Performance Evaluation of Watershed Environment Governance—A Case Study of Taihu Basin

The rapid development of urbanization and industrialization brings a series of problems of environment governance, and several basins are facing huge pressure. This paper selects the Taihu basin in the Yangtze River Delta of China as the study area, establishes the DPSIR model to measure the water environment governance performance of the region (), analyzes the causes of changes in the five subsystems (the governance performance of the subsystems is recorded as ), and uses the diagnostic model to identify the barrier factors that restrict the improvement of in the last 5 years. The results show that during the study period， of the driving force subsystem generally tends to increase and maintains a steady growth, which is closely linked to economic growth in the basin; of the pressure subsystem increases with a small fluctuation, and the pollution generation still needs attention; in the state subsystem, shows a large fluctuation, and varies significantly in a cyclical manner, corresponding to the short maintenance time and repeated treatment of pollution in the watershed; of the impact subsystem shows an overall upward but a slightly slower trend, and it is related to the fact that the industrial structure of the basin still needs to be improved; and of the response subsystem shows an overall upward trend and a slightly larger increase, and the multi-actor collaborative management has helped a lot. The main barrier factors include key cross-sections’ water quality compliance rate, the water quality compliance rate of key water function areas, water consumption per 10,000 yuan of industrial added value, and the lake trophic status index. Based on the results of the study, the article gives recommendations for watershed governance, such as controlling pollution generation, optimising industrial structure, using technological tools to help governance, sharing the cost of governance among multiple parties and strengthening supervision The findings help to make scientific environmental protection planning and policies of the study region. The research can also provide experience for other countries and regions in watershed governance.

Study on the proportion and flow path of Yangtze River water diversion into Taihu Lake

The purpose of this study is to quantify the proportion and flow path of the water diversion from Yangtze River (YRD) into Taihu Lake. Based on the analysis of rainfall and data of Taihu basin in recent 30 years, a 1-D hydrodynamic model of main inflow river network area of Taihu basin was constructed, coupled the convection-diffusion model with conservative material, the characteristics of YRD and the water inflow into Taihu Lake (WITL) in three typical years were calculated. The results show that the YRD has shown a significant upward trend in the past 30 years, accounting for 26.4, 35.6 and 42% of the total WITL in three typical years of wet, normal and dry respectively. From the perspective of space, Taige River is the largest river in the western part of the lake that is affected by the river diversion (35%–72%), and Wuxi River is the smallest (1–3%). In addition, the primary flow path of YRD to Taihu Lake was through the Wuyi River and Lake Gehu from the water diversion station west of the Zao River.

Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

Reliable flood forecasting can provide scientific basis for flood risk assessment and water resources management, and the Taihu water level forecasting with high precision is essential for flood control in the Taihu Basin. To increase the prediction accuracy, a coupling model (DWT-iNARX) is established by combining the discrete wavelet transformation (DWT) with improved nonlinear autoregressive with exogenous inputs network (iNARX), for predicting the daily Taihu water level during the flood season under different forecast periods. And the DWT-iNARX model is compared with the back-propagation neural network (BP) and iNARX models to assess its capability in prediction. Meanwhile, we propose an uncertainty analysis method based on Monte Carlo simulations (MCS) for quantifying model uncertainty and performing probabilistic water level forecast. The results show that three models achieve good simulation results with higher accuracy when the forecast period is short, such as 1–3 days. In overall performance, iNARX and DWT-iNARX models show superiority in comparison with the BP model, while the DWT-iNARX model yields the best performance among all the other models. The research results can provide a certain reference for the water level forecast of the Taihu Lake.