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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]

The concept of elasticity has been widely employed to quantify the hydrological response to changes in climate and catchments properties. To separate the effect of different climatic variables on runoff, the potential evaporation (E 0 ) elasticity of runoff needs to be presented in term of observed climate variables. To fully reflect the effects of maximum and minimum temperatures and reduce the influence of the correlations of radiation with sunshine duration and relative humidity on the assessment results, we decompose the E 0 elasticity into five evaporation-related elasticities (i.e., sunshine duration, maximum and minimum temperature, wind speed and relative humidity) via the first-order differentiation of the FAO 56 Penman equation. As the catchment runoff is frequently affected by the land use/cover change, we also consider changes in catchment characteristics and derive a catchment alteration elasticity based on the Budyko framework. An application was carried out in 30 catchments with widespread climatic types in China. For the two periods (i.e., the baseline period and the changed period) divided by the Pettitt test, the contributions of different climatic variables and land use/cover conditions to runoff change were quantified. In general, the alteration of catchment characteristics and climatic change should be mainly responsible for changes in runoff in water-limited and humid basins, respectively. Although the elasticity of maximum temperature are usually higher than that of minimum temperature, the contributions to runoff change present the opposite direction. Furthermore, additional analysis indicated some overestimation in relative humidity elasticities in the previous studies, further emphasizing the necessity of our extension to alleviate the influence of correlation between climatic variables to the assessment results. Moreover, the results of model performance versus model complexity showed that the choice of model complexity still depends on the goal in the climatic and anthropogenic effects on runoff. If the climatic effect as a whole was of interest, the method based on two parameters elasticities would be more applicable compared to that based on multi-parameter elasticities because the simple model provides higher confidence in modelling the overall change in runoff. [ABSTRACT FROM AUTHOR]