1. Introduction
Flash floods are surface runoff events in mountainous watersheds caused by short-duration heavy rainfall. These events are characterized by their suddenness, destructiveness, and rapid rise and fall in surface runoff, often triggering disasters such as landslides and debris flows. In recent years, the frequency of extreme weather events worldwide has increased significantly [
1]. Coupled with rapid population growth and extensive urbanization, the interaction of factors such as precipitation, underlying surface conditions, and human activities has contributed to floods, which are among the high-frequency natural disasters. Both the affected populations and economic losses caused by floods rank among the highest globally. For instance, in April 2022, South Africa experienced floods and landslides caused by heavy rainfall, resulting in over 500 fatalities. On 8 August 2023, severe flooding in Beijing led to the death or disappearance of more than 50 people. In late October 2023, flooding in Kenya claimed the lives of 154 people [
2]. To address flash flood disasters, international organizations such as the World Meteorological Organization (WMO), the Global Water Partnership (GWP), the International Association of Hydrological Sciences (IAHS), the International Association for Hydro-Environment Engineering and Research (IAHR), and the National Oceanic and Atmospheric Administration (NOAA) have increased their attention and research efforts on flash flood disasters. China has established a comprehensive defense system against flash flood disasters, combining professional prevention with community-based monitoring and defense [
3]. This system has achieved significant success in practical defense. However, due to the complexity of flash flood disasters, involving various hydrological processes and nonlinearities, unclear disaster mechanisms, numerous influencing factors, and limited available data, the key technology research on flash flood risk assessment is still in its early stages and technological exploration. Taking into account the lack of a universally recognized scientific method for flash flood risk assessment in existing domestic and international research, this study focuses on the driving factors of flash flood disasters in China and conducts a risk assessment to understand the regional differences in the flash flood occurrence. Taking into account the lack of universally recognized scientific methods for assessing flash flood risks in existing domestic and international research, this study focuses on investigating the driving factors of flash flood disasters in China. It conducts a risk assessment to understand regional differences in flash flood occurrence. The goal is to provide theoretical references and insights from an adaptive perspective for flash flood prevention in China.
In recent years, domestic and international research on flash flood disasters has primarily focused on three aspects: First is research on flash flood warnings based on hydrological models [
4,
5]. Ngoc et al. [
6] proposed combining the particle swarm optimization algorithm with deep learning to enhance the performance of segmenting flash floods from satellite images, thereby optimizing flood warning systems. Surwase et al. [
7] employed multiple segmentation and Otsu’s threshold segmentation techniques to validate flood footprints, thereby improving warning accuracy. Liu et al. [
8], after analyzing the impact of sediment on disaster risk, integrated watershed sediment yield models and monitoring technologies, selecting different warning indicators and thresholds for runoff and sediment risks. The second aspect is the analysis of disaster distribution characteristics and causes [
9,
10,
11]. Zhang et al. [
12], based on the results of historical investigations into flash flood disasters, analyzed the spatial variation patterns of flash floods in Chongqing. They utilized a geodetector to categorize the changes in flash flood disasters into three stages. Chen et al. [
13] discovered that the mid-lower reaches of low hills and plains are more prone to flash flood disasters than the upstream mountainous areas. The third aspect primarily involves flash flood risk assessment and risk zoning [
14,
15,
16]. This is mainly based on factors such as precipitation, elevation, slope, etc., with quantification of their contributions to flash flood risk. Sanyal et al. [
17] proposed a comprehensive multi-factor index using GIS to create flood risk maps. Bhatt et al. [
18] utilized satellite data, coupled with ArcGIS 10.8 technology, to systematically identify high-risk flash flood areas. Moiaddadi [
19] introduced a flood risk probability index, validated its effectiveness along highways, and obtained a flood risk map based on vulnerability weights. Tang et al. [
20] conducted an analysis and overlay evaluation of flash flood impact factors, resulting in a flash flood risk zoning map. Huang et al. [
21], by constructing a flash flood risk assessment model, identified high-risk aggregation areas in the study area. Yang et al. [
22] found that the coefficient of variation analysis indicates that with the occurrence of landslides, the frequency distribution of slope becomes more dispersed, while aspect and TWI become more concentrated. Zhou et al. [
23] proposed a method that combines transient rainfall infiltration and the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model, along with the rapid mass movement simulation (RAMMS) model, to achieve hourly disaster prediction. Ma et al. [
24] verified that ecological vulnerability in mining areas is a key factor in exploring the development characteristics and destructive mechanisms of various surface disasters. Liu et al. [
25] conducted a detailed analysis of the precursors and causes of the recent Yahuokou landslide, and explored the current application status of time-series InSAR methods in landslide investigations. Wang et al. [
26] investigated the spatiotemporal deformation before and after the landslide damage in the Four Gates Village of the Yellow River in 2018. They verified that both spatial deformations before and after the damage followed a progressive failure pattern. Evidently, the current focus of research is on integrating the impact factors of flash floods and different analysis methods for flash flood risk assessment.
The geographic information system (GIS) is widely applied in flash flood disaster risk assessment. Muhammad et al. [
27] used geographic spatial models and the analytic hierarchy process to assess flash flood susceptibility and delineate flash flood risk zones. Hafedh [
28], employing the GIS and hydrological models, simulated flood processes, revealing low to extremely high risks for floods with recurrence periods of 5, 50, and 100 years. Hewaidy [
29] utilized the GIS to investigate topographical parameters, identifying high-risk zones in the study area. In China, Fang et al. [
30] and Lin et al. [
31] both used the GIS to study flash flood disaster risks, exploring and analyzing regions prone to frequent flash floods with severe losses. Geodetector models are mainly used to detect the importance of influencing factors and their interaction patterns, and are widely applied in economic [
32], population [
33], and agricultural [
34] research. This model is gradually being utilized to explore the driving factors of flash flood disasters. Huang et al. [
35] quantitatively analyzed the impact of various triggering factors on flash flood disasters and, using a geodetector, verified that rainfall is the direct trigger and conditioning factor for flash floods, while terrain and landforms provide the material basis and potential conditions. Li et al. [
36] used a geodetector to explore the probability of flash flood disasters, finding that the maximum 6 h and 24 h rainfall in a 100-year event had the greatest impact. Yu et al. [
37] revealed the driving factors of flash flood disasters at different scales and conducted a flash flood risk assessment based on a comprehensive weighting method. He et al. [
38] summarized the current status of flash flood defense construction in China, investigating the characteristics of flash floods induced by heavy rainfall under the new defense situation. Liu [
39] revealed the spatiotemporal evolution pattern of flash flood disasters in China since its founding, detecting the driving factors influencing the spatial distribution of historical flash flood disasters. Chen et al. [
40] used a hybrid clustering method with neural networks to formulate a flash flood zoning plan for China and evaluated it using a geodetector. Bin et al. [
41] studied flash flood disaster driving factors using methods such as the Mann–Kendall test and a geodetector, discovering that elevation and land use were the most critical factors, showing an upward trend over time. Clearly, flood risk assessment methods have experienced rapid development with their dependence on modern information technology.
Research on flash flood disaster risk assessment primarily focuses on exploring the probability of flash floods at the provincial or watershed scale. The weights of flash flood factors often use the analytic hierarchy process, but due to the subjective nature of this method, it significantly affects the accuracy of flash flood risk probability. Using a geodetector to calculate indicator weights can effectively address this issue. Meanwhile, the flash flood potential index (FFPI) is an established method for the operational application of flash flood risk assessment. Therefore, based on flash flood events in China from 2017 to 2021, this study first identified the influencing factors triggering flash floods. Subsequently, a geodetector was employed to explore the relationships between various factors and flash flood disasters, obtaining the weights of each influencing factor. Building upon this, this study introduced the flash flood potential index (FFPI) and constructed a comprehensive risk assessment framework for flash floods. The aim was to provide a certain reference for research on the prevention of flash floods.
5. Conclusions
Under the influence of extreme climates, flash floods occur frequently, resulting in significant economic losses and casualties. This study quantifies and identifies key influencing factors, conducting an in-depth investigation into the probability distribution of flash flood risks. The main conclusions are as follows:
(1) After examining the contributions of various factors triggering flash floods, among them, precipitation is a fundamental factor with the greatest impact on flash floods. Subsurface factors such as soil and slope serve as material conditions and potential criteria for triggering flash floods. Human activities also exacerbate the occurrence of flash floods. The mutual explanatory power between these two groups of factors surpasses the explanatory power of individual factors.
(2) After obtaining the probability distribution of flash flood risks in China, the southern and southwestern regions of China were identified as high-risk areas, while the risk levels in the northwest and northeast regions were relatively low. This is consistent with the observed distribution of flash flood disasters from 2017 to 2021.
This study mainly uses the explanatory power of the geographical detectors to calculate factor weights; most current studies obtain weights through expert scoring or the analytic hierarchy process, which are greatly affected by human factors. Therefore, this method avoids the influence of human subjective judgment. However, since the input data cover a limited time range, there are also potential errors in predicting actual situation.
Considering the uncertainties and vulnerabilities associated with flash flood disasters, future research will refine the factors influencing these disasters, focusing on issues such as improving the accuracy of input data. The goal is to provide valuable references for the risk management of flash floods in China.