Intra-Annual Distribution Characteristics And Influencing Factors Of Runoff In A Southwestern Karst Watershed

Karst landscapes cover 7-12% of Earth’s continental area,and approximately 25%of the world’s population partially or completely relies on drinking water from karst aquifers.Water shortages are a challenge worldwide in karst mountainous landscapes.Knowledge of intra-annual variability in runoff and the potential drivers of variability is important for optimizing regional water resources use.In this study,the Gini coefficient and Lorentz asymmetry coefficient were chosen to characterize the intra-annual variation of runoff,and the relationship between influencing factors and intra-annual runoff distribution was decoupled using partial least squares-structural equation modeling(PLS-SEM)through a comprehensive analysis of the intra-annual distribution characteristics of runoff in karst basins at both temporal and spatial scales.The results of the study indicate that.(1)Based on monthly runoff,climate and vegetation data from 2003-2017 for the Wujiang,Liujiang,Yujiang,Hongshui,Xunjiang and Xijiang watersheds,we analyze the mechanisms of climate change and vegetation dynamics on the intra-annual distribution characteristics of runoff on a time scale and conclude that the Gini coefficient varies from 0.15 to 0.59,and the mean value of the Lorentz asymmetry coefficient is greater than 1,which indicates that the intra-annual distribution of runoff The heterogeneity of intra-annual distribution of runoff in the six watersheds is mainly due to the larger proportion of annual runoff in the months with higher runoff volumes;the Gini coefficients of all six watersheds show a decreasing trend,indicating a weakening trend in the distribution of runoff during 2003-2017;climate change and vegetation dynamics strongly influence the intra-annual variation of runoff,which can explain 19-63% and 17-63% of the variation of Gini coefficient and Lorentz asymmetry coefficient,respectively.The Gini coefficient and the Lorentz asymmetry coefficient variation are explained by 19-63% and 17-67%,respectively;vegetation is negatively correlated with the Gini coefficient,and the direct effect of climate on the Gini coefficient is greater than its indirect effect on the Gini coefficient through vegetation.(2)Based on the monthly runoff,vegetation,lithology,soil,geomorphology and climate data of 121 watersheds from 2009-2012,we reveal the response of the intraannual distribution characteristics of runoff to vegetation,lithology,soil,geomorphology and climate factors at the spatial scale,and conclude that the Gini coefficient shows moderate variability and the Lorentz asymmetry coefficient shows low variability,and from 2009-2012 The watersheds with Lorentzian asymmetry coefficients greater than 1 accounted for about 48-75% of the total,indicating that the higher runoff volume caused the uneven intra-annual distribution of runoff in 121 watersheds mainly because the months with higher runoff volume accounted for a larger proportion of the annual runoff;vegetation,lithology,soil,geomorphology and climate variables strongly influenced the intra-annual variability of runoff,and the five variables together explained 52% of the total variability of the Gini coefficient.The Lorentz asymmetry coefficient did not show significant correlations with all five variables,and the five variables together explained only 10% of the total variation in the Lorentz asymmetry coefficient.However,like the Gini coefficient,the Lorentz asymmetry coefficient showed negative correlations with vegetation,soil and geomorphology variables,and positive correlations with lithology and climate variables.This study demonstrates that PLS-SEM is a practical method to elucidate the framework of complex coupled relationships,which can decouple the complex and interrelated relationships between hydrological processes and influencing variables.