Simple and accurate estimates of stage–discharge characteristics are extremely important for supporting appropriate management of two- or multi-stage compound channels, which commonly form in natural rivers or are constructed in regulation projects for urban rivers. Although various methods are available for conveyance estimation of two-stage compound channels, very few are concerned with the multi-stage case. After a critical review of predictive methods for two-stage channels, this paper presents a general model for predicting the stage–discharge relationship in multi-stage compound channels. A multi-stage compound section was divided laterally into several homogeneous subsections, which were further classified into seven subdivision elements. In each element, we employed a general expression to analyse momentum balance, which accounts for momentum exchange between adjacent subsections. Then, a linear system with a tridiagonal coefficient matrix was used to estimate discharge in multi-stage compound channels. Experimental data from the literature were used to calibrate model parameters and examine the performance of the proposed model. The comparative results indicate that the proposed model produces satisfactory predictions. Finally, the main model parameters were discussed.

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research.

Soil water content (SWC) plays a vital role in the process of evapotranspiration (ET) in rubber plantations. To investigate the influence of the ET process on soil water balance in rubber plantations, we measured SWC at depths of 10, 20, 30, 40, 70, 100, 130 cm, measured the characteristics of root density distribution, and estimated the ET and deep percolation at a rubber plantation in Xishuangbanna using the Hydrus-1D model. Our results indicate the Hydrus-1D model can accurate simulate soil water dynamics in the 0–130 cm of rubber plantations with Nash-Sutcliffe Efficiency Coefficient (NSE) was 0.80–0.96, the Root Mean Squared Error (RMSE) was 0.05–0.02, and the Pearson Correlation Coefficient (R) was 0.82–0.97. Obviously, there were seasonal variation characteristics in soil moisture changes in the rubber plantations of Xishuangbanna. The soil water storage (SWS) dropped to its lowest value of 194 mm and reached its maximum value of 504 mm from the dry season to the rainy season. The simulated ET of the rubber plantation was 1166.1 mm. The large uptake and utilization of soil water by rubber plantations in the dry season affects or exacerbates seasonal drought in Xishuangbanna and leads to a shortage of regional water resources.

The emergency control of Menglou~Qifang inverted siphon, which is about 72 km long, is the key to the safety of the Northern Hubei Water Transfer Project. Given the complicated layout of this project, traditional emergency control method has been challenged with the fast hydraulic transient characteristics of pressurized flow. This paper describes the application of model predictive control (MPC), a popular automatic control algorithm advanced in explicitly accounting for various constraints and optimizing control operation, in emergency condition. For the fast prediction to the pipe-canal combination system, a linear model for large-scale inverted siphon proposed by the latest research and the integrator-delay (ID) model for open canals are used. Simulation results show that the proposed MPC algorithm has promising performance on guaranteeing the safety of the project when there are sudden flow obstruction incidents of varying degrees downstream. Compared with control groups, the peak pressure can be reduced by 17.2 m by MPC under the most critical scenario, albeit with more complicated gates operations and more water release (up to 9.75 × 10⁴ m³). Based on the linear model for long inverted siphon, this work highlights the applicability of MPC in the emergency control of large-scale pipe-canal combination system.