The Mann-Kendall (MK) statistical test has been widely applied in the trend detection of the hydrometeorological time series. Previous studies have mainly focused on the null hypothesis of "no trend" or the "Type I Error." However, few studies address the capability of the MK test to successfully recognize the trends. In some cases, especially when the trend test is jointly applied with hydropower station design, flood risk assessment, and water quality evaluation, the "Type II error" is equally important and should not be neglected. To cope with this problem, we carry out Monte Carlo simulations and the results indicate that in addition to the significance level and the sample length, the MK test power has a close relationship with the sample variance and the magnitude of the trend. For a given time series with fixed length, the power of the MK test increases as the slope increases and declines with increasing sample variance. A deterministic relationship between the slope and the standard deviation of the white noise that can be used for evaluating the power of the MK test has also been detected. Furthermore, we find that a positive autocorrelation contained in the time series will increase both the Type I and the Type II errors due to the enlargement of the variance in the MK statistics. Finally, we recommend that researchers slightly increase the significance level and lengthen the time series sample to improve the power of the MK test in future studies.

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To evaluate the changes in extreme climatic events in the Feilaixia catchment in South China, the spatial and temporal distributions of extreme climate indices trends during 1969-2011 were investigated. With quality control and homogeneity assessment, daily maximum and minimum surface air temperature from 11 meteorological stations and daily precipitation from 24 rainfall stations were used. Eight indices of extreme temperature and six indices of extreme precipitation were chosen. Trends were calculated using Sen's slope estimator. Statistical significance of trends was checked with the Mann-Kendall method. High correlations were found between the mean annual temperature and temperature extremes, as well as between the annual total precipitation and precipitation extremes in most cases. The analyses of extreme temperature indices detected significant and stable trends in the majority of the stations. The strongly stable downward trends in cold extremes and the strongly stable upward trends in hot extremes were recorded in the whole region, except for in some small areas primarily located in the central part of the region. In contrast, significant and stable positive trends were sporadically recorded for precipitation extremes in the study area during 1969-2011, which predominantly occurred in the northern part of the region. Furthermore, the positive trends were much more frequent than the negative trends in most extreme precipitation indices. There were significant and stable trends recorded in most of the average temperature extremes, whereas insignificant and unstable trends were found for most of the average precipitation extremes.

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020–50 would increase under all the scenarios (relative to the baseline period 1970–2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020–50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020–50. The reservoir would be likely to undergo more flooding in 2020–50.