Reservoir water level monitoring and early warning are critical components of flood control and disaster mitigation in river basins, as they can effectively prevent dam failures or downstream flooding. However, existing reservoir monitoring systems generally suffer from response delays and ineffective early warnings when dealing with sudden anomalies in water level readings (such as those caused by extreme hydrological conditions, instrument malfunctions, or localized damage). This study proposes a method for the automatic identification and early warning of abnormal water level changes that integrates the quantile regression model and the isolated forest algorithm (QR-IForest framework). Taking two series-connected reservoirs in the Yonghan River Basin in Guangdong Province as the study subjects, the method performs anomaly detection on real-time water levels based on data from actual monitoring stations in the reservoir area. The results indicate that: (1) The fused method achieves higher accuracy in anomaly detection than either method alone, with a precision rate of 92.24%, a recall rate of 0.8992, and an F1-score of 0.9106. The true positive rate (TPR) for anomaly detection reaches 99.15%, while the true negative rate (TNR) reaches 89.92%. (2) Simulation tests were conducted for two different scenarios (heavy rainfall and river channel blockage). The results show that the model’s anomaly detection capability can provide rapid, early warnings, detecting anomalies as early as 2 hours before an incident occurs. (3) This integrated model can automatically classify detected anomalies. When identifying anomalies related to monitoring equipment or those caused by deviations in hydrological patterns, the detection accuracy exceeds 85%. The method developed in this study provides a reliable basis for efficiently identifying anomalies in water level monitoring data from small and medium-sized reservoirs, thereby ensuring the safe management and operation of these reservoirs.

In recent years, there have been frequent extreme weather events that defy traditional understanding. Specifically, mountain flood disasters can cause significant loss of life due to their sudden onset and destructive power. The 7.21 flood event in Xingyang, Zhengzhou, China, recorded a maximum 6 h precipitation of 240.5 mm in the Suo River basin, corresponding to a 500-year return period, and causing fatalities and substantial damage. The central government of China has launched supplementary mountain flood disaster surveys and evaluations involving key towns and villages, following an initial round of surveys in riverside villages, to improve foresight and response capabilities for mountain flood disaster risks under extreme conditions. This paper introduces the contents of the national mountain flood disaster surveys and evaluations of key towns and villages, elaborating on the principles, content, and rules for auditing the national survey and evaluation results. This paper innovatively proposes professional audit criteria, such as early warning indicators, monitoring facility correlations, and hazard zoning, based on a formal audit of the data quality. The implementation of professional audit criteria improved the data accuracy by 85% and reduced false alarms by 40%, enhancing the overall effectiveness of mountain flood disaster prevention. The analysis of the audit results suggests that the audit rules for the survey and evaluation results of key towns are scientific, reasonable, and effective, achieving the expected goals of data quality control. This approach can effectively enhance the practical value of the survey and evaluation outcomes for key towns, laying a solid data foundation for transforming flood disaster prevention from merely “existing” to “optimal”.

The losses caused by flood emerges from the intricate interplay between natural and human systems. Particularly noteworthy is the flood disaster induced by exceptionally intense precipitation within densely populated mega-citys. A poignant illustration of this dynamic unfolded on July 20th, 2021, in the city of Zhengzhou, China. This event precipitated 380 fatalities, accompanied by a direct economic loss exceeding 5.6 billion U.S. dollars, thereby exerting profound repercussions on national economic and social development. To unravel the intricate interactions between human systems and the extreme natural setting, our investigation delves into specific disaster events, such as fatalities occurring in metro line 5 and the tunnel of Jing-guang urban expressway, the overtopping peril of Guojiazui reservoir, and the explosion at an aluminum alloy factory. Employing a systemic perspective, we analyze human activities during the phases of early warning, response and disposal. Our findings underscore pivotal factors contributing to the substantial loss of life, including inefficient organization and preparedness by local government entities, inadequate emergency response measures from various departments, and a lack of readiness among the local populace. In response to these identified shortcomings, we proffer concrete recommendations for disaster prevention. These suggestions serve as valuable references for mega-cities, advocating measures such as fortifying the linkage mechanism among governmental departments and enhancing public awareness regarding flood hazards.

The Haihe River Basin has experienced frequent floods from extreme rainfall in recent decades, with a catastrophic event striking the Beijing-Tianjin-Hebei region in July 2023. From July 29 to August 1, Liucun Town recorded 473.6 mm of rainfall, while the Wangjiayuan Reservoir station observed 744.8 mm, the highest in Beijing. This study examines the impacts of this flood in Shisanling Town, upstream of the Shisanling Reservoir, using a new-generation distributed hydrological model with calibrated parameters. Based on the “7·29” rainfall event, we simulated flood processes and assessed impacts on population and infrastructure, as well as reservoir discharge scenarios. Results indicate that under the “7·29” rainfall, Shisanling Town’s inundated area could reach 6.2 km², affecting 38 villages (excluding Yuling), with Beixin Village the most severely impacted (1.6 km² submerged). Approximately 2,935 people, 458 houses, 239.5 ha of orchards, and 105.4 ha of farmland would be affected. The flood peak, with an inflow of 1,627 m³/s and river depth of 7.2 m, would reach the reservoir within 48 h. Once the water level exceeds 93 m, a discharge of 80.05 million m³ is required. At the observed release rate of 30 m³/s, drainage would take 31 days. A full discharge downstream would inundate ~218 km² across nine towns, affecting ~650,000 people, 76,000 houses, 100+ road segments, 23,300 ha of farmland, 10 parks, and 16 schools. This study provides critical insights for flood prevention, reservoir management, and emergency evacuation planning in small watersheds under extreme rainfall conditions.