Flash Flood Risk Research Articles

Flooding Hazard Vulnerability Assessment Using Remote Sensing Data and Geospatial Techniques: A Case Study from Mekkah Province, Saudi Arabia

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape.

Assessment of flash flood risks in the desert cities: a case study on Sabah Al-Ahmad, Kuwait

ABSTRACT Flash floods from rainstorms pose a severe natural hazard, especially dangerous in arid regions. This has occurred multiple times in Kuwait: in 1967, 1997, 2009, 2013, 2018, and 2020. For example, about 300 mm of rain beset the State of Kuwait on the 14th and 15th of November 2018. Subsequently, Sabah Al-Ahmad City was declared an environmental disaster zone, grappling with extensive water accumulation in its roads, urban zones, and surrounding valleys. This study focuses on (i) monitoring the hydro-geomorphological impacts of the floods on Sabah Al-Ahmad City and (ii) assessing the strategic plan to diminish flood risks, aiming to prevent a recurrence akin to the catastrophic events during rainstorms. (iii) proposing a protection plan for the city. The research employs various methodologies, including drones (UAV), field surveys, hydrological modelling, digital elevation model analysis, and spatial analysis of multi-spectral satellite imagery. These methods are instrumental in comprehending how flash floods impact the sustainable development of urban areas in Kuwait. Our approach is designed to support decision-makers and urban planners, enabling them to proactively identify flood-prone zones before future urban development initiatives in Kuwait.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980-2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985-2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025-2049, F2: 2050-2074, F3: 2075-2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

Global insights on flood risk mitigation in arid regions using geomorphological and geophysical modeling from a local case study

This research provides a comprehensive examination of flood risk mitigation in Saudi Arabia, with a focus on Wadi Al-Laith. It highlights the critical importance of addressing flood risks in arid regions, given their profound impact on communities, infrastructure, and the economy. Analysis of morphometric parameters ((drainage density (Dd), stream frequency (Fs), drainage intensity (Di), and infiltration number (If)) reveals a complex hydrological landscape, indicating elevated flood risk. due to low drainage density, low stream frequency, high bifurcation ratio, and low infiltration number. Effective mitigation strategies are imperative to protect both communities and infrastructure in Wadi Al-Laith. Geophysical investigations, using specialized software, improve the quality of the dataset by addressing irregularities in field data. A multi-layer geoelectric model, derived from vertical electrical sounding (VES) and time domain electromagnetic (TDEM) surveys, provides precise information about the geoelectric strata parameters such as electrical resistivity, layer thicknesses, and depths in the study area. This identifies a well-saturated sedimentary layer and a cracked rocky layer containing water content. The second region, proposed for a new dam, scores significantly higher at 56% in suitability compared to the first region’s 44%. The study advocates for the construction of a supporting dam in the second region with a height between 230 and 280 m and 800 m in length. This new dam can play a crucial role in mitigating flash flood risks, considering various design parameters. This research contributes to flood risk management in Saudi Arabia by offering innovative dam site selection approaches. It provides insights for policymakers, researchers, and practitioners involved in flood risk reduction, water resource management, and sustainable development in arid regions globally.

Rainfall projections under different climate scenarios over the Kaduna River Basin, Nigeria

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

How to avoid heavy life losses in Liulin-like flash flood events

Abstract. From 11 to 12 August 2021, an extreme rainstorm attacked the Liulin Town, a very common town in Hubei Province, China. The rainstorm triggered flash flood and caused heavy live losses. This study first conducted an analysis on the causes of the flash flood disaster, then, performed a simulation to review the flash flood process, and finally discussed the technical strategies and approaches to avoid life losses in similar cases. The main understandings include: (1) accumulative rainfall of shorter interval and time-sliding is significantly helpful to improve warning accuracy; (2) flash flood risk needs to be evaluated at catchment scale and making room for floods is necessary; (3) residential buildings threatened by flash floods need to rebuild some structures for live-saving in emergency circumstances; and (4) early warning system needs to have capabilities to collect real basin-wide rainfall and flood information, especially in case of rainstorm, and to be smart to judge hazard level.

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology.

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed.

Designing and Psychometric Evaluation of a Questionnaire for Assessing Society's Perception of Flash Flood Risk

Background: Public perception of risks associated with natural disasters like flash floods significantly influences disaster management effectiveness. Flash floods pose a major threat to life and property, and a lack of tools to evaluate societal risk perception creates a critical gap. Objectives: This study aimed to design and evaluate the psychometric properties of a questionnaire specifically assessing society's perception of flash flood risk. Methods: A two-stage approach was employed. The first stage involved collecting questionnaire items through a systematic literature review and a qualitative study. In the second stage, a comprehensive psychometric evaluation was conducted, including assessments of face validity, content validity, and construct validity. Reliability was established using Cronbach's alpha. Confirmatory factor analysis was further conducted with data collected from 136 community members who had experienced flash floods. Data analysis was performed using SPSS version 20 and AMOS software. Results: A combination of qualitative data and systematic review findings facilitated the development of 36 initial questionnaire items. Applying quantitative and qualitative construct validity measures led to the refinement of the instrument, resulting in 29 final items categorized into three domains: Risk perception (12 items), awareness (10 items), and preparedness (7 items). The internal consistency of the instrument was confirmed by a Cronbach's alpha coefficient of 0.88. Factor analysis further supported the good fit of the hypothesized model to the data. Conclusions: This study successfully developed and rigorously evaluated a questionnaire to assess society's perception of flash flood risk. This instrument offers valuable insights for informed decision-making and identification of factors influencing risk perception, ultimately contributing to improved disaster preparedness and management.

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

Concurrent and dynamical interdependency of compound precipitation and wind speed extremes over India

Compound precipitation and wind speed extremes have the potential to cause significant socio-economic losses. Therefore, this study aims to investigate the association of compound precipitation and wind speed extremes in observed period (1981–2020) and future periods (2021–2060 (F1) and 2061–2100 (F2)) over India. In this study, we have used IMD precipitation and ERA5 windspeed gridded (0.250 × 0.250) data for the observed period. For future analysis, we have used CMIP6-based EC-Earth3 GCM (SSP2–4.5 and SSP5–8.5) and CORDEX CMIP5 RCM (RCP-4.5 and RCP-8.5) climate models for precipitation and wind speed outputs. We have analyzed the characteristics of precipitation and wind speed extremes using three approaches: empirical method, tail dependence analysis, and event coincidence analysis. The empirical analysis revealed that the proportion of the area with >240 Compound Precipitation and Wind speed Extremes (CPWE) on the same day is 2.8% in observed period. However, it increased to 22.22% in future period F2, particularly in the SSP5–8.5 scenario of the CMIP6 case, and this increase is observed in regions like Am, eastern parts of Aw and Cwa, Bwk, and Dsb. The tail dependence analysis revealed that the area with tail dependence >0.5 constitutes approximately 0.72% in the observed period across the study area. However, under climate change scenarios, this percentage varies between 2.45% to 3.66% and notably, the increase is observed in regions such as Am, Bwk, and Dsb. The results of precursor coincidence rate (pcr) shows a high value of 3.52% in the entire study area for the observed period. The future analysis reveals an increase of areas in between 3.18% to 5.14% for different climate change scenarios. Consequently, the trigger coincidence rate (tcr) results also revealed the occurrence of high tcr in the range of 0.52% to 5.79% across the study area within the temporal window. However, the tcr range increases to 2.17% to 23.32% during the monsoon season during the observed period with respect to the days in the temporal window. The overall results showed that the high values of tail dependence, pcr and tcr are concentrated in the Am, coastal areas of Aw and Cwa, as well as in the Bwk and Dsb regions. This will lead to increase the extreme precipitation driven by extreme wind speed in those regions consequently, the risk of flash floods, landslides and natural hazards may rise in the future. Overall, this study provides valuable insights into the spatial distribution of compound precipitation and wind speed extremes across the study area.

Flash flood detection via copula-based intensity–duration–frequency curves: evidence from Jamaica

Abstract. Extreme rainfall events frequently cause hazardous floods in many parts of the world. With growing human exposure to floods, studying conditions that trigger floods is imperative. Flash floods, in particular, require well-defined models for the timely warning of the population at risk. Intensity–duration–frequency (IDF) curves are a common way to characterize rainfall and flood events. Here, the copula method is employed to model the dependence between the intensity and duration of rainfall events flexibly and separately from their respective marginal distribution. Information about the localization of 93 flash floods in Jamaica was gathered and linked to remote-sensing rainfall data, and additional data on location-specific yearly maximum rainfall events were constructed. The estimated normal copula has Weibull and generalized extreme value (GEV) marginals for duration and intensity, respectively. Due to the two samples, it is possible to pin down above which line in the intensity duration space a rainfall event likely triggers a flash flood. The parametric IDF curve with an associated return period of 216 years is determined as the optimal threshold for flash flood event classification. This methodology delivers a flexible approach to generating rainfall IDF curves that can directly be used to assess flash flood risk.

Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.

Improving Flash Flood Hydrodynamic Simulations by Integrating Leaf Litter and Interception Processes in Steep-Sloped Natural Watersheds

More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies.

Impact of Structural and Non-Structural Measures on the Risk of Flash Floods in Arid and Semi-Arid Regions: A Case Study of the Gash River, Kassala, Eastern Sudan

Sediment precipitation in riverbeds influences the effectiveness of structural and non-structural measures for flash flood mitigation and increases the potential for flooding. This study aimed to disclose the effectiveness of the implemented measures for flood risk mitigation in Kassala town, eastern Sudan. We employed remote sensing (RS) and GIS techniques to determine the change in the Gash River riverbed, the morphology, and the leveling of both the eastern and western sides of the river. Flood model simulation and a 3D path profile were generated using the digital elevation model (DEM) with a data resolution of 12.5 m from the ALOS BILSAR satellite. The main purpose of this study is to extract the layer of elevation of the riverbed on both the western and eastern banks and to determine the variations and their relationship to flood occurrence and mitigation. The construction of dikes and spurs near Kassala town has led to sediment precipitation, causing the riverbed to rise. The results show that it is now 1.5 m above the eastern Kassala town level, with a steep slope of 2 m/km, and the cross-section area at Kassala bridge has shrunk, which indicates that the bridge body will partially impede the river’s high discharge and increase the potential for flood risk in the study area. The eastern part of Kassala town has a higher likelihood of flooding than the western side. This study suggests redesigning structural measures like widening the Gash River, extending Kassala bridge for normal water flow, strengthening early warning systems, and implementing soil conservation activities for normal water flow.

Employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed: implications for flood management

Abstract. Extreme rainfall events in the Chi watershed of northeastern Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the watershed and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2022. The generalized Pareto distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using maximum likelihood estimation (MLE) and linear moment estimation (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet., Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2 d (CONS-2) and 3 d (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.

Comprehensive Risk Assessment Framework for Flash Floods in China

Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters.

Integrating Geographic Information Systems and Hydrometric Analysis for Assessing and Mitigating Building Vulnerability to Flash Flood Risks

Climate change represents an overwhelming challenge that demands urgent intervention for effective resolution. Among the devastating consequences of climate change, flash floods stand out as one of the most catastrophic repercussions. This research focuses on two primary objectives. Firstly, it aims to evaluate the existing state of flash flood intensity (FFI) in a specific area of Hamamatsu city, Japan, which frequently experiences flash flood incidents. Secondly, it seeks to develop a mitigation plan to alleviate the adverse impacts of flooding on buildings within the area. To accomplish these objectives, four parameters related to FFI (namely, runoff depth, runoff velocity, runoff duration, and affected portion) were selected and estimated through the implementation of hydrological and hydrodynamic models. Additionally, a hydrological model was employed, utilizing a storm event with a return period of 100 years as input. During this simulated storm event, FFI values were calculated and categorized into four distinct levels. The results revealed that more than one-tenth of the examined buildings encountered the highest scale of FFI (category 4), while categories 3 and 4 combined accounted for nearly three-quarters of all buildings in the study area. Moreover, two mitigation strategies were adopted to prevent flooding within the buildings’ vicinity. Finally, this study provides a valuable framework and guidance for decision-makers and insurance companies, enabling them to assess the flood hazard status of buildings and make informed decisions accordingly.

Application and Assessment of GIS Technology in Flash Flood Risk Management

This paper aims to explore the application of Geographic Information Systems (GIS) technology in the risk management of flash flood disasters and its role in safety assessment. Initially, the article briefly introduces the basic principles and functionalities of GIS technology, as well as how it can be effectively used in the monitoring, analysis, and prediction of flash flood disasters. Subsequently, the paper delves into the key applications of GIS in the risk assessment of flash floods, including the identification of disaster-prone areas, classification of risk levels, and planning of response strategies. The article also emphasizes the advantages of GIS technology in integrating multi-source data, achieving dynamic monitoring, and enhancing the efficiency of emergency response. Finally, through an analysis of existing research, the paper points out the potential and future directions of GIS in the field of safety evaluation of flash flood disasters. This study aims to provide more effective technical support and decision-making references for the risk management of flash flood disasters.

Assessing the influence of human activities on flash flood susceptibility in mountainous regions of Vietnam

Flash floods pose a significant and escalating threat to life, infrastructure, and property in the mountainous regions of Vietnam. Over the past two decades, Vietnam has experienced rapid socio-economic development, making it one of the fastest-growing countries in the region. However, this progress has been accompanied by urbanization, land cover conversion, and reductions in natural forest coverage, exacerbating the risk of flash floods. The objective of our study was to evaluate the quantitative influence of human activities on flash flood susceptibility from 2001 to 2022. Employing various machine learning algorithms, we analyzed 15 independent factors and examined 452 historical data points to predict the probability of flash floods and build flash flood susceptibility maps for the period of 2013–2022. By assuming similar weather conditions that trigger flash floods, we also estimated the probability and spatial distribution of flash floods for 2001–2020. To assess the anthropogenic impact, we conducted a detailed analysis of changes in land use patterns and employed indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). These factors played a significant role in shaping the differences in flash flood probability between the two time periods. The results demonstrated alarming trends, revealing a notable increase in the probability of flash floods by 7.69% and 4.01% in areas classified as high and very high susceptibility, respectively, over the past two decades. These findings provide tangible and robust evidence that policymakers can utilize to evaluate the implications of ongoing socio-economic growth. Furthermore, they serve as a critical foundation for formulating sustainable development plans that prioritize mitigating the future escalating risk of flash floods.