An Environmental Flow Framework for Riverine Macroinvertebrates During Dry and Wet Seasons Through Non-linear Ecological Modeling

A suitable environmental flow is critical for the functional maintenance of riverine ecosystems. Hydropower plants alter the flow regime by decreasing or even drying up the streamflow downstream of the dams, thereby affecting ecosystem sustainability. In this study, we aimed to develop a robust environmental flow framework that can provide scientific evidence for sustainable water resource management. Using ecological niche modeling based on non-linear responses of species to habitat factors, we assessed the environmental flow in the Xiangxi River Basin of Central China during dry and wet seasons from a multi-year perspective. The most abundant macroinvertebrate taxon (i.e., Baetis) was selected for model testing. The results showed seasonal differences in the minimum ecological water requirements and optimal environmental flow. These two hydrological metrics were higher during the wet season than during the dry season. During the dry season, the minimum ecological water requirement of Baetis was 1.3 m3·s−1, and the optimal environmental flow was 1.6 m3·s−1. During the wet season, the minimum ecological water requirement of Baetis was 2.5 m3·s−1, and the optimal environmental flow was 2.6 m3·s−1. This study provides a theoretical basis for the robust management of water resources in river basins.

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.

Effectivity and Efficiency of Best Management Practices Based on a Survey and SWAPP Model of the Xiangxi River Basin

A questionnaire survey was conducted among farmers in the Xiangxi River Basin to investigate the local livestock situation and the farmers’ understanding of and attitude towards pollution. The results showed that local farmers lacked environmental awareness and few livestock and poultry pollution treatment measures had been implemented. However, once farmers understood that livestock pollution would greatly influence their lives and interests, they would act to prevent Agricultural non–point source (ANPS) pollution. The farmers’ education level and satisfaction with the environment were the main factors affecting their awareness regarding ANPS pollution. The “Comprehensive Environmental Optimization Tool SWAT–APEX Interface” model (SWAPP) was used to simulate the reduction of ANPS by different best management practices (BMPs) and the construction cost was calculated. The results showed that compound bedding and piping systems and ponds were the most effective and economic measures for reducing ANPS pollution. Spatially, implementing BMPs in the upstream region was better for improving water quality. The nitrate reduction rate in upstream sub–basins reached 90%, which is 30% larger than that in downstream sub–basins with combined bedding and piping systems. Combining the farmers’ awareness of and engagement in livestock pollution with cost–effective BMPs can improve the BMPs’ effectivity and efficiency.

Features of Oxygen and Hydrogen Isotopes in Waters from the Karst Mountains, Xiangxi River Basin

Targeting the carbonate-dominated catchment of Xiangxi River, the first tributary of Three Gorges Reservoir, this paper explores the spatiotemporal distribution of stable isotopes in the stream water, and distinguishes the water sources and recharge processes in the river basin. Multiple water samples were collected from the catchments of the main tributaries of Xiangxi River, namely, Nanyang River and Gaolan River. The results show that the two tributary catchments had obvious spatial and seasonable variability in δ18O and δD. This is attributable to the seasonal changes in meteoric precipitation recharge, which depends on ambient temperature, precipitation, evaporation, and secondary evaporation of raindrops. Besides, there is a strong elevation effect on stable isotopes in stream water. In the samples from Gaolan River, the mean elevation effect on the δ18O and δD was -0.35‰, and -1.57‰ per 100m change in elevation in summer, and -0.12‰, and -1.19‰ per 100m change in elevation in winter, respectively. In the samples from Nanyang River, the mean elevation effect on the δ18O and δD was -0.14‰ and -0.89‰ per 100m change in elevation in summer, and -0.08‰ and -0.66‰ per 100m change in elevation in winter, respectively. The δD, δ18O, and deuterium excess (d-value) are not only affected by the elevation effect, but also by the seasonal effect in the study area. The seasonal variations and spatial changes in the tributary catchments reveal the difference between the two catchments in control factors like latitude effect, evaporation, and the mixing of different water sources.