The Caohai Wetland serves as an important ecosystem on the Yunnan–Guizhou Plateau and as a nationally important nature reserve for migratory birds in China. In this study, surface water, groundwater and wetland water were collected for the measurement of environmental isotopes to reveal the seasonal variability of oxygen and hydrogen isotopes (δ18O, δD), sources of water, and groundwater inflow fluxes. Results showed that surface water and groundwater are of meteoric origin. The isotopes in samples of wetland water were well mixed vertically in seasons of both high-flow (September) and low-flow (April); however, marked seasonal and spatial variations were observed. During the high-flow season, the isotopic composition in surface wetland water varied from ?97.13 to ?41.73‰ for δD and from ?13.17 to ?4.70‰ for δ18O. The composition of stable isotopes in the eastern region of this wetland was lower than in the western region. These may have been influenced by uneven evaporation caused by the distribution of aquatic vegetation. During the low-flow season, δD and δ18O in the more open water with dead aquatic vegetation ranged from ?37.11 to ?11.77‰, and from ?4.25 to ?0.08‰, respectively. This may result from high evaporation rates in this season with the lowest atmospheric humidity. Groundwater fluxes were calculated by mass transfer and isotope mass balance approaches, suggesting that the water sources of the Caohai Wetland were mainly from groundwater in the high-flow season, while the groundwater has a smaller contribution to wetland water during the low-flow season. 相似文献
In the present study, laboratory experiments were conducted to validate the applicability of a numerical model based on one-dimensional nonlinear long-wave equations. The model includes drag and inertia resistance of trees to tsunami flow and porosity between trees and a simplified forest in a wave channel. It was confirmed that the water surface elevation and flow velocity by the numerical simulations agree well with the experimental results for various forest conditions of width and tree density. Further, the numerical model was applied to prototype conditions of a coastal forest of Pandanus odoratissimus to investigate the effects of forest conditions (width and tree density) and incident tsunami conditions (period and height) on run-up height and potential tsunami force. The modeling results were represented in curve-fit equations with the aim of providing simplified formulae for designing coastal forest against tsunamis. The run-up height and potential tsunami forces calculated by the curve-fit formulae and the numerical model agreed within ± 10% error. 相似文献
The priority of flood management planning is physical victimization and focuses on taking structural measures. Although this approach is an accurate approach, more information is needed in implementing efficient precautionary and planning decisions. It is an indisputable fact that the existence of nothing that is not sustainable in nature cannot continue. Hence, it is necessary to implement a planning decision suitable for the structure of the population living in the region so that the continuity of the policies to be carried out against natural hazards of hydrometeorological origin such as a flood is ensured. How the socio-demographic structures affect the flood risk perception of 245 people living in the city center of Bayburt is examined in this study. It is the first research conducted for the province of Bayburt for this perspective. The participants were asked to fill a questionnaire containing 24 items and consisting of 2 sections. T test and one-way ANOVA (one-way analysis of variance) statistical methods were used to ascertain the difference between the responses of the participants to the questionnaire, based on their demographic structure. As the result of the study, significant differences were observed between the expressions depicting flood risk perception and the participant's age, income levels and educational background. In addition, it has been noted that there is a positive relationship between education and income levels and flood risk perception.
Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In... 相似文献
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation. 相似文献