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Abstract:Observed rainfall and flow data from the Dongjiang River basin in humid southern China were used to investigate runoff changes during low-flow and flooding periods and in annual flows over the past 45 years. We first applied the non-parametric Mann-Kendall rank statistic method to analyze the change trend in precipitation, surface runoff and pan evaporation in those three periods. Findings showed that only the surface runoff in the low-flow period increased significantly, which was due to a combination of increased precipitation and decreased pan evaporation. The Pettitt-Mann-Whitney statistical test results showed that 1973 and 1978 were the change points for the low-flow period runoff in the Boluo sub-catchment and in the Qilinzui sub-catchment, respectively. Most importantly, we have developed a framework to separate the effects of climate change and human activities on the changes in surface runoff based on the back-propagation artificial neural network (BP-ANN) method from this research. Analyses from this study indicated that climate variabilities such as changes in precipitation and evaporation, and human activities such as reservoir operations, each accounted for about 50% of the runoff change in the low-flow period in the study basin.