Coastal areas are vulnerable to floods caused by rainstorms and typhoons. It is necessary to ascertain the risk of floods caused by both of these extreme weather events. A conceptual risk model is proposed to evaluate the rainstorm risk, typhoon surge risk, and the compound risk in the coastal areas of Jiangsu Province during the period of 1960–2012. The results of the model show that the typhoon surge risk in the study region is greater than the rainstorm risk. Three Archimedean copulas were used to fit the joint probability distributions of the compound events. The Frank copula and the Gumbel copula proved to be the best-fitting joint distribution function for the Huaibei plain district and the Lixiahe district, respectively. The probability of the extreme compound events not happening is less than 90% in the study region. This means that the flood risk is mainly subject to the encounter of a low-level rainstorm and a low-level typhoon surge. The study shows that the northern region of Jiangsu Province is more vulnerable to the compound risk, and that we should pay more attention to the floods caused by the compound events of rainstorm and typhoon surge.

Water resources are very important to support the socio-economic development and maintain environmental health, which is a typical issue in water resources management. In this study, we developed an optimal allocation model for a large complex system of water resources by considering both water supply and river ecological benefits. The water supply benefit is defined as the minimum water deficit for different water users, while the ecological benefit involves making the reservoir release as close as possible to the natural streamflow. To solve this problem, the combination of decomposition-coordination (DC) and discrete differential dynamic programming (DDDP) methods were proposed. The proposed methods first decomposed a large system with multi-objective programming into subsystems, and the optimal solution of each subsystem was accomplished by the DDDP method to solve the system efficiently. Then the subsystems’ solutions were coordinated to figure out the near global optimal solution. The proposed models were tested in the Lingui and Yongfu County, Guilin City in China. Results show that the optimal reservoir release is close to the natural flow regime and there is a slight water deficit ratio in both level years. The water supply objective is more sensitive to the system model compared with the ecological objective, and the result of water allocation is optimized when the reservoir release is as close as possible to the natural flow based on the minimum water deficit. The proposed system model could facilitate sustainable water use and provide technical support for water resources management in economic development.

In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The BPNN model is most sensitive to the data characteristics consistency.

Understanding the dynamic behavior of Lightweight Aggregate Self‐Compacting Concrete (LWASCC) is of importance to the safety of concrete structures serving in dynamic loading conditions. In this study, the fundamental dynamic properties of LWASCC with three types of LWA were investigated by the impact resonance method. Results show that the dynamic elastic and shear modulus generally decrease with the increase of LWA volume fraction, whereas three types of LWA exert limited influence on dynamic Poisson’s ratio. The dynamic elastic and shear modulus show good linear dependence upon compressive strength. The inclusion of three types of LWA significantly increases the damping ratio, indicating significantly enhanced damping capacity of LWASCC under dynamic loading conditions. The damping ratio of LWASCC is improved by 2.0%, 4.4%, and 2.9% when adding 1% (by volume) expanded clay, rubber, and expanded polystyrene, respectively. The compressive strength and dynamic performances of LWASCC are highly influenced by the intrinsic properties (elastic modulus, damping capacity, wettability, etc.) and geometrical characteristics (size, surface roughness, etc.) of LWA, as well as the LWA‐matrix bonding capacity.

The water process refers to the movement and changes in water on Earth, encompassing changes among its three states and its spatial movement. This process is vital for human society as it directly influences water resources, environmental sustainability, and climate regulation. Previous studies have used various related factors to analyze the water process but have not explained the rationale behind selecting these factors from the perspective of pathways. Based on this, the paper explores the construction and application of a top-down water process knowledge graph to clarify the changing process of water movement and the sources of influencing factors. Firstly, we define the concept of the water process and classify its entities based on the concept of water boundaries. Secondly, we identify key knowledge components of the water process, including water bodies, processes, and influencing factors. Finally, we construct and analyze a knowledge graph of the water process and its influencing factors. Results show that (1) the paths of water process help us understand the movement and change process of the water bodies; (2) the number of paths increases with the length of the connection between entities, reflecting the complexity of water process relationships; and (3) tracing these pathways can help identify their influencing factors, providing a data foundation for applying deep learning algorithms in water process research.