Flood Hazard Areas Research Articles

A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards.

Insurance Coverage and Flood Exposure in the Gulf of Mexico: Scale, Social Vulnerability, Urban Form, and Risk Measures

Increasing flood losses in the Gulf of Mexico related to development patterns and climate hazards pose serious threats to resilience and insurability. The purpose of this study is to understand how scale, social vulnerability, risk, and urban form relate to National Flood Insurance Program (NFIP) policy coverage and flood exposure. Our multilevel models identify that flooding is significantly clustered by region and counties, especially shoreline counties. Our measures of risk suggest that the Federal Emergency Management Agency (FEMA) special flood hazard area (SFHA) underestimates risk and exposure when compared with the Flood Factor and that there is some compensation in terms of insurance coverage, suggesting a pattern of adverse selection. Older housing stock appears both less insured and less exposed, raising questions of whether current growth patterns are increasing risk independent of environmental change. Our models suggest that census tracts with higher percentages of black residents are less insured and more exposed, and a similar pattern exists for rural areas. Our results highlight the need to seek common solutions across the Gulf of Mexico, concentrating on the most flood-exposed counties, and that specific resilience strategies may be necessary to protect areas with socially vulnerable populations, especially in rural areas. Underlying challenges exist due to the spatial relationship between exposure and social vulnerability and the potential for adverse selection in insurance markets due to different measures of risk.

Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management.

An Examination of the Decision of Microentrepreneurs to Remain in Urban Flood-prone Areas in Marikina City, Metro Manila (NCR), Philippines

Despite the impact of urban flooding on businesses, the choice of microentrepreneurs continuing to stay in flood hazard areas runs counterintuitive to the principles of profitability and sustainability albeit both are the most vulnerable aspects of the business sectors. Existing literature has provided substantial knowledge on the factors affecting an entrepreneur’s choice of operating location but does not go deeper into why entrepreneurs decide to remain and operate in frequently flooded urban areas. A multi-stage cluster sampling technique was used in this study to establish an adequate representation of microentrepreneurs in flood-prone areas of Marikina City in Metro Manila, Philippines, using various statistical tools to evaluate decision factors. The research revealed that component factors such as economic considerations, the microentrepreneur’s socio-spatial relations, and experiences and perception of resilience influence the decision to remain in urban flood-prone areas. Moreover, specific sub-factors under each component factor were identified, which can be grouped into ‘pre-existing’ and ‘learned’ categories, introducing a new location decision model attuned to the dynamics that exists between microentrepreneurs and a natural disaster such as urban flooding. The findings of the study may guide policymakers and urban planners in designing targeted intervention measures that translate to more micro enterprise-responsive disaster risk reduction and management plans. Moreover, by treating microenterprises as separate from the traditional occupations, communities might appreciate the crafting of local land use plans which would successfully improve the overall resilience of this sector in urban flood-prone areas.

Flood Hazard Mapping in Cimandiri Watershed using Fuzzy Logic Method Based on Multi-Source Geospatial Datasets

Abstract Flood is a frequent disaster in the Cimandiri watershed. This is evidenced by the fact that during 2017-2022, 37 floods occurred in this area. This condition causes losses to the community and damage to the environmental ecosystem. Therefore, disaster management is needed to overcome and reduce the impact of this flood. One management that can be done is by mapping flood hazard areas. However, until now there is no map available that presents the flood hazard study area in the Cimandiri watershed. If available, the maps generally cover Regency, sub-Regency, or large-scale maps covering the entire territory of Indonesia. This condition causes deficiencies or misinformation on the map because the scale of the study is irrelevant. Based on this, this study attempts to conduct flood hazard mapping in the Cimandiri watershed with a scale of analysis of 100,000. This study uses fuzzy logic method to generate hazard maps based on spatial parameters, rainfall, distance from the river, elevation, slope, vegetation density, land cover, soil type, and topographic wetness. The results of this method produce a hazard map with a class range of 0 to 1, a class that is close to 1 means that it has a high level of hazard. To see the correctness of this map, a validation test was carried out using flood data from 2017-2022 which contained 37 flood event data. Based on the validation results, 86% of flood points are in the non-0 hazard class (low hazard), only 13% are incorrect because they are in the 0-hazard class.

Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia

ABSTRACT Increased frequency and magnitude of flooding pose a significant natural hazard to urban areas worldwide. Mapping flood hazard areas are crucial for mitigating potential damage to human life and property. However, conventional hydrodynamic approaches are hindered by their extensive data requirements and computational expenses. As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. Model evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). Our results demonstrated promising outcomes, with AUC values of 93% (DT), 97% (RF), 98% (XGB), and 91% (kNN) for the validation dataset. RF and XGB have slightly higher performance than DT and kNN and distance to river was the most important factor. The study highlights the potential of ML for urban flood modeling, offering reasonable accuracy and supporting early warning systems. By leveraging available geo-environmental variables, ML techniques provide valuable insights into flood hazard mapping, aiding in effective urban planning and disaster management strategies.

Flood detection in the Upper Krishna Basin through integrated geospatial analysis: leveraging decision frameworks and statistical measures

ABSTRACT Floods threaten the environment and human settlements across river basins globally, including the Upper Krishna Basin in India. This research delves into evaluating flood hazard areas within the Upper Krishna Basin utilizing the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Statistical Index (SI). These methodologies prioritize and classify flood-prone regions by integrating spatial and non-spatial criteria. The findings reveal significant variations in flood risk classification across the Upper Krishna Basin based on the three models. The AHP model identifies 3.37% of the region as low flood risk, with 22.90% classified as moderate risk, and 68.27% as high risk. In contrast, the FR model designates 3.76% as low risk, 10.50% as moderate risk, and 42.21% as high risk. Meanwhile, the SI model identifies 1.04% of areas with low risk, 35.38% with under-high risk, and 57.87% with very high risk. Validation using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) values demonstrates the superior reliability of the SI model. These findings offer valuable insights for decision-makers to allocate resources and implement effective flood mitigation measures.

Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.

Integrated flood hazard assessment using multi-criteria analysis and geospatial modeling

Floods are one of the most prevalent disasters worldwide, including in the capital city of Indonesia. Multi-criteria decision analysis is combined with Geographic Information Systems to produce a mapping of flood hazard areas. The weighting for each parameter is based on six criteria: rainfall, slope gradient, topography, soil type, land cover, and distance from rivers. The flood hazard map is validated using inundation data from the Regional Disaster Management Agency for the years 2015 and 2020. From the general analysis, it can be determined that the parameter most influencing floods is rainfall with a weight of 0.270, followed by slope gradient at 0.164, topography at 0.124, soil type at 0.096, land cover at 0.190, and distance from rivers at 0.155. Therefore, through mapping using QGIS, it is revealed that in 2002, highly flood hazard areas comprised 20.99% of the total Ciliwung Watershed area, which increased to 24.31% in 2020. The validation of the flood hazard map was conducted by recording the coordinate points of flood incidents in 2015 and 2020, revealing that the affected areas within the Ciliwung Watershed occurred in high to very high vulnerability zones. This research demonstrates that flood events in the study area occurred in high to very high flood hazard zones. The results of this study are considered valuable and important for providing accurate information to local governments to develop cost-effective and efficient strategies in dealing with potential flood hazards.

Urban development assessment in flood hazard areas using integrated HEC-RAS, SCS, and FAHP models: a case study of Pardisan Settlement, Qom

Urban development assessment in flood hazard areas using integrated HEC-RAS, SCS, and FAHP models: a case study of Pardisan Settlement, Qom

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

Effects of damage initiation points of depth-damage function on flood risk assessment

The flood depth in a structure is a key factor in flood loss models, influencing the estimation of building and contents losses, as well as overall flood risk. Recent studies have emphasized the importance of determining the damage initiation point (DIP) of depth-damage functions, where the flood damage is assumed to initiate with respect to the first-floor height of the building. Here we investigate the effects of DIP selection on the flood risk assessment of buildings located in Special Flood Hazard Areas. We characterize flood using the Gumbel extreme value distribution’s location (μ) and scale (α) parameters. Results reveal that average annual flood loss (AAL) values do not depend on μ, but instead follow an exponential decay pattern with α when damage initiates below the first-floor height of a building (i.e., negative DIP). A linear increasing pattern of the AAL with α is achieved by changing the DIP to the first-floor height (i.e., DIP = 0). The study also demonstrates that negative DIPs have larger associated AAL, thus contributing substantially to the overall AAL, compared to positive DIPs. The study underscores the significance of proper DIP selection in flood risk assessment.

Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping.

Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management.

Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain

Special flood hazard areas (SFHAs), defined as having an annual probability of occurrence of 1 percent or above, are used by U.S. Federal Emergency Management Agency (FEMA) to demarcate areas within which flood insurance purchase is required to secure a mortgage. However, quantifying flood risk within SFHAs can be challenging due to the lack of modeled flood depth data for all return periods. To address this issue, this research quantifies flood risk indicated by average annual loss (AAL) within the A Zone—the subset of the SFHA where wave heights can potentially range from 0 to 3 feet. The methodology resolves the Gumbel quantile function for four distinct flooding cases (i.e., locations flooded at return periods exceeding 1.58-, 10-, 25-, and 50-year return period events) and generates synthetic flood hazard parameters for these cases within the 100-year floodplain, as well as with additional elevation above the base flood elevation (BFE), known as freeboard, for single-family homes with different attributes. The results indicate that for single-family homes in the A Zone, with the lowest floor elevated to the BFE, the AAL ranges from 0.3 to 1 percent of the building replacement cost value. Adding one foot of freeboard reduces flood risk by over 90% if the annual flood risk is between the minimum and 25th percentiles and the 100-year flood depth is less than two feet. The demonstrated approach helps enhance flood resilience in the A Zone, demonstrating the feasibility of proactive measures to protect communities.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring the necessity of effective flood management and mitigation efforts. Identifying flood hazard areas is the very first step in achieving such ends. While machine learning algorithms have been widely used in flood susceptibility studies, there is a significant dearth of research addressing their application for local-scale assessment of flood susceptibility in Bangladesh. Besides, in most of the flood susceptibility studies, only statistical metrics are used to evaluate the prediction capability of machine learning algorithms, overlooking their ability to account for spatial consistency. Therefore, this study attempts to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in flash flood susceptibility assessment at local scale using statistical and spatial metrics. RF model performed better than SVM with area under the curve (0.964), accuracy (92.1 %) and kappa (0.894). Five susceptibility zones were identified using a natural breaks method: very high, high, moderate, low and very low. The overall spatial agreement between the susceptibility maps of RF and SVM was 73.3 %. For very low susceptible classes, the spatial agreement was highest for RF-SVM (97.15 %) and SVM-RF (81.54 %). The findings of this study can be useful for a more accurate assessment of flash flood susceptibility in similar local settings. Moreover, the prepared susceptibility map can be helpful for taking sustainable measures to mitigate the devastating effects of flood hazard in northeastern Bangladesh.

Integrating multi-criteria analysis and geospatial applications for mapping flood hazards in Sekondi-Takoradi Metropolis, Ghana

Despite numerous structural and non-structural measures to curb the problem, flood disasters have become a recurring theme globally. Reducing vulnerability to flooding requires a constant reassessment of flood hazard areas since their spatial extent keeps changing. This paper sought to map flood hazards in the Sekondi-Takoradi Metropolis (STM) of Ghana. STM experiences frequent flooding incidents, rapid urbanization, and massive encroachment of lands, partly due to a booming petroleum industry. However, an empirical spatial assessment of its flood hotspots is non-existent. We applied a multi-criteria analysis (specifically the Analytical Hierarchy Process [AHP]) to address this issue by processing causative flood parameters derived from remotely sensed images using a geographical information system. The principal results show that about 12% and 24% of STM lands are in very high and high flood zones, respectively. The rest of the land is in moderate (24.6%), low (24.3%), and very low (15%) flood zones. Areas along the lower portions of STM are identified as most susceptible to inundation. A computed consistency ratio of 0.03 is indicative of the efficiency of the study's model. These findings provide relevant stakeholders with an extensive understanding of the different flood zones for effective flood planning and prevention in STM.

Geochemical behavior of heavy metals and radionuclides in a pit lake affected by acid mine drainage (AMD) in the Muskau Arch (Poland)

Pit lakes in the ‘anthropogenic lake district’ in the Muskau Arch (western Poland; central Europe) are strongly affected by acid mine drainage (AMD). The studied acidic pit lake, ŁK-61 (pH <3), is also exposed to floods due to its location in the flood hazard area, which may significantly influence the geochemical behavior of elements. The elemental compositions of water and lake sediment samples were measured with ICP–OES and ICP–MS. The sediment profile was also examined for 137Cs and 210Po activity concentrations using gamma and alpha spectrometry, respectively. Grain size distribution, mineralogical composition, diatoms, and organic matter content in the collected core were also determined. The key factors responsible for the distribution of selected heavy metals (e.g., Cu, Ni, Pb, Zn) and radioisotopes (137Cs and 210Po) in the bottom sediments of Lake ŁK-61 are their coprecipitation/precipitation with Fe and Al secondary minerals and their sorption onto authigenic and allogenic phases. These processes are likely driven by the lake tributary, which is an important source of dissolved elements. The data also showed that the physiochemical parameters of Lake ŁK-61 water changed during an episodic depositional event, i.e., the flood of the Nysa Łużycka River in the summer of 2010. The flood caused an increase in the water pH, as interpreted from the subfossil diatom studies. The down-core profiles of the studied heavy metal and radionuclide (HMRs) contents were probably affected by this depositional event, which prevented a detailed age determination of the collected lake sediments with 137Cs and 210Pb dating methods. Geochemical modeling indicates that the flood-related shift in the physicochemical parameters of the lake water could have caused the scavenging of dissolved elements by the precipitation of fresh secondary minerals. Moreover, particles contaminated with HMRs have also possibly been delivered by the river, along with the nutrients (e.g., phosphorus and nitrogen).

Modes of climate mobility under sea-level rise

Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5 m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1 m SLR, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2 m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales.

Can sediments play a role in river flood risk mapping? Learning from selected European examples

BackgroundClimate change and increasing anthropogenic pressure are two of the major drivers of increasing extreme events like droughts and floods. To deal with the increasing number of flooding events hitting Europe in the last few decades, around twenty years ago the European Commission started to develop ad-hoc legislation to reduce flood risk by mapping flood hazard and risk areas, such as the Directive 2007/60/EC on the Assessment and Management of Flood Risk. This Directive looks to identify regions where flood management strategies should be prioritized. Despite this holistic approach, flaws connected to the consideration of sediment transport and morphological changes in rivers exist, leading to potential underestimations of the impact of floods affecting active watercourses or areas subjected to frequent morphological changes.ResultsBy discussing six examples related to European lowland and mountain watercourses affected by significant floods in the last 20 years, the present mini-review aims to provide additional evidence on the need for a rethinking of flood risk mapping, moving from a “clear water” perspective to a more integrated approach, where the interactions between all the fluvial components (water, sediment, biota, and humans) are adequately considered.ConclusionsThe examples reported here show the importance of considering sediment and wood in flood risk management, suggesting the need for integrating flood-related studies with other disciplines like geomorphology and ecohydrology.