Due to the existence of drainage networks, urban areas have formed their own hydrological mechanism. The pretreatment of complex and elaborate drainage network data has become a challenging step in building an urban hydrological model. This study proposes a network-combing method based on the potential outfall mechanism for an urban drainage system, analyzes the topological structure of the underground network, and generates a subcatchment based on the potential outfall (SBPO). Two hydrological methods are constructed for a typical region in Kunming, Yunnan Province, China. The results show that: The network-combing method of potential outfall mechanisms can well complete the sorting work of a drainage network system and can clarify the relative independent relationship. The SBPO method basically agrees with the SWMM constructed with a high-resolution network in terms of runoff volume, the peak value and the duration of the outflow process at the outfall. However, the subcatchment by the potential outfall mechanism can help to understand the service partition, and the calculation cost is greatly reduced. The method emphasizes the importance of the influence of a drainage system on water confluence, which can help to better understand the process of runoff in urban areas.

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

In recent years, the combined effects of rapid urbanization and climate change have led to frequent floods in urban areas. Rainstorm flood risk warning systems play a crucial role in urban flood prevention and mitigation. However, there has been limited research in China on nationwide urban flood risk warning systems based on rainfall predictions. This study constructs a two-level early warning system (EWS) at the national and urban levels using a two-dimensional hydrological–hydrodynamic model considering infiltration and urban drainage standards. A methodology for urban rainstorm flood risk warnings is proposed, leveraging short-term and high-resolution rainfall forecast data to provide flood risk warnings for 231 cities in central and eastern China. Taking Beijing as an example, a refined rainstorm flood warning technique targeting city, district, and street scales is developed. We validated the methodology with monitoring data from the “7.31” rainstorm event in 2023 in Beijing, demonstrating its applicability. It is expected that the findings of this study will serve as a valuable reference for the urban rainstorm flood risk warning system in China.

With the rapid development of urban expansion, as well as the frequent occurrence of extreme weather conditions such as extreme rainfall, flooding disasters at substations are becoming severe. Such flooding events can interrupt the power supply of residents and effect large areas. This paper presents a methodological framework for assessing the flood risk of substations based on the risk matrix coupled with a hydrodynamic model in the Dashi River Basin. Two factors of the risk matrix of substations are considered to determine the flooding risk level. One factor is the possibility of flood risk, and the other factor is the severity of the consequences of substation inundation. The hydrodynamic model is used to quantify the possibility of the flood risk, which is validated using the inundation process during the 23.7 catastrophic flood event. Additionally, the analytic hierarchy process (AHP) is applied to analyze the severity of the inundation consequences. Moreover, we identify four flood risk levels: general, high, higher, and particularly significant. For demonstration, the proposed framework is applied to 12 substations in the Dashi River Basin. The findings indicate that more than half of the substations in the Dashi River Basin are at risk of flooding, and the risk levels of substations D9 and D12 are particularly significant, indicating a very high risk. These results emphasize the need to improve the flood risk management ability for the power grid so as to enable the application of accurate and effective measures to reduce the substation flood disaster risk. Moreover, the framework developed in this study has good universality and can provide a systematic solution for flood risk analysis of similar substations.