Enhancing flood resilience has become crucial for watershed flood prevention. However, current methods for quantifying resilience often exhibit coarse spatiotemporal granularity, leading to insufficient precision in watershed resilience assessments and hindering the accurate implementation of resilience enhancement measures. This study proposes a watershed flood resilience assessment method based on a system performance curve that considers thresholds of inundation depth and duration. A nested one- and two-dimensional coupled hydrodynamic model, spanning two spatial scales, was utilized to simulate flood processes in plain river network areas with detailed and complex hydraulic connections. The proposed framework was applied to the Hangjiahu area (Taihu Basin, China). The results indicated that the overall trend of resilience curves across different underlying surfaces initially decreased and then increase, with a significant decline observed within 20–50 h. The resilience of paddy fields and forests was the highest, while that of drylands and grasslands was the lowest, but the former had less recovery ability than the latter. The resilience of urban systems sharply declined within the first 40 h and showed no signs of recovery, with the curve remaining at a low level. In some regions, the flood tolerance depth and duration for all land use types exceeded the upper threshold. The resilience of the western part of the Hangjiahu area was higher than that of other regions, whereas the resilience of the southern region was lower compared to the northern region. The terrain and tolerance thresholds of inundation depth were the main factors affecting watershed flood resilience. The findings of this study provide a basis for a deeper understanding of the spatiotemporal evolution patterns of flood resilience and for precisely guiding the implementation and management of flood resilience enhancement projects in the watershed.

There exist conflicts of interest between upstream and downstream regions in flood control and drainage; how to balance these conflicts and achieve collaborative flood management remains an important scientific problem. To explore a balanced governance strategy, this study took the Demonstration Zone of Green and Integrated Ecological Development of the Yangtze River Delta, which consists of three separate administrative regions, as the research domain. Using evolutionary game theory, the study conducts a comparative analysis of the interests between upstream and downstream areas. It introduces external drivers, such as the intervention of higher-level administrative bodies and incentive-constraining policies, along with internal balancing mechanisms like bidirectional compensation. The goal is to explore collaborative strategies and cooperation mechanisms that can balance the conflicts of interest between upstream and downstream areas. Results indicate that: (1) The final collaborative strategy was closely related to factors such as the cost of conflict, the amount of two-way compensation, additional benefits of flood control and drainage, and the intensity of incentive constraints. (2) Incorporating a reasonable two-way compensation and reward and punishment mechanism into the evolutionary game theory model can promote the model to a stable strategy. (3) The external driving mechanisms aim to coordinate the conflicts between upstream and downstream regions through incentive or constraint policies, which help motivate and encourage proactive collaboration in flood control and drainage management. The internal balancing mechanism is responsible for compensating for economic losses caused by imbalances, thereby creating pressure that fosters regional cooperation in flood control and drainage governance. In a word, the collaborated management mechanism helps provide a more balanced strategy across different administrative regions.

The analysis of temporal and spatial variability in risk has garnered significant research attention, particularly regarding flash flood disasters in the context of warming and wetting conditions on the Qinghai–Tibetan Plateau. Focusing on the Lhasa River basin, this study develops a framework that integrates geographic information systems and a combined subjective–objective weighting approach to comprehensively assess flash flood risk despite limited observations. This paper investigates the distribution patterns of hazard, vulnerability, and the integrated risk of pluvial flash floods; demonstrates the reliability of the assessment results; and provides mitigation recommendations for disaster risk management at the county level. The results showed a trend in increasing flash flood risk in recent decades compared to the 1990s. Moreover, very-high- and high-risk areas were concentrated in downstream regions with frequent precipitation extremes and anthropogenic activity. From 1991 to 2020, the high to very high-risk areas gradually expanded from central Lhasa to neighbouring counties. This study contributes valuable insights into flash flood risk assessment cand mapping, which are crucial in terms of the protection of life and property in the plateau basin.

The forecasting of future stormwater resources serves a pivotal role in gauging the potential effects of stormwater utilization on natural and societal systems. However, there has been limited research on the effect of climate change on future rainwater resources, particularly for large-scale water diversion projects. Based on global climate model data, future land use data and SCS-CN (soil conservation service curve number) model, this study proposed a calculation method of rainwater resources under different SSP scenarios in the future period, and carried out a case study on the cities in the receiving area of South-to-North Water Diversion Project (SNWDP). The results show that there will be an increase in the amount of rainfall in the future compared to 2020.The most significant increase in rainwater resources occurs in urban reception areas of the Eastern Route Project (ERP). However, according to future projections, the quantity of rainwater resources in the study area decreases from south to north. The spatial distribution of rainwater resources demonstrates a nearly normal distribution in the eastern and western routes and a bimodal distribution in the Central Route Project (CRP). In the context of global climate change, the increase in rainfall resources in the CRP may be beneficial for the establishment and implementation of the SNWDP. This study can provide a reference for analysing the uncertainty of rainwater resources in the future period under climate change. It can also provide guidance for the construction and develop scheduling schemes of the SNWDP, a world-class water diversion project.

Adjacent mountain area and plain area generally belong to the same climate zone, and a close hydraulic connection is also found between them. However, the flood controls in these two areas belong to different management departments. Flood occurring in these two areas can bring serious waterlog disasters, posing great threat to human lives and property. Previous studies mainly focused on the individual flood event in mountain or plain areas, while the research on the floods simultaneously occurring in both areas is insufficient. Hence, this study establishes a copula function-based assessment method to systematically analyze the characteristics of simultaneously-occurring floods in mountain and plain areas in Hangzhou City, thereby providing a basis for the collaborative management of floods in both areas. Results show that the proposed approach can give water level combinations in mountain and plain areas that are prone to co-occurring floods. Moreover, the water level in plain area is most likely to simultaneously appear with the water level in mountain areas under similar return periods, indicating that floods of equivalent level are likely to occur in plain areas when severe floods occur in mountain areas. This study provides a better understanding on the interplay of floods simultaneously occurring in mountain and plain areas, offering insights for coordinating the flood protection system in these two areas.

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period.