Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R²) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.

The water ecological environment problems brought about by rapid urbanization have prompted the proposal and implementation of different approaches to urban water ecological construction, such as eco-cities, best management practices (BMPs), and low-impact development (LID). As one of the most representative urban water ecological management policies in China, the Water Ecological Civilization City (WECC) was proposed in 2013, and 105 cities were selected for pilot construction. Many studies have evaluated the effectiveness of WECC construction, but international quantitative comparison is lacking. To address this, an urban Water-Human-Health (WHH) Assessment Model, considering water resources, ecological environment, economic and social development level, and water resources utilization, was developed and applied to five WECC pilot cities in China and 10 other cities worldwide, in which mainstream urban water ecological construction modes have been used. Principal component analysis of the index values in the assessment system was used to evaluate the current status of water ecosystem health in the 15 cities, showing that Sydney, Cleveland, and Hamburg were the most advanced in urban water ecological management. The two cities with the best evaluation results (Sydney and Cleveland), and the WECC city with the highest score (Wuhan) were selected for documentary analysis of their water ecological construction documents to identify similarities and differences to inform best practice internationally for urban water ecological construction. The results showed that Sydney and Cleveland attach similar emphasis across most constituents of urban water ecological construction, while, for Wuhan, greater importance is attached to water resource management and water culture. The advantages and disadvantages of WECC construction and international experience are discussed. The WHH assessment model proposed in this study provides a new quantitative evaluation method for international urban water ecological health evaluation, which could be further improved by including an urban flood risk indicator.