Analysis of influencing factors on Budyko parameter and the application of Budyko framework in future runoff change projection EGU2018-14137 Weiguang Wang State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, wangweiguang@hhu.edu.cn Introduction Results indicated that the multivariate adaptive regression splines model considering the interaction of different factors showed slightly higher precision than the stepwise regression model on the parameter w simulation. a certain range in the projection of precipitation change, which is 19 usually larger than that of potential evaporation, can be found in diverse future scenarios and GCMs. We can still sort out a clear linear relationship between relative change in runoff and precipitation. Hence, it is evident that the effect of precipitation on runoff change is more significant than that of potential evaporation, that is, precipitation is the main driver of runoff change in the projections. Budyko framework serves as a powerful tool to make simple and transparent estimation for the partition with a single parameter. To extend the theory in predictability of water availability, we employed the multivariate adaptive regression splines model to estimate the dominant interactions between climatic seasonality, catchment characteristics and agricultural activities on Budyko-type parameter investigation. Subsequently, we established a climate elasticity method based on Budyko hypothesis to project future runoff change and estimate its key influencing factors to strengthen the runoff change prediction reliability Figure 5 Elasticity of annual runoff to (a) precipitation and (b) potential evapotranspiration in the 35 catchments. Discussion and conclusions Previous studies have found that land surface characteristics, including vegetation, soil types, and topographic features, have significant impacts on variations in the parameter w. In this study, a multivariate adaptive regression spline (MARS) model for variable selection was proposed to estimate the model parameter. The MARS model revealed that only six factors were selected in the final model. The results showed that the model’s performance was generally satisfactory when the values of explanatory variables of a testing catchment were within the data domain of calibration catchments. It is therefore potentially useful for the prediction in ungauged basins as long as the required climatic factors and catchment characteristics are available. Regional impacts of climate change on hydrology need to be quantitatively investigated. Our work showed that the effect of precipitation on runoff change is more significant than that of potential evaporation. Overall, the results of the present study can serve as a reference for regional water resources management and planning. Figure 2 An overview of the datasets, procedures, and models used in projection of Future Runoff Change. Results Study area The mean values of P and E0 elasticities to runoff are 1.972 and -0.972, respectively, indicating a 10% increase in P would increase Q by 19.72% on average, while a 10% increase in E0 would decrease Q by 9.72% on average. Obviously, runoff changes in these catchments are more sensitive to the temporal variabilities of precipitation.. The absolute values of runoff elasticity are relatively high in the Hai River Basin, lower reaches of Yellow River Basin and Yangtze River Basin, indicating that runoff is more sensitive to changes in climate variables in these regions. Figure 3 Plot of the effect of the interaction on the model parameter w in MARS. Figure 1 The study area Spanning different climatic conditions, a total of 96 catchments (including catchments in Songhua River basin, Liao River basin, Hai River basin, Yellow River basin, Huai River basin, Yangtze River basin, Pearl River basin, Yalungzangbo River basin, etc) selected in this study are from China. The interactions between explanatory factors (effective irrigation area and seasonality index of precipitation, Palmer drought index and relief ratio, average storm depth and Milly’s index of seasonality, Palmer drought index and seasonality index of precipitation) can effect the estimation of parameter in Budyko framework. Acknowledgements Materials and methods This work was jointly supported by the National Science Foundation of China (51779073, 51379057), the Fundamental Research Funds for the Central Universities (2017B21414), the National ‘‘Ten Thousand Program” Youth Talent, the Six talent peaks project in Jiangsu Province, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Data Daily meteorological data from the weather stations data Basin characteristics date including land use, topographic characteristics, and soil properties Methods Budyko-type equation Multivariate adaptive regression spline model (MARS) Climate Elasticity Method based on Budyko Hypothesis Figure 4 Comparison between simulated and observed w for all catchments. (a) MSR model, (b) MARS model. Figure 6 Contributions of climate change and anthropogenic activity on Budyko parameter w on typical catchment.