Flash Flood Risk Research Articles

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.

Identification of the key driving factors of flash flood based on different feature selection techniques coupled with random forest method

Study regionThe mountainous area of Guangdong Province, China. Study focusA random forest coupling with feature selection (RF-FS) method framework, containing random forest coupling with shadow variable search, random forest coupling with recursive feature elimination, and random forest coupling with genetic algorithm, is introduced to identify the key driving factors (KDFs) of flash flood that can be used to explain the flash flood process and generate the flash flood risk map efficiently.New hydrological insights for the region: Among the KDFs identified by the RF-FS method framework, meteorological factors (maximum 1-day precipitation, total daily precipitation exceeding 95 % threshold, and maximum 5-day consecutive precipitation) are dominant, followed by hydrological factors (topographic wetness index and stream power index). Flash flood risk modeling was then developed by using the KDFs identified by the random forest coupling with genetic algorithm which is the best in the RF-FS method framework in predicting flash flood risks. Furthermore, the modeling results show that about 45 % of the mountainous areas in Guangdong Province are high flash flood-prone areas (high and extremely high-risk areas), and most of them are located in the central, eastern, and southwestern mountainous areas of Guangdong Province. The areas with flash flood-prone occurrences are generally consistent with the areas with large precipitation, implying that these areas should be prioritized for flash flood prevention and early warnings.

Performance benchmarking on several regression models applied in urban flash flood risk assessment

Performance benchmarking on several regression models applied in urban flash flood risk assessment

GIS-based flash flooding susceptibility analysis and water management in arid mountain ranges: Safaga Region, Red Sea Mountains, Egypt

The Safaga Region (SR) is part of the Red Sea mountain range in Egypt. Catastrophic flash flooding is now an inescapable event, wreaking havoc and causing massive loss of life and property. The majority of the floodwater, however, has been wasted as runoff to the Red Sea, which, if used wisely, could meet a fraction of the water demands for a variety of applications in this area. The current work aims to use GIS techniques to integrate remote sensing data for evaluating, mitigating, and managing flash floods in SR. The data set comprised Tropical Rainfall Measuring Mission (TRMM) thematic rainfall data, 1:50,000 scale topographical map sheets, geological maps, the ASTER Digital Elevation Model (ASTER GDEM), Landsat 7 Enhanced Thematic Mapper” (ETM7+), and Landsat 8 Operational Land Imager. The flash flood risk model of SR is developed using ArcGIS-10.3 geoprocessing tools integrating all the causal factors thematic maps. The final flood risk model for the SR suggests that 57% of the total basins in the SR are at high risk of flooding. Almost 38% of all basins are at moderate flood risk. The remaining 5% of basins are less prone to flooding. Flood-prone zones were identified, suitable dam-building sites were located, and extremely probable areas for water recharge were recognized. On the basis of reliable scientific data, structural and non-structural mitigation strategies that might reduce the damage susceptibility, alleviate the sensitivity of the flash flood, and best utilize its water supply were recommended.

An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

Purpose Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours. Design/methodology/approach This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed. Findings Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk. Originality/value The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Spatiotemporal rainfall variability and its relationship to flash flood risk in Northeastern Sylhet Haor of Bangladesh

Abstract The variability and distribution of rainfall are uniquely significant for climatic risk prediction. This study aims to assess spatiotemporal rainfall variability and flash flood intensity events in the Sylhet haor region of Bangladesh by analyzing rainfall data from April for the period 1995–2022. For this, we used both Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data and Bangladesh Meteorological Department (BMD) daily rainfall data. Rainfall patterns were studied using zonal statistics in ArcGIS and graphical illustration. The results revealed that the rainfall pattern was erratic and showed a range of spatiotemporal variability. If the average rainfall exceeds 250 mm in Meghalaya and Assam and 400 mm in Sylhet, severe flash floods may occur in the Sylhet haor region. An increase in pre-monsoon rainfall and its shift from May to April may increase the intensity of flash floods and consequently damage the rice crop. This finding might help flood management agencies to develop flood management strategies, prepare flood contingency plans, provide real-time and advanced warnings to strengthen flood warning and forecasting systems, and schedule seasonal agricultural activities.

Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE

Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE.

Risk Assessment and Mapping of Flash Flood Vulnerable Zones in Arid Region, Fujairah City, UAE-Using Remote Sensing and GIS-Based Analysis

A flash flood is the most common natural hazard that endangers people’s lives, the economy, and infrastructure. Watershed management and planning are essential for reducing flood damages, particularly in residential areas, and mapping flash flood-sensitive zones. Flash flooding is an interface dynamic between geoterrain system factors such as geology, geomorphology, soil, drainage density, slope, and flood, rather than only water movement from higher to lower elevation. Consequently, the vulnerability to flash floods necessitates an awareness of and mapping topographical features. A flash flood vulnerable zones (FFVZ) map is essential for thorough flash flood risk assessment and management to minimize its detrimental effects, particularly in residential areas, especially in cities like Fujairah with seven wadis flowing into the city and even though it has two main dams and fifteen breaker dams. So, in this work, eight satellite image-derived parameters rainfall, elevation, slope, land use/land cover (LULC), drainage density, geology, geomorphology, and soil were combined to predict the flash flood-vulnerable zones using a weighted overlay technique based on geographic information systems (GIS). Each element of the thematic maps is ranked and weighted according to how vulnerable it is to flash floods in the study area, with 55 km2 being classified as a very highly vulnerable area, 78 km2 as a high-risk area, 9.3 km2 as a moderate risk area, 70 km2 as a low vulnerable area, and 257 km2 as a very low vulnerable area. In addition, places with a very high vulnerability level include the Fujairah Airport, Fujairah Port, some residential neighborhoods in the city’s center, oil storage areas, two hospitals, and universities. Additionally, from 1990 to the present, Landsat and Sentinel 2 data showed consistent changes in vegetation and built-up areas. Therefore, in addition to helping policy and decision-makers make the best choices about the efficacy of the study area’s protective structures against the risk of flash floods in the future, the results can also be a valuable source of information.

Влияние ливневых паводков на строительство сооружений по сбору дождевой воды

Introduction. The lack of fresh water has become a serious problem. The main causes of the water crisis are population growth and climate change. Egypt is a very arid country which is facing water scarcity due to increasing demand and limited supply. Applying new approaches is required in this situation to deal with water scarcity. Wadi Watir in the Sinai Peninsula, chosen as the research area, is an important tourist and commercial watershed. It experiences water scarcity and the threat of flash floods. The residents are losing their crops and sheep. Roads are deserted, and their lives are in danger due to the periodic occurrence of flash floods. Materials and methods. For the first time, the current research investigates the impact of flash floods on the implementation of rainwater harvesting (RWH) systems. The flash flood risk and RWH structure maps were overlaid using GIS. Results. The results showed that five percolation tanks, five check dams, 11.15 km2 of area for on-ground percolation tanks, and 0.48 km2 of space for farm ponds are optimal for water harvesting during flash floods. Conclusions. The development of Wadi Watir was prioritized and divided into three phases. Therefore, the occurrence of flash floods clearly affects the development plans. The impact of flash floods should be considered during the calculation of the design capacity of the RWH structures.

An Integrated Multidimensional Resilience Index for urban areas prone to flash floods: Development and validation

Resilience analysis is critical in developing flash flood risk reduction strategies in the context of global change and sustainable development. The most common method for assessing resilience is index-based. Nevertheless, the resulting indices typically fail to represent resilience's multidimensional character since they frequently disregard all involved dimensions (i.e., social, economic, environmental, physical, institutional, and cultural). Furthermore, regional resilience indices are rarely externally validated in urban areas prone to flash flooding because the required data are limited and flash flooding does not occur concurrently throughout the study region. This research developed and validated a regional Integrated Multidimensional Resilience Index (IMRI) in urban flash flood-prone areas to address the aforementioned knowledge gaps. The Monte Carlo method enabled internal validation of the IMRI following uncertainty and sensitivity analyses. Latent Class Cluster Analysis (LCCA) was used to characterize resilience, leveraging resulting regional spatial patterns. The findings obtained revealed that the most resilient urban areas have greater social and cultural resilience, while the least resilient urban areas should strengthen their social and institutional resilience. Validation results demonstrated a low bias between the IMRI scores and the control statistics derived from the Monte Carlo analysis as well as a higher than 80 % probability of not getting variations in the resilience categories, confirming the robustness of the IMRI. Through LCCA, five distinct regional spatial patterns of resilience were identified. The methodological approach deployed here enabled the identification of the underlying characteristics that determine the urban system's resilience to flash flooding, thereby supporting the formulation of resilience-building strategies for each dimension and urban area under consideration.

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.

Risk of Flash Floods in Urban and Rural Municipalities Triggered by Intense Precipitation in Wielkopolska of Poland

This research analyzed interventions of State Fire Service (SFS) units in the Wielkopolska region of Poland that were triggered by extreme precipitation for the period 2010–2021. Our results demonstrated that the most populated and urbanized towns in the Wielkopolska (Greater Poland, west of Warsaw) region are at the most risk in the event of extreme precipitation occurrence as measured by the total number of interventions made by the SFS. The number of SFS unit interventions in towns, standardized to 10,000 inhabitants, indicates that the highest proportional volume of interventions also occurred in smaller towns. In the rural municipalities the number of SFS unit interventions increases along with higher population density and proportion of infrastructure areas. As analyzed in this study, the 12 years from 2010 to 2021 were characterized by a higher number of days with heavy precipitation, for example, 20, 30, 40, and 50 mm, in comparison to the previous periods 1961–2010 and 1981–2010. Intervention databases collected by emergency services are a valuable source of information for hazard mapping. Based on those and other available data, a statistical model was created and factors influencing the local and regional occurrence of interventions were determined. Increasing suburbanization, the rising proportion of impermeable surfaces, and the impact of climate change are of considerable importance in urban flood risk. It is necessary to help municipalities develop abilities to absorb larger amounts of rainwater.

The rain deluge and flash floods of summer 2022 in the United Arab Emirates: Causes, analysis and perspectives on flood-risk reduction

Across the hyperarid Arabian Peninsula, preparing to face evolving risks of flash-flood hazards against projected scenarios for climate change requires both an understanding of the extreme rainfall events responsible and the implementation of appropriate mitigation/adaptation measures. In July 2022, torrential storms affected northeastern UAE. The damaging impacts prompted a broader consideration of flash-flood risks, especially for rapidly-urbanising arid landscapes. Occurring unexpectedly in mid-summer when rainfall is normally absent, the storms were produced by a combination of climatic and oceanic conditions: ITCZ position, unusual westward extension of a humid air mass from the Indian monsoonal trough, a cool sea-surface temperature anomaly that drove strong onshore moisture advection, and orographic precipitation enhancement by the coastal ranges. A foreign media suggestion that cloud seeding was partly responsible, however, is contested. Although flood emergency management was coordinated and effective, it is argued that various strategies may be strengthened to reduce future disaster risks. These include increased monitoring of mountain wadi systems to augment meteorological forecasts with hydrological information, and improved modelling to predict runoff-generation processes better in ungauged catchments. Greater capture of floodwaters to infiltrate and replenish groundwater aquifers also supports ongoing national efforts to develop sustainable solutions for addressing freshwater scarcity.

Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.

Risk assessment and management of vulnerable areas to flash flood hazards in arid regions using remote sensing and GIS-based knowledge-driven techniques

Earth Observation from space has allowed characterizing, detecting, and managing natural hazards in spatiotemporal scale. Flash flood is the most frequent natural disaster that causes destruction to human lives, the economy, and infrastructure. Thus, developing a flash flood hazard zone (FFHZ) map is significant for comprehensive flash flood risk assessment and management to minimize its harmful effects, particularly in residential areas, because of climate change. Therefore, in this article, ten parameters derived from satellite images, including lithology, slope, topographic wetness index (TWI), Stream Power Index (SPI), Stream Transport Index (STI), Terrain Roughness Index (TRI), drainage density (Dd), distance to river, radar intensity map, and rainfall distribution map, were fused to predict the flood-vulnerable areas through GIS-based overlay analysis after normalization and assigning weight by applying Analytical Hierarchy Analysis (AHP). The findings allowed for the identification of the most vulnerable areas and provided an explanation for the flood's effects on New Qena City (NQC). The output FFHZs of the Wadi Qena Basin (WQB) were divided into six hazard zones, i.e., extreme hazard (6.86%), very strong (15.04%), strong (18.74%), moderate (22.58%), low (22.80%), and very low (13.98%) susceptibility. Furthermore, approximately 35% of the under-construction NQC is subject to the extreme to very serious hazards, as opposed to the extension area to NQC east of the Qena-Safaga Road. Interferometry Synthetic Aperture Radar (InSAR) change detection coherence (CCD) and spatiotemporal analysis of Landsat and Sentinel-2 data revealed steady changes in vegetation and infrastructure from 1984 to present. Based on GIS analyses about 10, and 14% of the NQC can be inundated if the flood extends 500, and 1000 m around the flood canal, respectively. Thus, several strategies were advised to safeguard the development projects, particularly the residential sections of the under construction NQC, including erecting four dams with a total capacity of 300 million m3, reinforce the dam at Wadi Shahadein, constructing concrete chevron bunds along the flood zone, and extending the depths of the flooding canal.

Development of Freshwater Fish Cultivation Business and Its Derivative Products In Kajar Hamlet

Batu City is well-known as one of the cities based on agribusiness and tourism in Indonesia. Agribusiness that is widely developed in Batu City are vegetables and fruits. Kajar Hamlet which is located in Pandanrejo Village, Bumiaji District, Batu City certainly has great agribusiness potential as well. Kajar Hamlet also owns various forms of agribusiness with vegetable and fruit products as well as decorative flowers. But the potential that is no less interesting is the large demand for fresh freshwater fish products that come from many restaurants. Kelompok Peternak Ikan Nila is trying to develop a tilapia cultivation business. Starting from assistance from ITS-Surabaya (as university) to cultivate tilapia using cages in waterways in Kajar Hamlet and evaluation of failures due to the risk of flash floods that have occurred, tilapia cultivation was made using fish ponds by utilizing the residents' unproductive land and the adequacy of water from existing waterways without changing the main function of the waterways as irrigation for agricultural areas. This paper attempts to present the process of developing a freshwater fish farming business in Kajar Hamlet along with an analysis of the business and potential of derivative products that can be further developed.

Mapping of Areas Vulnerable to Flash Floods by Means of Morphometric Analysis with Weighting Criteria Applied

Flash floods, produced by heavy seasonal rainfall and characterized by high speeds and destructive power, are among the most devastating natural phenomena and are capable of causing great destruction in very little time. In the absence of hydrological data, morphometric characterization can provide important information on preventive measures against flash floods. A priority categorization of hydrographic units in the Cañete River basin was carried out using morphometric analysis together with a weighted sum analysis (WSA) based on a statistical correlation matrix. The delineation of the drainage network was performed based on Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission (SRTM). The Cañete River basin was subdivided into 11 sub-basins, and 15 morphometric parameters were selected. The priority category (very high, high, and moderate) of each sub-basin was assigned according to the value of the composite factor obtained through WSA. The results of this analysis showed that 26.08% of the total area is under a very high flash flood risk (sub-basins 3, 9, and 11), 38.46% is under a high flash flood risk (sub-basins 5, 7, 8, and 10), and 35.45% is under a moderate flash flood risk. This study concludes that flash floods predominate in sub-basin 3 and that downstream areas present characteristics of river flooding (sub-basins 9 and 11).

Mapping and Assessment of Flood Risk in the Wadi Al-Lith Basin, Saudi Arabia

Flood risk mapping is vital in watershed management and planning, especially in reducing flood damages. In this study, a flood risk map was developed for the Wadi Al-Lith watershed (Saudi Arabia) by combining geographic information system techniques with a multi-criteria decision-making method known as the Analytical Hierarchy Process (AHP). Several factors were investigated in the study, including elevation, slope, topographic wetness index, drainage density, rainfall, soil and land use, and land cover. The watershed was divided into five regions: very high, high, moderate, low, and very low flooding danger areas. The results showed that 35.86% of the total watershed area is under high and very high flood risks, while 26.85% of the total area is under a moderate flood risk. Less than 38% of the total watershed area was under a low flooding risk. The results of the developed model were validated with the flooding event that occurred on 23 November 2018 in the study area. The model was also compared with the flood mapping of the 100-year return period generated by the HEC-RAS software. Both the developed model and the HEC-RAS software showed similar results. The findings demonstrated that the developed model could be used to develop flood risk maps, especially in watersheds that experience scarcity and shortages in the short-interval rainfall measurements and the stream flow gauges (e.g., Wadi Al-Lith watershed and other watersheds in Saudi Arabia). Additionally, the obtained results can provide helpful knowledge for the policy- and decision-makers to make the right decisions regarding the effectiveness of the protective structures of the study area against the risk of flash flooding in the future.

Morphometric Parameters and Geospatial Analysis for Flash Flood Susceptibility Assessment: A Case Study of Jeddah City along the Red Sea Coast, Saudi Arabia

Flash floods are one of the most common and destructive natural hazards, and recent flood events show their severe impact on Saudi Arabia. Flash floods are increasing year by year in duration and intensity, resulting in huge destructive impacts for the nation concerned. Anticipating the spatial patterns and occurrence of rainfall-induced floods is in high demand. Recent technique-based studies and their comprehensive results aid in understanding the flood potential of the drainage basins and in minimizing the risks of a threat to humans and of economic damage. Jeddah City is located at the western coast of the Red Sea in Saudi Arabia and is one of the most important coastal cities in the Arabian Kingdom. It has experienced several destructive flash flood events, particularly in 2009 and 2011, causing serious damage and significant loss of life. An analysis of the morphometric parameters using geospatial techniques provides significant insights into the hydrological response of the drainage basins to major heavy rainfall events. In this paper, two relative flood susceptibility scenarios were produced: drainage basin levels and very accurate pixel-level conditions. The morphometric comparison levels suggest that basins 1 and 7 are very high, whereas the other basins have moderate and low levels. The derived flood susceptibility map was integrated with the topographic position and wetness algorithms (TPI and TWI) through overlay processing. The integration analysis aids in realizing the relationship between the general basin morphometric characteristics and the in situ relief for producing the flood susceptibility spots over the entire basins. Thus, the method of this paper can be applied to evaluate the site-specific plan minimizing the effects of flash flood risks in similar areas.

Study on Risk Assessment of Flash Floods in Hubei Province

Flash floods are typically associated with short, high-intensity and extreme rain-storms, and they are characterized by short response time and severely impact and damage communities in different areas in China. In order to scientifically assess the risks of flash floods, this paper takes Hubei Province as an example to carry out risk assessment. Based on Pearson correlation coefficient and principal component analysis methods, 14 factors were selected from 98 factors to establish a risk assessment model. The confidence coefficient model and multi-factor superposition method were used to determine the weight of each risk factor, and a risk map of Hubei Province was finally constructed. The results show that medium-high risk areas in Huanggang account for 47.00%, and high-risk areas account for 8.70%, with both areas adding up to more than 50%, followed by more than 40% in Shiyan, E’zhou and Xianning, and more than 30% in Huangshi, Yichang, Xiangyang, Jingmen and Suizhou. The risk level distribution is highly consistent with the location and frequency of flash flood disasters, shows high reliability, and can provide data support for flash flood disaster prevention and control. This study used a quantitative method to determine the key factors affecting flash flood disasters and provides a reference and basis for flash flood risk assessment in other provinces in China.