The run‐off volume altered by the construction of hydropower plants affects ecohydrological processes in catchments. Although the impacts of large hydropower plants have been well documented in the literature, few studies have been conducted on the impacts of small cascaded hydropower plants (SCHPs). To evaluate the impacts of SCHPs on river flow, we chose a representative basin affected by hydropower projects and, to a lesser degree, by other human activities, that is, the Qiuxiang River basin in Southern China. The observed river discharge and climate data during the period of 1958–2016 were investigated. The datasets were divided into a low‐impact period and a high‐impact period based on the number of SCHPs and the capacities of the reservoirs. The daily river discharge alteration was assessed by applying the Indicators of Hydrologic Alteration. To separate the impact of the SCHPs on the local river discharge from that of climate‐related precipitation, the back‐propagation neural network was used to simulate the monthly average river discharge process. An abnormal result was found: Unlike large reservoirs in large watersheds, the SCHPs regulated the flows during the flood season but were not able to mitigate the droughts during the dry season due to their limited storage and the commonly occurring inappropriate interregulations of the SCHPs. The SCHPs also reduced the annual average river discharge in the research basin. The contribution of the SCHPs to the river discharge changes was 85.37%, much higher than the contributions of climate change (13.43%) and other human activities (1.20%). The results demonstrated that the impacts of the SCHPs were different from those of large dams and reservoirs that regulate floods and relieve droughts. It is necessary to raise the awareness of the impacts of these river barriers. 相似文献
为了研究碱湖N2O释放速率及其对盐度与pH的响应,选取内蒙古大克泊碱湖的五个沉积物样点,采用15N同位素标记模拟实验,研究反硝化和厌氧氨氧化的速率、相对比例和气体产生情况,揭示高盐和高pH对碱湖氮移除的影响。发现大克泊湖潜在氮移除速率为0~16.06 n mol N mL-1 h-1,潜在反硝化速率为0~12.62 n mol N mL-1 h-1,潜在厌氧氨氧化速率为0~9.81 n mol N mL-1 h-1;当盐度34.00 g·L-1与pH 10.22时,厌氧氨氧化对氮移除贡献较大,达到43.18%~71.79%。反硝化过程气体产物以N2为主,几乎无N2O气体释出。另外,该区域潜在氮移除速率与pH呈正相关关系,与TOC、NO-3、HCO-3呈负相关关系;未发现氮移除速率与盐度之间的相关关系。因此,在研究的碱湖中,氮移除过程中主要为N2排放,而N2O低于检测水平;氮移除过程的影响因素复杂且不限于最主要的环境变量(盐度与pH)。这些结果为研究湖泊N2O排放提供了数据基础。 相似文献
With increasing demands for coal resources, coal has been gradually mined in deep coal seams. Due to high gas content, pressure and in situ stress, deep coal seams show great risks of coal and gas outburst. Protective coal seam mining, as a safe and effective method for gas control, has been widely used in major coal-producing countries in the world. However, at present, the relevant problems, such as gas seepage characteristics and optimization of gas drainage borehole layout in protective coal seam mining have been rarely studied. Firstly, by combining with formulas for measuring and testing permeability of coal and rock mass in different stress regimes and failure modes in the laboratory, this study investigated stress–seepage coupling laws by using built-in language Fish of numerical simulation software FLAC3D. In addition, this research analyzed distribution characteristics of permeability in a protected coal seam in the process of protective coal seam mining. Secondly, the protected coal seam was divided into a zone with initial permeability, a zone with decreasing permeability, and permeability increasing zones 1 and 2 according to the changes of permeability. In these zones, permeability rises the most in the permeability increasing zone 2. Moreover, by taking Shaqu Coal Mine, Shanxi Province, China as an example, layout of gas drainage boreholes in the protected coal seam was optimized based on the above permeability-based zoning. Finally, numerical simulation and field application showed that gas drainage volume and concentration rise significantly after optimizing borehole layout. Therefore, when gas is drained through boreholes crossing coal seams during the protective coal seam mining in other coal mines, optimization of borehole layout in Shaqu Coal Mine has certain reference values.
Natural Resources Research - Depletion of shallow mineral resources caused by deep mining has become an inevitable trend, and deep mining can increase safety accidents and geological hazards.... 相似文献
Natural Resources Research - In this study, the adsorption–desorption/induced strains/permeability characteristics of seven columnar coal samples with Ro.ran ranging from 0.42 to 3.23% were... 相似文献
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
