Flash Flood Vulnerability Research Articles

Impact of Land Use/Land Cover Change on Future Flash Flood Vulnerability and Monsoon Runoff in an Ungauged Himalayan Watershed

Impact of Land Use/Land Cover Change on Future Flash Flood Vulnerability and Monsoon Runoff in an Ungauged Himalayan Watershed

Channel-reach morphometric analysis on a headwater stream in a low-mountainous region: a case study from Mecsek Hills

Small catchments in mountainous regions affect downstream rivers as a primary source of sediment supply and also generate flash swasfloods, especially during extreme events. These floods have significantly shaped the catchments of small streams in the Mecsek Hills and some rural areas over the past two decades. However, there has been no previous study examining the hydromorphology of headwater catchments in low mountain environments in Hungary. The present study was meant to investigate the fundamental hydrogeomorphological properties of a first-order catchment. A customary and detailed GIS survey of 50-metre sections was aimed at deciphering flash flood vulnerability and geomorphic interrelations within a micro watershed. We found moderate susceptibility to flash floods compared to the whole Mecsek Hills. Stable large woody debris jams were identified during the field survey as major geomorphic channel features functioning as natural barriers which drive channel evolution and reduce flood hazards.

Assessment of Flash Flood Vulnerability Index in a tropical watershed region: a case study in Ciliwung Hulu watershed, Indonesia

Flash floods, an unpredicted swift climatological disaster, frequently occur in Indonesia. However, there are limited vulnerability assessments, especially in urban and vital regions such as Bogor District. The study aims to assess the vulnerability index of Ciliwung Hulu Watersheds as one of the most susceptible areas in the district. Flash flood vulnerability index (FFVI) is selected to be calculated as the indicator. Data were obtained from the official government offices and processed using the FFVI formula referring to the work of Nasiri et al., (2019) and Perka BNPB No. 2/ 2012 and then mapped using ArcGIS 10.3. The results and the maps show that the study area is categorized as highly to very highly vulnerable to flash flood disasters. The attained results help facilitate the governance interplay processes in building a more disaster-ready management plan and to construct a more resilient society.

Flash-flood susceptibility modelling in a data-scarce region using a novel hybrid approach and trend analysis of precipitation

ABSTRACTRecently, the incidence of heavy rainfall events and associated flash floods have encouraged us to investigate long-term trends in extreme rainfall and flash flood vulnerability mapping. Thus, in this study, a hybrid model was designed by integrating weight of evidence and Naïve Bayes (WOE-NB) to identify areas in Uttarakhand prone to flash floods, and we compared its ability with that of AdaBoost. Furthermore, the significance of long-term rainfall trends was evaluated using Mann–Kendall, modified Mann-Kendall, and innovative trend analysis (ITA), and extreme rainfall events were examined for 51 years (1970–2020). Results showed the WOE-NB and AdaBoost had acceptable goodness of fit (area under the curve = 0.969 and 0.973, respectively). Moreover, ITA can identify some important patterns based on on-trend results that other tests cannot. The return period revealed about 97.54% of the flash floods were caused by normal rainfall, with 2.45% being caused by severely abnormal rainfall.

A grey fuzzy analytic hierarchy process-based flash flood vulnerability assessment in an ungauged Himalayan watershed

A grey fuzzy analytic hierarchy process-based flash flood vulnerability assessment in an ungauged Himalayan watershed

Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling

Flash floods and landslides are dangerous natural hazards in hilly areas. They often occur extensively and potentially cause widespread destruction to agriculture, infrastructure, roads, houses, and human beings. This research aimed to analyze the hazard susceptibility on a mountainous roadway using advanced Machine Learning (ML) models. We conducted field surveys to collect data on flash flood and landslide locations in 2017, 2018, and 2019 on a particular roadway in Vietnam, National Highway 6, consisting of 88 flash flood sites and 235 landslide sites. The state-of-art ML models were utilized for the predictive modeling, including AdaBoost-RBF, Bagging-RBF, MultiBoostAB-RBF, and Random Sub-spaceRBF, with Radial Basis Function (RBF) serving as the primary classifier. The AdaBoost-RBF model outperformed all others in predicting landslide and flash flood vulnerability. The resulting map showed that 44.89% or 14,183 ha is in very high susceptibility zones, 15.55% or 4914 ha is in high susceptibility zones, 10.37% or 3.275 ha is in moderate susceptibility zones, 13.69% or 4324 ha is in low susceptibility zones, and 15.50% or 4899 ha is in very low susceptibility zones. A detailed map of the areas where landslides and flash floods are most likely to occur on the roadway might provide local authorities with crucial information for disaster management.

Assessment of potential flash flood hazards. Concerning land use/land cover in Aqaba Governorate, Jordan, using a multi-criteria technique

Aqaba is the principal port and the onlymaritime outletin Jordan. It is located in the northeastern tip of the Red Sea and is characterized by hyper arid climatic conditions. As such, Aqaba frequently experiences high intensity rainfall as flash floods. This paper intends to build a flash flood vulnerability map for Aqaba city and assess its impact on land use/land cover (LULC), that could help decision makers to take it into consideration in the planning process Six spatial layers were considered including: rainfall, elevation, slope, stream order (distance to stream), soil, and LULC. These layers were processed using GIS tools, multi-criteria analysis (MCA); in particular, the analytical hierarchy process (AHP) method was used to identify the optimal weights of each parameter contributing to potential flash floods. The map showed that 1960.5 km2 (24.4 %) 1960.5 km2 (28.4 %) corresponding to high, and very high PFF, respectively. River mouth, canyons, and flat low altitudes areas are vulnerable to high and very high flash floods hazards risk especially in urban and agricultural areas. In addition, they revealed that steep and ridge slopes, upper-stream high altitudes are less susceptible to FF hazards.

Refined assessment of flash flood vulnerability in Linzhi based on spatialization and GIS

Flash floods are one of the most serious mountain disasters in Linzhi, China, pose a grave threat to the lives and property of the residents. However, the current regional vulnerability research cannot meet the needs of refined disaster prevention and mitigation in mountainous areas. Therefore, to address this knowledge gap, this study presented a vulnerability assessment framework base on spatialization technique along with the triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and random forest (RF) model. To achieve this goal, an assessment system containing 12 indicators was constructed from the three perspectives of exposure, susceptibility and coping capacity. Firstly, factor spatialization is introduced in data preprocessing. Then TFN-AHP and the RF model were used to calculate the exposure, susceptibility, and coping capacity. Finally, the spatial distribution of the vulnerability throughout Linzhi was obtained. According to the results, the exposure, susceptibility and coping capacity in most areas are extremely low and low, accounting for 74.1%, 99% and 99%, respectively. The vulnerability is similar to the exposure that most of the regions have extremely low vulnerability and low vulnerability, accounting for 83.5%. The moderate vulnerability regions account for 8.5%, and the high vulnerability regions account for only 1%. The assessment results show that high-exposure areas mainly exhibit a linear distribution, and mainly include river valleys, cultivated land, and residential areas. Obviously, these areas are mainly distributed in the population and economic agglomeration areas and on low-altitude terrain. The distribution of high and comparatively high vulnerability zones, where economic and human activities are considerably high, are closely to the topography of the catchment. Thus, the results of this study provide scientific and technological evidence for resettlement, population distribution adjustment, and prevention and mitigation of flash floods in Linzhi.

Holistic characterization of flash flood vulnerability: Construction and validation of an integrated multidimensional vulnerability index

• Integrated and multidimensional approaches are key to identifying vulnerability underlying causes. • Validation of vulnerability indices can be tackled through sensitivity and uncertainty analysis. • Integrated and multidimensional vulnerability analysis is key to tackling integrated flood risk management. Over the last twenty years, numerous strategies and policies (e.g., 2030 Agenda for Sustainable Development) have emerged to promote flood risk reduction that is compatible with the conservation or restoration of river ecosystems, with the ultimate goal of achieving sustainable development. In this context, vulnerability analysis is considered a key aspect in flood risk reduction, and the most widespread methodology for its characterization is the construction of indices. However, such indices have been obtained so far through a fragmented approach, either because they do not consider all vulnerability dimensions (i.e., social, economic, ecosystem, physical, institutional and cultural) or components (i.e., exposure, sensitivity and resilience). Moreover, indices developed on a regional scale focusing on areas prone to flash floods are rarely validated, as the necessary information is often not available and flash floods do not occur simultaneously in all urban areas of a given region. This paper addresses the above two knowledge gaps and describes the construction at regional level of a flash flood Integrated Multidimensional Vulnerability Index (IMVI). Vulnerability here is characterized holistically, integrating in the index all the dimensions and components involved. Subsequently, an uncertainty and sensitivity analysis was performed to validate the IMVI. Lastly, vulnerability regional spatial patterns were identified through a Latent Class Cluster Analysis. The methodology implemented here allows one to identify vulnerability sources and their underlying causes, helping to improve flood risk management. Moreover, validation outputs enable to determine index uncertainty sources, thereby encouraging decision-makers to design more cost-effective vulnerability reduction strategies.

Fuzzy flash flood risk and vulnerability assessment for the city of Sanandaj, Kurdistan Province, Iran

Fuzzy flash flood risk and vulnerability assessment for the city of Sanandaj, Kurdistan Province, Iran

Flash Flood Vulnerability Mapping Based on FFPI Using GIS Spatial Analysis Case Study: Valea Rea Catchment Area, Romania.

The risk associated with extreme hydrological processes (flash floods, floods) is more present than ever, taking into account the global climatic changes, the expansion of inhabited areas and the changes emerging as a result of inadequate land management. Of all the hydrological risks, slope flash floods represent the processes that have the highest impact because of the high speed of their development and their place of origin, which makes them difficult to predict. This study is performed in an area susceptible to the emergence of slope flash floods, the Valea Rea catchment area, spatially located in Northwest Romania, and exposed to western circulation, which favours the development of such processes. The entire research is based on a methodology involving the integration of spatial databases, which indicate the vulnerability of the territory in the form of a weighted average equation to highlight the major impact of the most relevant factor. A number of 15 factors have been used in raster spatial databases, obtained by conversion (land use, soil type, lithology, Hydrologic Soil Group, etc.), derived from the digital elevation model (slope, aspect, TWI, etc.) or by performing spatial analysis submodels (precipitation, slope length, etc). The integration of these databases by means of the spatial analysis equation based on the weighted average led to the vulnerability of the territory to FFPI, classified on five classes from very low to very high. The final result underlines the high and very high vulnerability (43%) of the analysed territory that may have a major impact on the human communities and the territorial infrastructure. The results obtained highlight the torrential nature of the analysed catchment area, identifying several hotspots of great risk, located mainly within the built-up areas of intensely inhabited regions; a fact which involves a major risk and significant potential material damage in the territory. The model was validated by directly comparing the results obtained with locations previously affected, where the flood effects have been identified, highlighting the fact that the model may be taken into account to be applied in practice, and also to be implemented in territories that share the same features.

Geology and Potential Flash Flood Vulnerability of Sungai Jernih Village and Surroundings, Pondok Tinggi District, Kerinci Regency, Jambi

Sungai Jernih Village and its surroundings have relatively steep slopes. Based on rainfall data for the last three years this area has moderate rainfall, with a tropical climate. In general, making the weathering level around it high and causing weathered soil/rock material conditions so that it is not strong retain the volume of water that is absorbed by the surface. The weathering can be carried away by water currents and flash floods. Pondok Tinggi sub-district has experienced flash floods, one of which was on May 13, 2017. The method used in this study is a direct mapping method, namely conducting field observations to map the distribution of lithology. Prior to field observations, analysis of secondary data obtained from literature studies and other sources was carried out to consider the results of field observations as a basic reference for research. Mapping results in the form of maps and parameter analysis using scoring overlaid with ArcGIS software. The low level of vulnerability with an area of ​​51.25% consists of 2 sub-districts, are in Pondok Tinggi District and Kumun District. The level of vulnerability is sufficient with an area of ​​25.39% consisting of 2 sub-districts are Sungai Bungkal District and Pondok Tinggi District. Then the highest vulnerability level with an area of ​​23.36% consists of 4 districts are Pesisir Bukit District, Sungai Bungkal District, Sungai Bungkal District Full and Pondok Tinggi District.

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

Flash flood-induced vulnerability and need assessment of wetlands using remote sensing, GIS, and econometric models

Flash flood vulnerability mapping and livelihood vulnerability assessment are often considered essential elements of risk reduction strategies and act as indispensable initiatives. In line with the contention, an effort has been made to restrict flash flood vulnerability mapping and need assessment based on vulnerability classification at haor regions in Bangladesh using satellite remote sensing, geographic information system (GIS), and econometric models. First, flash flood vulnerability mapping was performed using a bivariate statistical regression-based frequency ratio (FR) model. The vulnerability mapping results revealed that wetlands from land use land cover (LULC), soil adjusted vegetation index (SAVI) (>0.65–0.94), drainage density (>0.0166–0.0219 m/m2), flooding depth (2.61–3.40 m), and rainfall (2243–2619 mm/6 months) have significant roles in flash floods occurring with selected factor weight values of 2.241, 1.935, 1.576, 1.391 and 1.032, respectively. Moreover, the outputs of vulnerability mapping were classified into very high (7.57%), high (22.56%), moderate (56.96%), low (12.34%), and very low (0.56%), covering areas of 1303.0, 3882.0, 9801.0, 2124.0 and 96.0 km2, respectively. Second, 40 sampled respondents from each vulnerable groups were interviewed to assess their vulnerability and coping strategies against flash floods using field survey data through the composite livelihood vulnerability index (CLVI). CLVI findings showed that the vulnerability differences among the five groups of respondents were relevant to vulnerability mapping and classification done in the remote sensing and GIS platform. The logit estimates explained that the age of the household head, household size, farming experience, educational status, occupation of the household head, farm size, proximity to the marketplace, and no. of earning members affect farmers' attitudes toward coping strategies at different levels significantly. Identifying the risk of a flash flood in providing farmers with accurate information, developing advanced risk management strategies, and providing agricultural credit and service provision, policymakers and research institutions may benefit from mapping and evaluating their livelihood vulnerability on a single platform.

Flash flood vulnerability assessment and zonation through an integrated approach in the Upper Ganga Basin of the Northwest Himalayan region in Uttarakhand

Flash Floods are one of the most destructive naturally occurring events, having a dreadful capability to cause havoc within a few hours. In India, Uttarakhand, a hill State of the Northwest Himalayan region, experiences flash floods every year, especially in the high elevation zones. In this study, morphometric parameters were derived from SRTM DEM data for 29 sub-watersheds of the Upper Ganga Basin (UGB) using Geographic Information System (GIS). The sub-watersheds were prioritized for flash flood vulnerability by employing Weighted Sum Approach (WSA), Principal Component Analysis (PCA), and an Integrated Approach (IA). A comparative assessment of the three approaches revealed that the results obtained by PCA and IA are relatively analogous in terms of the number of sub-watersheds classified under very high, high, and very low vulnerability zones. The IA methodology was validated through spatial analysis of previously occurred flash flood event locations of 2018 and 2019. For assessment of the vulnerable zones, remote sensing-based population distribution analysis and geospatial distribution analysis of 6360 towns and villages was carried out. Finally, the spatial analysis based on the final vulnerability map, population density map, and sub-watershed-wise geospatial distribution of towns and villages revealed that 5 densely populated sub-watersheds fall under very high and high vulnerability zones. The medium vulnerability zone also emerged as a critical zone. Therefore, IA has proved to be a replicable and reliable method to identify flash flood-prone vulnerable sub-watersheds. The study demonstrates an impactful use of remote sensing data and GIS for flash flood vulnerability zonation in mountainous catchments.

Flash flood vulnerability assessment of roads in China based on support vector machine

Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads.

Urban land cover and morphometric analysis for flash flood vulnerability mapping and riparian landscape conservation in Kebena River watershed, Addis Ababa

Addis Ababa city has a degraded stream ecosystem and redundant flash flooding that can destroy the existing urban infrastructure and utilities. This research aimed to map flash flood vulnerability of the Kebena watershed inside Addis Ababa and evaluate the status of the riparian landscape. Methodologically, the study employed the Biophysical Composition Index (BCI) to detect impervious surfaces and the Normalized Difference Vegetation Index (NDVI) to classify the vegetation cover. The Arc-hydro tool was used to identify micro-watersheds and measure the morphometric factors, then principal component analysis (PCA) identified the surrogate factors. Fuzzy overlay analysis combined land cover and morphometric analysis results to produce the final flash flood vulnerability map. Moreover, riparian buffering at 15, 30, and 90 m distances were defined to measure the degree of imperviousness, greenness, and vulnerability to flash flooding. Accordingly, 969 ha of land was depicted within the watershed as flash flood vulnerable areas. These areas are primarily found in the southeastern and southwestern parts where impervious land cover prevailed, and the northwestern portion which has extremely rugged terrain and has a sparse vegetation cover. For all buffering distances, the proportion of impervious surface is greater than the vegetation cover. Even within 15 m buffering distance, which was set as the national urban planning standard. It is concluded that Kebena watershed is vulnerable to flash floods as the riparian landscape is dominated by impervious and depleted vegetation cover. Therefore, integrated geospatial and statistical techniques are helpful to devise a method for sustainable riparian landscape monitoring.

Empirical flash flood vulnerability functions for residential buildings

The estimation of flood losses is crucial for the quantitative estimation of risk and the cost–benefit analysis of risk mitigation and prevention measures. Flood losses are usually estimated by means of vulnerability functions, which are often developed in a synthetic way by experts, since loss datasets collected after events are sparse or lacking and mostly refer to slow riverine inundations. Simple univariable empirical vulnerability functions are recognized as a more valuable tool with respect to synthetic curves developed for different contexts. The aim of this work is to derive empirical vulnerability functions for a flash flood event which occurred in Livorno (Italy) in September 2017, based on the hydrologic–hydraulic reconstruction of the flood and observed losses to residential properties. The hydrologic processes are reproduced with a distributed hydrologic model, and the flood propagation is simulated with a full 2D model. Many open GIS data are used and combined with virtual on-site inspection in the study area to analyze building exposure. Different regression curves are tested to fit the data and obtain damage curves. The results show (i) a poor correlation between relative or absolute losses and flow velocity, (ii) a low correlation between relative losses and water depth, thus confirming the existing literature. More significant correlation is found between absolute losses and water depths. The curves here derived provide a first attempt to develop damage functions for flash flood in Italy, which could be applied in similar urban and morphologic contexts.

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States

Flash flood is among the most catastrophic natural hazards which causes disruption in the environment and societies. Flash flood is mainly initiated by intense rainfall, and due to its rapid onset (within six hours of rainfall), taking action for effective response is challenging. Building resilience to flash floods require understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. This study provides a comprehensive assessment of socio-economic vulnerability to flash floods and investigates the main characteristics of flash flood hazard, i.e. frequency, duration, severity, and magnitude. A socio-economic vulnerability index is developed at the county level across the Contiguous United States (CONUS). For this purpose, an ensemble of social and economic variables from the US Census and the Bureau of Economic Analysis were analyzed. Then, the coincidence of socio-economic vulnerability and flash flood hazard were investigated to identify the critical and non-critical regions. Results show that the southwest U.S. experienced severe flash flooding with high magnitude, whereas the Northern Great Plains experience lower severity and frequency. Critical counties (high-vulnerable-hotspot) are mostly located in the southern and southwestern parts of the U.S. The majority of counties in the Northern Great Plains indicate a non-critical status.

Impacts of Triple Factors on Flash Flood Vulnerability in Egypt: Urban Growth, Extreme Climate, and Mismanagement

Urban growth, extreme climate, and mismanagement are crucial controlling factors that affect flood vulnerability at wadi catchments. Therefore, this study attempts to understand the impacts of these three factors on the flash flood vulnerability in different climatic regions in Egypt. An integrated approach is presented to evaluate the urban growth from 1984 to 2019 by using Google Images and SENTINEL-2 data, and to develop hazard maps by using a rainfall-runoff-inundation model (RRI). Annual rainfall trend analysis was performed to evaluate the temporal variability trend. The hazard maps that were created were classified into three categories (low, medium, and high) and integrated with the urban growth maps to evaluate the impacts on the flood-vulnerable areas. The results show a significant increase in urban growth resulting in an increase of prone areas for flood hazards over time. However, the degree of this hazard is mainly related to growth directions. Mismanagement affects urban growth directions in both planned and unplanned growth, whether by loss of control over unplanned growth or by deficiencies in approved plans. The rainfall analysis showed that there is no explicit relationship to increases or decreases in the flood vulnerable areas. An urban planning approach is recommended for risk reduction management based on a comprehensive study considering such factors.