46.78 mm) on the Budyko parameter estimating. Drought (represented by the value of Palmer drought severity index < -0.74) and uneven distribution of annual rainfall (represented by the value of coefficient of variation of precipitation > 0.23) tend to enhance the Budyko parameter reduction by large SI (>0.797). Low vegetation coverage (34.56%) is likely to intensify the rising effect on evapotranspiration ratio by IA (percentage of irrigation area to total catchment area). The Budyko n values estimated by the MARS model reproduce the calculated ones by the observation well for the selected 96 catchments (with R = 0.817, MAE = 4.09). Compared to the multiple stepwise regression model estimating the parameter n taken the influencing factors as independent inputs, the MARS model enhances the capability of the Budyko framework for assessing water availability at regional scale using readily available data. [ABSTRACT FROM AUTHOR]