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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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As the most direct indicator of drought, the dynamic assessment and prediction of actual evapotranspiration (AET) is crucial to regional water resources management. This research aims to develop a framework for the regional AET evaluation and prediction based on multiple machine learning methods and multi-source remote sensing data, which combines Boruta algorithm, Random Forest (RF), and Support Vector Regression (SVR) models, employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and CMIP6, covering meteorological, vegetation, and hydrological variables. To verify the framework, it is applied to grids of South America (SA) as a case. The results meticulously demonstrate the tendency of AET and identify the decisive role of T, P, and NDVI on AET in SA. Regarding the projection, RF has better performance in different input strategies in SA. According to the accuracy of RF and SVR on the pixel scale, the AET prediction dataset is generated by integrating the optimal results of the two models. By using multiple parameter inputs and two models to jointly obtain the optimal output, the results become more reasonable and accurate. The framework can systematically and comprehensively evaluate and forecast AET; although prediction products generated in SA cannot calibrate relevant parameters, it provides a quite valuable reference for regional drought warning and water allocating.

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Gated recurrent unit (GRU) has obtained attention as a potential model for streamflow forecasting in recent years. Common patterns and specialties when employing it in different regions, as well as a comparison between different models still need investigation. Therefore, we examined the performances of GRU for one, two, and three-day-ahead streamflow forecasting in seven basins in various geographic regions in China from the aspect of robustness, overall accuracy, and accuracy of streamflow peaks’ forecasting. The robustness and accuracy of it are closely related to correlations between the input and forecasting target series. Also, it outperforms the benchmark machine learning models in more cases, especially for one-day-ahead forecasting (NSE of 0.88–0.96 except for the unsatisfactory result in the Luanhe River basin). The deterioration of its accuracy along the increasing lead time depends on the dominant time lags between the rainfall and streamflow peaks. Recommendations were proposed for further applications.

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.