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Rock burst is one of the most severe dynamic hazards in the underground mining industry, and it is essential to establish a reliable method to achieve accurate prevention and control of it. This paper proposes a comprehensive spatial and temporal rockburst warning method based on the Mann-Kendall trend test (MKT) and spatially smoothed seismicity model, which can evaluate rockburst risk in real-time and quantitatively. The method uses real-time values of rockburst warning indicators as input, uses MKT to determine the temporal trend of each indicator and takes whether the trend conforms to the rockburst precursor law as the criterion for triggering the alarm. Then combined with the confusion matrix to evaluate the warning effectiveness of each indicator and optimize the indicators, and then obtain the real-time temporal rockburst hazard index Q of the mining area through data fusion. Finally, the spatial rockburst hazard index S contour map is drawn using the spatially smoothed seismicity model to identify local rockburst hazard zones and assist mine personnel in taking preventive and control measures of rockburst in time. The results show that the proposed model considers the precursor information of rockburst evolution from "time-space-strength" multi-dimensionally. The warning effectiveness of Q reaches 0.431, which is higher than any single indicator. The local rockburst hazard zone shown in the contour map of S is consistent with the actual location of strong tremors and rockburst events. The model has been successfully applied in the Wudong coal mine in Xinjiang, China, which can assist mine personnel in making efficient and accurate rockburst prevention decisions and effectively reduce their prevention costs. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to detect trends in mean flow and total precipitation data over southern parts of Quebec and Ontario, Canada. The main purpose of the trend assessment is to find out what time scales are affecting the trends observed in the datasets used. In this study, a new trend detection method for hydrological studies is explored, which involves the use of wavelet transforms (WTs) in order to separate the rapidly and slowly changing events contained in a time series. More specifically, this study co-utilizes the Discrete Wavelet Transform (DWT) technique and the Mann–Kendall (MK) trend tests to analyze and detect trends in monthly, seasonally-based, and annual data from eight flow stations and seven meteorological stations in southern Ontario and Quebec during 1954–2008. The combination of the DWT and MK test in analyzing trends has not been extensively explored to date, especially in detecting trends in Canadian flow and precipitation time series. The mother wavelet type and the extension border used in the wavelet transform, as well as the number of decomposition levels, were determined based on two criteria. The first criterion is the mean relative error of the wavelet approximation series and the original time series. In addition, a new criterion is proposed and explored in this study, which is based on the relative error of the MK Z-values of the approximation component and the original time series. Sequential Mann–Kendall analysis on the different wavelet detail components (with their approximation component added) that result from the time series decomposition was also used and found to be helpful because it depicts how harmonious each of the detail components (plus approximation) is with respect to the original data. This study found that most of the trends are positive and started during the mid-1960s to early 1970s. The results from the wavelet analysis and Mann–Kendall tests on the different data types (using the 5% significance level) reveal that in general, intra- and inter-annual events (up to 4years) are more influential in affecting the observed trends. [Copyright &y& Elsevier]

Summary: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]