Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

Research on water environmental capacity accounting of the Yongzhou Section of Xiangjiang River Basin based on the SWAT-EFDC coupling model

In this paper, a coupling model of SWAT (Soil and Water Assessment Tool) and EFDC (Environmental Fluid Dynamics Code) was established, and the relationship between the pollution source and water quality response was identified. Based on the hydrodynamic water quality simulation results and the one-dimensional WEC (water environmental capacity) theoretical formula, the total nitrogen and total phosphorus WEC and the remaining WEC of the Yongzhou Section of Xiangjiang River Basin under the guaranteed rate of 90% and in 2017 were calculated, respectively. It can be seen from the results that the total nitrogen WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 27,673.04 t, the total nitrogen WEC under the guaranteed rate of 90% is 19,497.61 t/a and the total phosphorus WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 4,877.22 t. The total phosphorus WEC under the guaranteed rate of 90% is 2,936.64 t/a; in 2017, the remaining WECs of total nitrogen and total phosphorus in the entire basin are 14,646.69 and 3,358.67 t, respectively.

Comprehensive approaches reveal three cryptic species of genus Nidirana (Anura, Ranidae) from China

Three cryptic species, which were previously reported as Nidirana adenopleura, are revealed on the basis of comprehensive approaches. Nidirana guangdongensis Lyu, Wan, and YY Wang, sp. nov. is distributed in Nanling Mountains and southern Luoxiao Mountains, Nidirana mangveni Lyu, Qi, and YY Wang, sp. nov. is known from northern Zhejiang, and Nidirana xiangica Lyu and YY Wang, sp. nov. occurs in Xiangjiang River Basin, while the true Nidirana adenopleura is designated from Taiwan Island, northern Fujian, southern Zhejiang, and central Jiangxi. These three new species can be distinguished from all congeners by significant divergences in the mitochondrial 16S and CO1 genes, differences in advertisement calls, and the combination of multiple characteristics. This work indicates that the current records of Nidirana adenopleura should be of a species complex composed of multiple species and have clarified the true identity of N. adenopleura.

Evaluating Economic Growth, Industrial Structure, and Water Quality of the Xiangjiang River Basin in China Based on a Spatial Econometric Approach

This research utilizes the environmental Kuznets curve to demonstrate the interrelationship between economic growth, industrial structure, and water quality of the Xiangjiang river basin in China by employing spatial panel data models. First, it obtains two variables (namely, CODMn, which represents the chemical oxygen demand of using KMnO4 as chemical oxidant, and NH3-N, which represents the ammonia nitrogen content index of wastewater) by pretreating the data of 42 environmental monitoring stations in the Xiangjiang river basin from 2005 to 2015. Afterward, Moran’s I index is adopted to analyze the spatial autocorrelation of CODMn and NH3-N concentration. Then, a comparative analysis of the nonspatial panel model and spatial panel model is conducted. Finally, this research estimates the intermediate effect of the industrial structure of the Xiangjiang river basin in China. The results show that spatial autocorrelation exists in pollutant concentration and the relationship between economic growth and pollutant concentration shapes as an inverted-N trajectory. Moreover, the turn points of the environmental Kuznets curve for CODMn are RMB 83,001 and RMB 108,583 per capita GDP. In contrast, the turn points for NH3-N are RMB 50,980 and RMB 188,931 per capita GDP. Additionally, the environmental Kuznets curve for CODMn can be explained by industrial structure adjustment, while that for NH3-N cannot. As a consequence, the research suggests that the effect of various pollutants should be taken into account while making industrial policies.