Acoustic signals emitted during rock fracture constitute an important tool for rock damage evaluation. To investigate the evolutionary characteristics of acoustic emission (AE), microseismic (MS), and sound signals produced by hard rock fracture, uniaxial compression tests on granite specimens observed by AE, MS and sound monitoring were carried out. The evolution characteristics of the acoustic signal index, including its waveform, fractal dimension, b value, main frequency, energy proportion of signal frequency bands, and signal activeness, were analysed. The results indicate that there are significant differences in some characteristics of the AE, MS, and sound on the eve of granite failures, such as the waveform amplitude density, the average decline rate of the b value, the distribution of the main frequency, and the evolution of the energy proportion of the advantage frequency band. The three types of acoustic signals can characterize different scales of rock fracture under uniaxial compression. AE is sensitive to small-scale rock fractures, and MS and sound are sensitive to large-scale rock fractures. In addition, a unified damage evolution equation established by acoustic signals is proposed to quantitatively describe the damage process of granite specimens during uniaxial compression tests.
The frequent occurrence of extreme drought events in Guangxi has caused huge losses to human beings and economy in the region for many years.Therefore, this study adopted run theory and the objective identification method of regional extreme events (OITREE) and then carried out the comprehensive feature identification of multidimensional elements such as intensity, duration and area of meteorological drought events based on the daily standardized effective precipitation and drought index (SWAP) sequence of Guangxi from 1979 to 2018a. By comparing the evolutionary characteristics of
In order to solve the problem that the existing optimal operation model of reservoirs cannot coordinate the contradiction between long-term and short-term benefits, the paper nested the long-term optimal operation and mid-long-term optimal operations of reservoirs and established the multi-objective optimal operation nested model of reservoirs. At the same time, based on this model, the optimal control mode is determined when there are errors in the predicted runoff. In the optimal scheduling nested model, the dynamic programming algorithm is used to determine the long-term optimal scheduling solution, and the genetic algorithm is used to solve the mid-long-term optimal scheduling. The optimal control mode is determined by three indicators: power generation benefit, water level over limit risk rate and the not-exploited water volume. The results show that, on the premise of meeting the flood control objectives, the nested model optimal dispatching plan has higher benefits than the long-term optimal dispatching plan and the actual dispatching plan, which verifies the superiority of the nested model in the reservoir optimal dispatching problem. When there is error in predicting runoff, among the water level control mode, flow control mode and output control mode, the average power generation benefit of output control mode is 150.05 GW·h, the low-risk rate of water level overrun is 0.29, and the not-exploited water volume is 39,270 m3. Compared with the water level control mode and the flow control mode, the output control mode has the advantages of higher power generation efficiency, lower water level over limit risk rate and less not-exploited water volume. Therefore, from the perspective of economic benefit and risk balance, the output control mode in the optimization scheduling nested mode is the optimal control mode.
Karst basins have a relatively low capacity for water retention, rendering them very vulnerable to drought hazards. However, karst geo-climatic features are highly spatially heterogeneous, making reliable drought assessment challenging. To account for geo-climatic heterogeneous features and to enhance the reliability of drought assessment, a framework methodology is proposed. Firstly, based on the history of climate (1963–2019) from the Global Climate Model (GCM) and station observations within the Chengbi River karst basin, a multi-station calibration-based automated statistical downscaling (ASD) model is developed, and the Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) are selected as performance metrics. After that, future climate (2023–2100) under three GCM scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are obtained by using the ASD model. Finally, the Standardized Precipitation Evapotranspiration Index (SPEI), calculated by future climate is applied to assess drought conditions. The results indicate that the multi-station calibration-based ASD model has good performance and thus can be used for climate data downscaling in karst areas. Precipitation mainly shows a significant upward trend under all scenarios with the maximum variation (128.22%), while the temperature shows a slow upward trend with the maximum variation (3.44%). The drought condition in the 2040s is still relatively severe. In the 2060s and 2080s, the basin is wetter compared with the historical period. The percentage of drought duration decreases in most areas from the 2040s to the 2080s, demonstrating that the future drought condition is alleviated. From the SSP1-2.6 scenario to the SSP5-8.5 scenario, the trend of drought may also increase.
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