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Owing to the publicity and externality of watershed governance, collaborative governance among cities has become an inevitable choice to improve watershed governance performance and promote sustainable development of watersheds. Existing studies have provided many enlightenment on promoting watershed collaborative governance (WCG), while most of them investigated WCG from single or several dimensions with scanty quantitative empirical studies. Against this background, this study aims to establish a comprehensive WCG research framework involving multiple phases and diverse actors to empirically measure the synergy degree of WCG. Specifically, this study constructs a WCG index system composed of resource investment, open cooperation, and performance supervision subsystems that involves actors of government, enterprise, and the public. Using the complex system synergy model, an empirical analysis is performed to evaluate the synergy degree of Taihu Basin collaborative governance among and within cities in Jiangsu province, China during 2014–2020. The results reveal that the Tai Basin collaborative governance among cities was still in the run-in adaptation stage, which was primarily constrained by the discrete open cooperation subsystems among cities. The synergy degree of Taihu Basin collaborative governance within cities presented significant differences. Several targeted implications are proposed according to the results. This study provides a comprehensive index system for synergy degree measurement of WCG and could offer effective guidance for policymakers to design effective strategies for improving WCG performance. [ABSTRACT FROM AUTHOR]

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions. [ABSTRACT FROM AUTHOR]

This paper presents the application of a distributed-framework basin modeling system (DFBMS) in Taihu Basin, China. The concepts of professional modeling systems and system integration/coupling have been summarized in the first three series papers. This study builds a hydrologic and hydrodynamic model for Taihu Basin, which is in the lowland plain areas with numerous polder areas. Digital underlying surface area data agree with the survey results from the water resource development and utilization. The runoff generated in each cell was calculated with the model based on the digital underlying surface data. According to the hydrological feature units (HFU) concept from the DFBMS, Taihu Basin was conceptualized into six different HFUs. The basic data of rainfall, evaporation, water surface elevation (WSE), discharge, tide level, and water resources for consumption and discharge in 2000 were used to calibrate the model. The simulated results of WSE and discharge matched the observed data well. The observed data of 1998, 1999, 2002, and 2003 were used to validate the model, with good agreement with the simulation results. Finally, the basic data from 2003 were used to simulate and evaluate the management scheme of water diversion from the Yangtze River to Taihu Lake. Overall, the DFBMS application in Taihu Basin showed good performance and proved that the proposed structure for DFBMS was effective and reliable. [ABSTRACT FROM AUTHOR]

In the flat lowland agricultural areas of subtropical climate zones, the runoff process has a great influence on the regional water quantity and quality. In this study, field data about rainfall, evapotranspiration, soil moisture, groundwater table, and surface water dynamics were collected in two different experimental sites in the Taihu Basin, China. Results showed that densely distributed ditches contributed to shallow groundwater depths and persistent near-saturation soil. A correlation analysis was conducted to improve the understandings of runoff generation in humid lowland areas of the Taihu Basin. It was found that a Dunne overland flow was the dominant mechanism responsible for the rapid runoff generation. The total rainfall and runoff expressed a good linear relationship with an R2 of 0.95 in the Hongqiwei test site. The initial groundwater depth was considered as the indicator of the antecedent soil moisture estimation for the close relationship. The depression storage was suggested in a range from 4.72 to 8.03 mm for an estimation based on the water balance analysis for each rainfall event, which proves that the depression storage should not be neglected when calculating the runoff generation process in humid lowlands. [ABSTRACT FROM AUTHOR]

We propose a flood risk management model for the Taihu Basin, China, that considers the spatial and temporal differences of flood risk caused by the different climatic phenomena. In terms of time, the probability distribution of climatic phenomenon occurrence time was used to divide the flood season into plum rain and the typhoon periods. In terms of space, the Taihu Basin was divided into different sub-regions by the Copula functions. Finally, we constructed a flood risk management model using the Copula-based Bayesian network to analyze the flood risk. The results showed the plum rain period occurs from June 24 to July 21 and the typhoon period from July 22 to September 22. Considering the joint distribution of sub-region precipitation and the water level of Taihu Lake, we divided the Taihu Basin into three sub-regions (P-I, P-II, and P-III) for risk analysis in the plum rain period. However, the Taihu Basin was used as a whole for flood risk analysis in the typhoon period. Risk analysis indicated a probability of 2.4%, and 0.8%, respectively, for future adverse drainage during the plum rain period and the typhoon period, the flood risk increases rapidly with the rising water level in the Taihu Lake. [ABSTRACT FROM AUTHOR]