Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

Due to the impact of climate change and rapid urbanization, issues around global urban flood control and water environment security have emerged as major global concerns. As a practical way to address these issues, exploitation of urban rainwater resources has become a worldwide hotspot for research and application. This paper 1) briefly examined the evolution of rainwater utilization management modes in advanced countries, 2) classified urban rainwater utilization measures from the utilization stages into three categories—source control, medium transmission, and terminal treatment, 3) summarized the advantages, disadvantages, and scope of the application of these measures, 4) reviewed the benefits, drawbacks, and application areas of these measures, and 5) conducted a quantitative analysis of their impact on rainwater pollution and flood control.

To reveal the effects of water diversion from the Yangtze River to Lake Taihu on the phytoplankton habitat of the main water transfer channel of the Wangyu River, we investigated the water’s physicochemical parameters and phytoplankton communities during the water diversion and non-diversion periods over the winters between 2014–2016, respectively. During the water diversion periods in the winter of 2014 and 2015, the nutrients and organic pollutant contents of the Wangyu River channel were significantly lower than those during the non-diversion period in 2016. Moreover, the phytoplankton diversities and relative proportions of Bacillariophyta during the diversion periods evidently increased during the water diversion periods in winter. The increase in the water turbidity content, the decrease in the contents of the permanganate index, and the total phosphorus explained only 21.4% of the variations in the phytoplankton communities between the diversion and non-diversion periods in winter, which revealed significant contributions of the allochthonous species from the Yangtze River and tributaries of the Wangyu River to phytoplankton communities in the Wangyu River. The increasing gradient in the contents of nutrients and organic pollutants from the Yangtze River to Lake Taihu indicated the potential allochthonous pollutant inputs along with the Wangyu River. Further controlling the pollutants from the tributaries of the Wangyu River is critical in order to improve the phytoplankton habitats in river channels and Lake Taihu.