Extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Abstract Extreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme 5 precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated. It was shown that little change is observed in annual extreme precipitation in terms of various indices, but some significant changes are found in the precipitation processes on a monthly basis. The result indicates that when detecting climate changes, besides annual indices, seasonal variations in extreme events 10 should be considered as well. Despite of little change in annual extreme precipitation series, significant changes are detected in several annual extreme flood flow and low-flow series, mainly at the stations along the main channel of Dongjiang River, which are affected significantly by the operation of several major reservoirs. The result highlights the importance of evaluating the impacts of human activities in assessing the 15 changes of extreme streamflows. In addition, three non-parametric methods that are not-commonly used by hydro-meteorology community, i.e., Kolmogorov–Smirnov test, Levene's test and quantile test, are introduced and assessed by Monte Carlo simulation in the present study to test for changes in the distribution, variance and the shift of tails of different groups of dataset. Monte Carlo simulation result shows that, while 20 all three methods work well for detecting changes in two groups of data with large data size (e.g., over 200 points in each group) and big difference in distribution parameters (e.g., over 100% increase of scale parameter in Gamma distribution), none of them are powerful enough for small data sets (e.g., less than 100 points) and small distribution parameter difference (e.g., 50% increase of scale parameter in Gamma distribution).