Flash floods are important natural disasters in China that can result in casualties and property losses. In this paper, we present a quantitative approach to examine the driving factors of the spatiotemporal distribution of flash floods based on a geographical detector. The environmental background condition (elevation, slope, etc.), precipitation, and human activity factors, as well as changes in these factors, are investigated in Sichuan Province via a driving force analysis. The results show that heavy precipitation is the most important driver, with power of determinant (PD) values of 0.71 and 0.77 for the spatial distributions of flash floods from 1995 to 2004 and from 2005 to 2014, respectively. The PDs of population density are 0.65 and 0.78 in the same two periods, while those of elevation are 0.59 and 0.73. Precipitation variability is the most important driver of the spatiotemporal variability of flash floods, followed by GDP density and population density, with PDs of 0.48, 0.29, and 0.27, respectively. The results show that human activities and precipitation are the primary driving forces of the spatiotemporal variability of flash floods and should be the focus of flash flood prevention and forecasting.

Flash floods are one of the most serious natural disasters, and have a significant impact on economic development. In this study, we employed the spatiotemporal analysis method to measure the spatial–temporal distribution of flash floods and examined the relationship between flash floods and driving factors in different subregions of landcover. Furthermore, we analyzed the response of flash floods on the economic development by sensitivity analysis. The results indicated that the number of flash floods occurring annually increased gradually from 1949 to 2015, and regions with a high quantity of flash floods were concentrated in Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Specifically, precipitation and elevation had a more significant effect on flash floods in the settlement than in other subregions, with a high r (Pearson’s correlation coefficient) value of 0.675, 0.674, 0.593, 0.519, and 0.395 for the 10 min precipitation in 20-year return period, elevation, 60 min precipitation in 20-year return period, 24 h precipitation in 20-year return period, and 6 h precipitation in 20-year return period, respectively. The sensitivity analysis showed that the Kunming had the highest sensitivity (S = 21.86) during 2000–2005. Based on the research results, we should focus on heavy precipitation events for flash flood prevention and forecasting in the short term; but human activities and ecosystem vulnerability should be controlled over the long term.

Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored catchments. The parameters for the ungauged regions are estimated by regionalization. River water stage data and reservoir water stage data from Shunchang, and reservoir water stage data from Zherong are used to validate the model. The model performs well and the average Nash–Sutcliffe efficiency (NSE) is above 0.8 for the five catchments. The validation shows the difference in the timing of flood peaks using the two types of water stage data is less than 1 h. The rising and declining trends of the floods correspond to the observed trends over the entire validation process. Furthermore, the flash flood warning system was effectively applied in flash flood event on 28 September 2016 in Zherong. Thus, the CNFF-HM with regionalization is effective in forecasting flash floods for ungauged mountainous regions.

Flash flood hazards on the Qinghai–Tibet Plateau are surging under rapid warming and humidification, driving glacier retreat, lake outbursts, and extreme precipitation that imperil water security across Asia. To address the limitations of studies relying primarily on historical observations and lacking quantitative insights into disaster mechanisms and dynamic prediction, this study integrates a logistic regression model with the geographical detector approach. Using multi-source flash flood records from 1950 to 2023 and 12 environmental variables (elevation, slope, precipitation, river network density, land use, etc.), the analysis quantifies the interactive effects of key drivers and uncovers the nonlinear mechanisms governing flash flood sensitivity. The results indicate that: (1) the model demonstrates strong predictive capability, achieving 78% accuracy and an AUC of 0.87; (2) mean annual precipitation is the dominant factor, while its interaction with river proximity enhances the explanatory power of flash flood disasters by 37%, indicating a nonlinear reinforcing effect; and (3) high-resolution sensitivity mapping for 2023 reveals that areas of high and very high flash flood sensitivity are concentrated in the South Tibet Valley and Hengduan Mountains, aligning with regions of glacial lake expansion and frequent extreme precipitation. In contrast, medium- and low-sensitivity areas are widely distributed across the North Tibet Plateau, where arid geomorphology and sparse river networks exert dominant control. This spatial pattern corresponds closely with regional topographic, climatic, and hydrological processes. The study offers a transferable approach for dynamic flash flood risk early warning, precise disaster zoning, and improved resilience of transboundary basins on the Qinghai–Tibet Plateau.

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”.