Mapping Socioeconomic Exposure for Flood Risk Assessment in Italy

Detailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.     

A Probabilistic Analysis of Surface Water Flood Risk in London

Flooding in urban areas during heavy rainfall, often characterized by short duration and high‐intensity events, is known as “surface water flooding.” Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.     

A Sensitivity Analysis of the Social Vulnerability Index

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003) , examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty-four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.     

洪水灾害风险分析的系统理论

从系统论的观点出发 ,提出了洪水灾害复杂大系统的概念 ,并以这一概念为基础 ,探讨了洪水灾害风险特征及洪水灾害风险评价的基本内容 ,提出并系统地阐述了以洪水危险性分析、承灾体易损性分析和洪水灾害灾情评估为核心内容的洪水灾害风险分析的系统理论     

Out of Sight,Out of Mind? Geographic and Social Predictors of Flood Risk Awareness

The persistent gap in flood risk awareness in Canada, and elsewhere in North America, is a continual source of worry for researchers and emergency managers; many people living in at‐risk places are simply unaware of risks and of their proximity to hazards. This study seeks to understand which residents were aware of flood risk, using unique representative survey data of Calgary residents living in the city's flood‐prone neighborhoods collected after the devastating and costly 2013 Southern Alberta Flood. The article uses logistic regression models to analyze which residents were aware of risk to their homes. Findings indicate that, in addition to various demographic predictors, many of the geographic predictors (including the elevation of one's home relative to the river) are significant predictors of awareness. Having a direct sight line to one of Calgary's two rivers is also a significant predictor in some of the models, suggesting that the visibility of hazards matters for flood risk perception, although this effect fades when many of the geographic predictors are added. Finally, the models indicate that several variables related to local, neighborhood‐based social networks are significant as well. These findings reveal that both physical surroundings and social context are important for understanding risk awareness. The article concludes by discussing the relevance for social science research on disasters and hazards, as well as for planners and emergency managers.     

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.     

Flood Risk,Flood Mitigation,and Location Choice: Evaluating the National Flood Insurance Program's Community Rating System

Climate change is expected to worsen the negative effects of natural disasters like floods. The negative impacts, however, can be mitigated by individuals’ adjustments through migration and relocation behaviors. Previous literature has identified flood risk as one significant driver in relocation decisions, but no prior study examines the effect of the National Flood Insurance Program's voluntary program—the Community Rating System (CRS)—on residential location choice. This article fills this gap and tests the hypothesis that flood risk and the CRS‐creditable flood control activities affect residential location choices. We employ a two‐stage sorting model to empirically estimate the effects. In the first stage, individuals’ risk perception and preference heterogeneity for the CRS activities are considered, while mean effects of flood risk and the CRS activities are estimated in the second stage. We then estimate heterogeneous marginal willingness to pay (WTP) for the CRS activities by category. Results show that age, ethnicity and race, educational attainment, and prior exposure to risk explain risk perception. We find significant values for the CRS‐creditable mitigation activities, which provides empirical evidence for the benefits associated with the program. The marginal WTP for an additional credit point earned for public information activities, including hazard disclosure, is found to be the highest. Results also suggest that water amenities dominate flood risk. Thus, high amenity values may increase exposure to flood risk, and flood mitigation projects should be strategized in coastal regions accordingly.     

Socioeconomic Vulnerability and Adaptation to Environmental Risk: A Case Study of Climate Change and Flooding in Bangladesh

In this article we investigate the complex relationship between environmental risk, poverty, and vulnerability in a case study carried out in one of the poorest and most flood-prone countries in the world, focusing on household and community vulnerability and adaptive coping mechanisms. Based upon the steadily growing amount of literature in this field we develop and test our own analytical model. In a large-scale household survey carried out in southeast Bangladesh, we ask almost 700 floodplain residents living without any flood protection along the River Meghna about their flood risk exposure, flood problems, flood damage, and coping mechanisms. Novel in our study is the explicit testing of the effectiveness of adaptive coping strategies to reduce flood damage costs. We show that, households with lower income and less access to productive natural assets face higher exposure to risk of flooding. Disparity in income and asset distribution at community level furthermore tends to be higher at higher risk exposure levels, implying that individually vulnerable households are also collectively more vulnerable. Regarding the identification of coping mechanisms to deal with flood events, we look at both the ex ante household level preparedness for flood events and the ex post availability of community-level support and disaster relief. We find somewhat paradoxically that the people that face the highest risk of flooding are the least well prepared, both in terms of household-level ex ante preparedness and community-level ex post flood relief.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.     

Individual Actual or Perceived Property Flood Risk: Did it Predict Evacuation from Hurricane Isabel in North Carolina, 2003?

Individual perception of risk has consistently been considered an important determinant of hurricane evacuation in published studies and reviews. Adequate risk assessment is informed by environmental and social cues, as well as evacuation intentions and past disaster experience. This cross‐sectional study measured perceived flood risk of 570 residents of three coastal North Carolina counties, compared their perception with actual risk determined by updated flood plain maps, and determined if either was associated with evacuation from Hurricane Isabel in 2003. Census blocks were stratified by flood zone and 30 census blocks were randomly selected from each flood zone. Seven interviews were conducted at random locations within selected blocks. Bivariate and multivariable analyses were conducted to produce crude and adjusted risk differences. Neither the designated flood zone of the parcel where the home was located nor the residents' perceived flood risk was associated with evacuation from Hurricane Isabel in the bivariate analysis. In the multivariable analysis, intention to evacuate and home type were important confounders of the association between actual risk and evacuation. The belief that one is at high risk of property damage or injury is important in evacuation decision making. However, in this study, while coastal residents' perceived risk of flooding was correlated with their actual flood risk, neither was associated with evacuation. These findings provide important opportunities for education and intervention by policymakers and authorities to improve hurricane evacuation rates and raise flood risk awareness.     

Quantifying Riverine and Storm‐Surge Flood Risk by Single‐Family Residence: Application to Texas

The development of catastrophe models in recent years allows for assessment of the flood hazard much more effectively than when the federally run National Flood Insurance Program (NFIP) was created in 1968. We propose and then demonstrate a methodological approach to determine pure premiums based on the entire distribution of possible flood events. We apply hazard, exposure, and vulnerability analyses to a sample of 300,000 single‐family residences in two counties in Texas (Travis and Galveston) using state‐of‐the‐art flood catastrophe models. Even in zones of similar flood risk classification by FEMA there is substantial variation in exposure between coastal and inland flood risk. For instance, homes in the designated moderate‐risk X500/B zones in Galveston are exposed to a flood risk on average 2.5 times greater than residences in X500/B zones in Travis. The results also show very similar average annual loss (corrected for exposure) for a number of residences despite their being in different FEMA flood zones. We also find significant storm‐surge exposure outside of the FEMA designated storm‐surge risk zones. Taken together these findings highlight the importance of a microanalysis of flood exposure. The process of aggregating risk at a flood zone level—as currently undertaken by FEMA—provides a false sense of uniformity. As our analysis indicates, the technology to delineate the flood risks exists today.     

An Agent‐Based Model of Evolving Community Flood Risk

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.     

Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization

It is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state‐of‐the‐art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.     

Disaster Risk Management Policies and the Measurement of Resilience for Philippine Regions

How can a government prioritize disaster risk management policies across regions and types of interventions? Using an economic model to assess welfare risk and resilience to disasters, this article systematically tackles the questions: (1) How much asset and welfare risks does each region in the Philippines face from riverine flood disasters? (2) How resilient is each region to riverine flood disasters? (3) What are, per region, the possible interventions to strengthen resilience to riverine flood disasters and what will be their measured benefit? We study the regions of the Philippines to demonstrate the channels through which macroeconomic asset and output losses from disasters translate to consumption and welfare losses at the micro-economic level. Apart from the regional prioritizations, we identify a menu of policy options ranked according to their level of effectiveness in increasing resilience and reducing welfare risk from riverine floods. The ranking of priorities varies for different regions when their level of expected value at risk is different. This suggests that there are region-specific conditions and drivers that need to be integrated into considerations and policy decisions, so that these are effectively addressed.     

Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Does federal flood hazard mitigation assistance affect community rating system participation?

With the inexorable march of climate change, increased flooding is inevitable. Understanding the feedback between federal flood mitigation policies and the ways in which local governments build flood resilience is a significant gap in the literature. In particular, the effect that federal flood mitigation grants have on the intensity of local flood mitigation is nonexistent. This work measures flood risk mitigation by using the level of participation in FEMA's Community Rating System (CRS). Communities that participate in the CRS and undertake mitigation are awarded points; more points imply a higher level of participation. Since its inception in 1990, CRS communities have received considerably more federal pre-disaster flood mitigation grants compared to non-CRS communities. This study assesses the effect of federal pre-disaster flood mitigation grants on the level of participation in the CRS program. We use data on Hazard Mitigation Assistance programs and CRS participation data between 2010 and 2015. We link these data to flood risk and socioeconomic information. Our results indicate (i) federal pre-disaster flood mitigation grants do not appear to significantly influence the level of CRS participation, (ii) the effect of flood risk and socioeconomic factors on the level of CRS participation are mixed, and (iii) the current level of CRS participation is influenced by the previous level of CRS participation, which is not tied to federal pre-disaster flood mitigation grant. These findings add to the growing discussions on the drivers and barriers of local flood risk mitigation.     

Wildfire Risk Transmission in the Colorado Front Range,USA

Wildfires are a global phenomenon that in some circumstances can result in human casualties, economic loss, and ecosystem service degradation. In this article we spatially identify wildfire risk transmission pathways and locate the areas of highest exposure of human populations to wildland fires under severe, but not uncommon, weather events. We quantify varying levels of exposure in terms of population potentially affected and tie the exposure back to the spatial source of the risk for the Front Range of Colorado, USA. We use probabilistic fire simulation modeling to address where fire ignitions are most likely to cause the highest impact to human communities, and to explore the role that various landowners play in that transmission of risk. Our results indicated that, given an ignition and the right fire weather conditions, large areas along the Front Range in Colorado could be exposed to wildfires with high potential to impact human populations, and that overall private ignitions have the potential to impact more people than federal ignitions. These results can be used to identify high‐priority areas for wildfire risk mitigation using various mitigation tools.     

The Emergence of Flood Insurance in Canada: Navigating Institutional Uncertainty

Flood insurance has remained unavailable in Canada based on an assessment that it lacks economic viability. In response to Canada's costliest flood event to date in 2013, the Canadian insurance industry has started to develop a framework to expand existing property insurance to cover flood damage. Research on flood insurance has overlooked why and how insurance systems transition to expand insurance coverage without evidence of economic viability. This article will address this gap through a case study on the emergence of flood insurance in Canada, and the approach to its expansion. Between 2013 and 2016, insurance industry officials representing over 60% of premiums collected in Canada were interviewed. These interviews revealed that flood insurance is being expanded in response to institutional pressure, specifically external stakeholder expectations that the insurance industry will adopt a stronger role in managing flood risk through coverage of flood damage. Further evidence of this finding is explored by assessing the emergence of a unique flood insurance model that involves a risk‐adjusted and optional product along with an expansion of government policy supporting flood risk mitigation. This approach attempts to balance industry concerns about economic viability with institutional pressure to reduce flood risk through insurance. This analysis builds on existing research by providing the first scholarly analysis of flood insurance in Canada, important “empirical” teeth to existing conceptual analysis on the availability of flood insurance, and the influence of institutional factors on risk analysis within the insurance sector.     

Financial Instruments for Mitigation of Flood Risks: The Case of Florence

This article analyzes the mechanisms and effects of innovative financial instruments that a central public administration (CPA) may adopt to minimize the flood risk in particularly exposed regions. The pattern we suggest assumes that in risky areas the CPA can issue two financial instruments, called project options and CAT‐bonds, producing a dynamic interaction among three types of agents: the CPA itself, the local public administrations, and private investors. We explore the possible scenarios of such interaction and the conditions under which the CPA's goal of maximal risk reduction is attained. This pattern is proposed for flood risk mitigation in the city of Florence, where the model dynamics are tested assuming parameters obtained from engineering studies.