Water project and watershed management is currently being reengineered under digital twin schemes in China through pilot projects. An evaluation on pilot reengineering is important for its further implementation and improvement. This paper investigates national legislation and pilot projects’ implementations of a digital twin watershed and digital twin water project from the perspectives of design, policy, technology, investment, personnel, cyberspace security, co-construction, and sharing through interviews and expert review, and it uses a Bayesian network to study their reliability. First, the design of the digital twin watershed and digital twin water project is reasonable with regard to system architecture and business management. Second, although there are some national legislations on cyberspace security and geospatial data, they are incomplete for policy making and are probably infeasible for some technology. Third, there are insufficient mechanisms to sustainably support investment, personnel, and cyberspace security. Forth, co-construction and sharing are required for both inside and outside water departments. Fifth, the Bayesian network is useful for investigating the reliability of weak nodes, and it is helpful for the design and further implementation of the digital twin watershed and digital twin water project, as will be demonstrated with an anonymous example. This study could provide useful insights into the further reengineering of water projects and watershed management under a digital twin scheme in the world.

Recently, frequent water shortages and reductions in water flow have been observed in the northern segment of the Yellow River within the Beijing–Hangzhou Grand Canal. In response, a water replenishment program has been initiated. This study is focused on the section of the Grand Canal north of the Yellow River and uses the GSFLOW model to examine interaction between surface water and groundwater, as well as the effect of water replenishment. The results indicate that, after the water replenishment, the efficiency of water replenishment was highest in the Xiao Canal (64.30%), followed by the Wei Canal (39.09%), the South Canal (12.11%), and the North Canal, which exhibited the lowest efficiency (5.75%). This variation can be attributed to greater water loss with increasing distance from the replenishment source, leading to lower replenishment efficiency. Surface water recharge to groundwater was extended by 32 days, with replenishment effects persisting even after the water supply ceased. The maximum influence distance on either side of the canal reached 5.73 km, with an average impact distance of 1.48 km, resulting in a total affected area of 974.7 km², accounting for 2.2% of the study area. Water replenishment positively influenced the recovery of groundwater levels along the Grand Canal.

Concrete crack opening (CCO) is of great importance to hydraulic engineering maintenance. A forecast method is put forward combining back propagation neural network (BPNN) and differential equation (DE) for daily CCO modeling and was applied to Wangqingtuo Reservoir, in the northern semiarid region of China and the contribution of the DE was assessed by using BPNN model as a contrast. First, it is made up of BPNN and DE calibrations: (1) use historical data to calibrate BPNN models and obtain residuals; (2) use the particle swarm optimization to calibrate coefficients of the DE. The periodicity and time delay of air temperature is expressed by the DE well. Second, important results were found by field application: (1) the sole BPNN models can provide reasonable predictions; (2) better prediction can be achieved based on BPNN-DE-2TD by increasing KGE, 12% for JB-1, 37% for JB-3, and 6% for JB-7; (3) it is indicated that the addition of DE can improve the modeling on the role of air temperature under seasonal and linear trend, while BPNN part can express the nonlinear role of water level and precipitation well, confirmed by Fourier amplitude sensitivity test sensitivity and Shapley Additive exPlanations analysis. This study could provide useful insights into further forecasting of CCO under this forecast method in the world.

Reliable daily seepage pressure forecasting remains challenging for earth-rock dam safe ty monitoring. This study aimed to develop a hybrid framework coupling back-propagation neural network (BPNN) and differential equation (DE) for daily seepage pressure prediction. The framework was validated using daily monitoring records (January 2020–July 2024) from three piezometers at the Wangqingtuo regulating reservoir, Tianjin. Input variables included reservoir water level, squared water level, precipitation, and maximum and minimum air temperature. The dataset was partitioned into training (2020–2022), internal testing (2022–2023), and external forecasting (2023–2024) subsets. The two-stage method first trains a BPNN to produce baseline predictions and extract residuals; then particle swarm optimization (PSO) calibrates unknown coefficients of the residual-based DE. Among DE variants, BPNN–DE-1TD achieves the most stable performance across monitoring points. Compared with standalone BPNN, this model reduces mean absolute percentage error (MAPE) by 17%, 19%, and 26% at P5-1, P4-2, and P6-4, yielding absolute MAPE of 1.56%, 1.17%, and 5.41%, respectively. The hybrid framework effectively captures seasonal oscillations and time-lag responses of seepage pressure. This work demonstrates the value of residual-corrected semi-physical modelling for controlled shallow regulating reservoirs, and the method may be adapted for comparable dam seepage forecasting cases with similar hydraulic characteristics.

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with <20% absolute error in training and testing stages, the PSO can achieve similar performance in the forecasting stage; (2) the heuristic can achieve better prediction in years with a lower sunspot number; (3) besides the influence of the sun activity, atmospheric circulation, water usage, and water-project regulation do play important roles on the measured or natural runoff to some extent. This study could provide useful insights into further forecasting of measured and natural runoff under this forecast heuristic in the world.