Flood Vulnerability, Risk, and Susceptibility Assessment

Frequent flooding in the central part of the Hindu Kush region, Nepal, results in substantial damage to lives and property, particularly in the plains. This study explores the catchment-scale flood hazard and vulnerability, generating flood risks under climate change scenario. Flood model was developed using HEC-RAS 2D rain-on-grid approach and CMIP6 projected climate data for SSP245 and SSP585 scenarios was used. Flood hazard, vulnerability and risk maps were generated delineating potential inundation scenarios for both 50-year, and 100-year return periods, under present and future climate scenarios. Hazard maps revealed a probable increase in flooding under future climate scenarios. Vulnerability assessments also demonstrated an anticipated escalation in future. Cropland dominates the land-use type in the study area, with agriculture vulnerability increasing by 22.14% (SSP245) and 29.322% (SSP585). The number of inundated buildings increases from 2967 in the existing scenario to 4601 (SSP245) and 5274 (SSP585). Similarly, road vulnerability areas are increased by 28.06% (SSP245) and 37.86% (SSP585). The impact of flooding is higher in SSP585 scenario as compared to SSP245 scenario. The catchment level outcomes have diverse applications, particularly in decision-making for flood management, integrating risk maps for effective strategies, and providing valuable insights into shape climate change adaptation policies. Keywords: Catchment scale, Climate change, Flood vulnerability, Flood risk, HEC-RAS 2D.

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas.

Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flash-floods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.

With regard to minimizing flood damage, there are measures of different character each of which has its justification and plays an important role in flood protection. Implementation of traditional flood protection measures is still very important; however, an increasing role should be played particularly by flood prevention and flood risk management. The paper presents a case study on flood hazard and flood risk assessment at the local spatial scale using geographic information systems, remote sensing, and hydraulic modelling. As for determining flood hazard in the model area, which has 3.23 km2, the estimation of maximum flood discharges and hydraulic modelling were important steps. The results of one-dimensional hydraulic modelling, which are water depth and flow velocity rasters, were the basis for determining flood hazard and flood risk. In order to define flood risk, the following steps were applied: determining flood intensity on the basis of water depth and flow velocity rasters, determining flood hazard using three categories (low, medium, and high) based on flood intensity, defining vulnerability for the classes of functional areas using three categories of acceptable risk (low, medium, and high), and lastly determination of flood risk which represents a synthesis of flood hazard and vulnerability of the model area.

Flash floods are a significant risk in arid regions, especially in the Mekerra Basin of Northwestern Algeria, where rapid urbanization and climatic variability contribute to increasing flood vulnerability. This study integrates the Analytic Hierarchy Process (AHP) with Geographic Information Systems (GIS) to assess flood risk susceptibility in the basin. Eight key factors including, elevation, slope, drainage density, distance from rivers, flow accumulation, land use, topographic wetness index (TWI), and maximum daily rainfall were evaluated to calculate a Flood Susceptibility Index (FSI), categorizing the basin into flood risk levels. The findings show that 52.30 % of the basin is at moderate risk, while 42.37 % is at low risk. High-risk zones, representing 4.72 % of the area, are mainly located near the main wadi, where low-lying terrain, dense drainage networks, and urbanization contribute to increased flood vulnerability. The model achieved an 81 % accuracy when validated against historical flood data from 1986 to 2007, using a comparison of predicted flood-prone areas with observed flood occurrences. High-risk zones near the main wadi require immediate mitigation, such as reinforced infrastructure, while moderate-risk areas need targeted flood measures like improved drainage systems. Low-risk areas should focus on long-term strategies, including land-use planning and maintaining green spaces. The results highlight the combined impact of human activities and natural factors on flood risk, underscoring the importance of improving infrastructure and land-use planning, particularly in urban areas like Sidi Bel Abbes a major city in the basin. These findings underscore the need for targeted flood mitigation in urban areas and offer guidance for policymakers in improving infrastructure, land-use planning, and resilience against floods in the Mekerra Basin and similar regions. • Flood susceptibility in the Mekerra Basin in Algeria was evaluated, through the integration of the AHP and GIS techniques. • Eight flood-conditioning factors were examined to construct a Flood Susceptibility Index (FSI). • 52.30 % of the basin is classified as moderately susceptible to flooding, while 42.37 % falls within the low-risk category. • High-risk areas, comprising 4.72 % of the basin, are mainly located in low-lying urban area with dense drainage networks. • Model validation using historical flood data demonstrated an 81 % accuracy, confirming the reliability of the approach.

Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards.

Despite an increasing number of people exposed to flood risks in Europe, flood risk perception remains low and effective flood risk management policies are rarely implemented. It becomes increasingly important to understand how local governments can design effective flood risk management policies to address flood risks. In this article, we study whether high flood exposure and flood risk perception correlate with the demand for a specific design of flood risk management policies. We take the ideal case of Switzerland and analyze flood risk management portfolios in 18 flood-prone municipalities along the Aare River. We introduce a novel combination of risk analysis and public policy data: we analyze correlations between recorded flood exposure data and survey data on flood risk perception and policy preferences for selected flood risk management measures. Our results indicate that local governments with high flood risk perception tend to prefer non-structural measures, such as spatial planning and ecological river restoration, to infrastructure measures. In contrast, flood exposure is neither linked to flood risk perception nor to policy preferences. We conclude that flood risk perception is key: it can decisively affect local governments’ preferences to implement specific diversified policy portfolios including more preventive or integrated flood risk management measures. These findings imply that local governments in flood-prone areas should invest in raising their population’s awareness capacity of flood risks and keep it high during periods without flooding.

PurposeFloods have been identified as the most frequent and threatening disaster in Sri Lanka amidst an increasing trend of natural and man-made disasters in the world. Subject experts state that disaster risk management should be based on the results of risk assessments, but flood risk management in Sri Lanka is seemingly not based on community-level flood risk assessments. Accordingly, the purpose of this paper is to introduce a community-level flood risk assessment method to the local context of Sri Lanka.Design/methodology/approachThe sample (n = 425) for the study was selected using the stratified random sampling method, and the Deduru Oya basin was selected as the study area. The risk assessment model introduced by Bollin et al. (2003) was used for the current study, but with some modifications. Accordingly, 16 variables were selected for the risk assessment. Descriptive data analysis methods were used in the study.FindingsCommunity-level flood risk assessment method was introduced. Variable index, flood risk index and flood risk map were developed for the study area. The Grama Niladari Divisions (GNDs) were grouped into five categories from very high risk to very low risk. The GNDs named Wirakumandaluwa, Thimbilla, Deduru Oya, Bangadeniya and Elivitiya were ranked as the most flood-risk GNDs, respectively.Originality/valueThis paper produces a flood risk assessment method for the local context. Flood risk in the study area was assessed based on people’s perceptions. Accordingly, the flood risk index and flood risk map for the study area were developed based on the empirical data. GNDs were ranked based on the flood risk index.

There are several approaches to assess flood vulnerability as a proactive measure to reduce the risk of flooding. The indicator-based approach is primarily practiced from a policy point of view through the use of composite indicators. Composite indicators can be built from very easy to very complex and sophisticated methods. However, there are two complications that arise with this issue. On the one hand, the flood vulnerability index should be fairly simple, taking into account the interdisciplinary nature of various stakeholders involved in flood risk management. While on the other hand, addressing the issue of subjectivity or prejudice should be scientifically defensible. As there is no a single universally “best” methodological approach for the construction of composite indicator due to its data-specific nature for each individual study. The aim of this study is therefore to construct such an index of flood vulnerability that is not only intuitive to a variety of stakeholders, but also scientifically justified in the context of Khyber Pakhtunkhwa, Pakistan. Therefore, the current study demonstrated a detailed procedure to construct the flood vulnerability indices through different methodological approaches of data rescaling, weighting, and aggregation schemes, along with a fairly simple approach for robustness. For this purpose, data was collected through different (official) portals for the nine highly flood-prone districts of the Khyber Pakhtunkhwa. It was found that the weighting schemes had a greater influence on the flood vulnerability ranking of the selected districts compared to data rescaling and aggregation schemes. The simple model, which is the frequently using approach of building composite indicators in scientific community, was found to be appropriate for the selected data. The methodology adopted in the study can provide decision-makers and relevant authorities with a practical tool to identify and prioritize certain vulnerable areas and measures to mitigate current flood vulnerabilities while preparing for future flood risk mitigation in the province through a fairly simple and methodologically defensible approach.

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

Floods are one of the most widely distributed hazards globally, and their management is an essential issue of concern among all stakeholders, especially in developing countries. Flooding is one of the most significant natural hazards affecting the Tsholotsho District, causing widespread damage to infrastructure and the natural environment and endangering people's lives. This study assessed flood hazards and risk areas in the District using remote sensing (RS), Geographic Information Systems (GIS), and analytical hierarchy process (AHP). Six main factors were considered to identify flooding hazard wards in the District: drainage density (DD), flow length, elevation, Normalized difference, vegetation index (NDVI), slope, and distance from the river. The study also identified five flood risk zones: high, moderate, low, and very low. The result of the flood risk map revealed that the low-risk area was 433699.57 hectares (56%), the moderate-risk area was 278328.41 hectares (36%), and the very high risk was 41060.25 hectares (5%) of the District. The results indicate that low-lying areas, mainly adjacent to major rivers such as the Gwayi and Manzamnyama Rivers, are highly susceptible to flooding, exacerbated by land degradation and deforestation. The study highlights the critical role of integrating GIS and remote sensing in flood risk assessment, providing decision-makers with valuable insights for developing early warning systems, flood hazard zoning, and sustainable land management practices. Recommendations for improving flood resilience in Tsholotsho District include community-based disaster risk reduction programs and the implementation of adaptive infrastructure.

A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Flooding, caused by the excessive accumulation of water on land, disrupts activities in floodplain regions, particularly during the rainy season. The main objective is to map Flood vulnerability areas and identify regions most vulnerable to flooding to inform effective flood management strategies using an integrated approach that combines remote sensing, geographic information systems (GIS), and the analytical hierarchy process (AHP) to assess Flood vulnerability in the Wuseta Watershed. The research was conducted in three phases: pre-fieldwork, fieldwork, and post-fieldwork. Key factors influencing Flood vulnerability such as drainage density, elevation, land use/land cover, and slope were hierarchically weighted to produce a Flood vulnerability map. Rainfall distribution was not considered as a contributing factor the Ethiopian Meteorological Agency has installed only one weather station in the study area, located in Wuseta watershed. As a result, the rainfall distribution is considered uniform throughout the watershed, making it unsuitable for flood susceptibility assessment. The Flood vulnerability map categorizes the watershed into five zones: very high (0.07km2), high (4.65km2), moderate (7.86km2), slight (4.41km2), and very slight (0.001km2). The results show that the upstream, northern, northwestern, and northeastern areas of the watershed face slight to very slight Flood vulnerability, while the southern region is highly vulnerable to flooding. These findings provide valuable insights for policymakers and local communities, aiding in the development of targeted mitigation strategies and raising awareness of flood-prone areas. This study underscores the value of integrating geospatial technologies and multi-criteria decision analysis in flood risk assessment, particularly in data-scarce regions, to enhance disaster preparedness and climate resilience.

Calibrating a High-Performance Hydrodynamic Model for Broad-Scale Flood Simulation: Application to Thames Estuary, London, UK

This study assesses and maps the physical flood vulnerability within the Xerias, Krafsidonas, and Anavros ungauged catchments in Volos, Thessaly, Greece, using a Geographical Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) integrated with the Analytic Hierarchy Process (AHP). Six factors influencing flood dynamics were selected including slope, flow accumulation, geology, land use/cover, flood history and burned areas. The factors were weighted using the AHP based on their relative influence in flood occurrence. Physical flood vulnerability was assessed utilizing the Weighted Linear Combination (WLC) method and visualized through thematic flood-vulnerability maps. The analysis indicates that the southwestern and central-southern parts of the study area, which are highly urbanized and industrialized, exhibit the highest physical flood-vulnerability. Specifically, 32.76% of the Xerias catchment, 41.16% of the Krafsidonas catchment, and 34.71% of the Anavros catchment exhibit high to very high flood vulnerability. On the other hand, mountainous areas with steep slopes, permeable lithology, and dense forests exhibit low to very low physical flood vulnerability. The method’s accuracy was verified through sensitivity analysis and comparison with national flood-risk data for the study area. The results emphasize the physical vulnerability of Volos to flooding and the necessity for targeted flood mitigation measures, demonstrating the value of GIS in flood risk management.