The Use of Individual and Societal Risk Criteria Within the Dutch Flood Safety Policy—Nationwide Estimates of Societal Risk and Policy Applications

The Dutch government is in the process of revising its flood safety policy. The current safety standards for flood defenses in the Netherlands are largely based on the outcomes of cost‐benefit analyses. Loss of life has not been considered separately in the choice for current standards. This article presents the results of a research project that evaluated the potential roles of two risk metrics, individual and societal risk, to support decision making about new flood safety standards. These risk metrics are already used in the Dutch major hazards policy for the evaluation of risks to the public. Individual risk concerns the annual probability of death of a person. Societal risk concerns the probability of an event with many fatalities. Technical aspects of the use of individual and societal risk metrics in flood risk assessments as well as policy implications are discussed. Preliminary estimates of nationwide levels of societal risk are presented. Societal risk levels appear relatively high in the southwestern part of the country where densely populated dike rings are threatened by a combination of river and coastal floods. It was found that cumulation, the simultaneous flooding of multiple dike rings during a single flood event, has significant impact on the national level of societal risk. Options for the application of the individual and societal risk in the new flood safety policy are presented and discussed.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Quantifying Flood Risks in the Netherlands

The Flood Risk in the Netherlands project (Dutch acronym: VNK2) is a large‐scale probabilistic risk assessment for all major levee systems in the Netherlands. This article provides an overview of the methods and techniques used in the VNK2 project. It also discusses two examples that illustrate the potential of quantitative flood risk assessments such as VNK2 to improve flood risk management processes: (i) informing political debates about the risks of flooding and the effectiveness of risk management actions, and (ii) (re)directing research efforts towards important sources of uncertainty.     

Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood‐prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov‐Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat‐5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.     

Flood Risk Assessment and Regionalization from Past and Future Perspectives at Basin Scale

Flooding is a major natural disaster that has brought tremendous losses to mankind throughout the ages. Even so, floods can be controlled by appropriate measures to minimize loss and damage. Flood risk assessment is an essential analytic step in preventing floods and reducing losses. Identifying previous flood risk and predicting future features are conducive to understanding the changing patterns and laws of flood risk. Taking the Dongjiang River basin as a study case, we assessed and regionalized flood risk in 1990, 2000, and 2010 from the past perspective and explored dynamic expansion during 1990–2010. Then, we projected land-use type, population, and gross domestic product in 2030 and 2050 and finally assessed and regionalized the risk from a future perspective. Results show that areas with very high risk accounted for 14.98–18.08% during 1990–2010; approximately 13.90% areas of the basin transformed from lower-level risk to higher-level risk whereas 9.07% fell from a higher level to a lower level during the period. For the future scenario, areas with very high and high risk in 2030 and 2050 are expected to account for 21.55% and 24.84%, respectively. Generally, our study can better identify changes in flood risk at a spatial scale and reveal the dynamic evolution rule, which provides a synthetical means of flood prevention and reduction, flood insurance, urban planning, and water resource management in the future under global climate change, especially for developing or high-speed urbanization regions.     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

Adapting to Climate: A Case Study on Riverine Flood Risks in the Netherlands

Climate change may well lead to an increased risk of river floods in the Netherlands. However, the impacts of changes in water management on river floods are larger, either enhancing or reducing flood risks. Therefore, the abilities of water-management authorities to learn that climate and river flows are changing, and to recognize and act upon the implications, are of crucial importance. At the same time, water-management authorities respond to other trends, such as the democratization of decision making, which alter their ability to react to climate change. These complex interactions are illustrated with changes in river flood risk management for the Rhine and the Meuse in the Netherlands over the last 50 years. A scenario study is used to seek insight into the question of whether current water-management institutions and their likely successors are capable of dealing with plausible future flood risks. The scenarios show that new and major infrastructure is needed to keep flood risks at their current level. Such a structural solution to future flood risks is feasible, but requires considerable political will and institutional reform, both for planning and implementation. It is unlikely that reform will be fast enough or the will strong enough.     

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.     

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.     

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.     

Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

In this article, we propose an integrated direct and indirect flood risk model for small‐ and large‐scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb‐Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input‐output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high‐ and low‐probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low‐probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high‐probability events are qualitatively different from low‐probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high‐probability and low‐probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.     

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.     

Flood Risk Management: US Army Corps of Engineers and Layperson Perceptions

Recent severe storm experiences in the U.S. Gulf Coast illustrate the importance of an integrated approach to flood preparedness planning that harmonizes stakeholder and agency efforts. Risk management decisions that are informed by and address decision maker and stakeholder risk perceptions and behavior are essential for effective risk management policy. A literature review and two expert models/mental models studies were undertaken to identify areas of importance in the flood risk management process for layperson, non‐USACE‐expert, and two USACE‐expert groups. In characterizing and mapping stakeholder beliefs about risks in the literature onto current risk management practice, recommendations for accommodating and changing stakeholder perceptions of flood risks and their management are identified. Needs of the U.S. Army Corps of Engineers (USACE) flood preparedness and response program are discussed in the context of flood risk mental models.     

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.     

Integrated Uncertainty Analysis for Ambient Pollutant Health Risk Assessment: A Case Study of Ozone Mortality Risk

The U.S. Environmental Protection Agency (EPA) uses health risk assessment to help inform its decisions in setting national ambient air quality standards (NAAQS). EPA's standard approach is to make epidemiologically‐based risk estimates based on a single statistical model selected from the scientific literature, called the “core” model. The uncertainty presented for “core” risk estimates reflects only the statistical uncertainty associated with that one model's concentration‐response function parameter estimate(s). However, epidemiologically‐based risk estimates are also subject to “model uncertainty,” which is a lack of knowledge about which of many plausible model specifications and data sets best reflects the true relationship between health and ambient pollutant concentrations. In 2002, a National Academies of Sciences (NAS) committee recommended that model uncertainty be integrated into EPA's standard risk analysis approach. This article discusses how model uncertainty can be taken into account with an integrated uncertainty analysis (IUA) of health risk estimates. It provides an illustrative numerical example based on risk of premature death from respiratory mortality due to long‐term exposures to ambient ozone, which is a health risk considered in the 2015 ozone NAAQS decision. This example demonstrates that use of IUA to quantitatively incorporate key model uncertainties into risk estimates produces a substantially altered understanding of the potential public health gain of a NAAQS policy decision, and that IUA can also produce more helpful insights to guide that decision, such as evidence of decreasing incremental health gains from progressive tightening of a NAAQS.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

Flood Risk Management: Exploring the Impacts of the Community Rating System Program on Poverty and Income Inequality

Flooding remains a major problem for the United States, causing numerous deaths and damaging countless properties. To reduce the impact of flooding on communities, the U.S. government established the Community Rating System (CRS) in 1990 to reduce flood damages by incentivizing communities to engage in flood risk management initiatives that surpass those required by the National Flood Insurance Program. In return, communities enjoy discounted flood insurance premiums. Despite the fact that the CRS raises concerns about the potential for unevenly distributed impacts across different income groups, no study has examined the equity implications of the CRS. This study thus investigates the possibility of unintended consequences of the CRS by answering the question: What is the effect of the CRS on poverty and income inequality? Understanding the impacts of the CRS on poverty and income inequality is useful in fully assessing the unintended consequences of the CRS. The study estimates four fixed‐effects regression models using a panel data set of neighborhood‐level observations from 1970 to 2010. The results indicate that median incomes are lower in CRS communities, but rise in floodplains. Also, the CRS attracts poor residents, but relocates them away from floodplains. Additionally, the CRS attracts top earners, including in floodplains. Finally, the CRS encourages income inequality, but discourages income inequality in floodplains. A better understanding of these unintended consequences of the CRS on poverty and income inequality can help to improve the design and performance of the CRS and, ultimately, increase community resilience to flood disasters.     

Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates

In flood risk analysis, limitations in the multivariate statistical models adopted to model the hydraulic load have restricted the probability of a defense suffering structural failure to be expressed conditionally on a single hydraulic loading variable. This is an issue at the coastal level where multiple loadings act on defenses with the exact combination of loadings dictating their failure probabilities. Recently, a methodology containing a multivariate statistical model with the flexibility to robustly capture the dependence structure between the individual loadings was used to derive extreme nearshore loading conditions. Its adoption will permit the incorporation of more precise representations of a structure's vulnerability in future analyses. In this article, a fragility representation of a shingle beach, where the failure probability is expressed over a three‐dimensional loading parameter space—water level, wave height, and period—is derived at two localities. Within the approach, a Gaussian copula is used to capture any dependencies between the simplified geometric parameters of a beach's shape. Beach profiles are simulated from the copula and the failure probability, given the hydraulic load, determined by the reformulated Bradbury barrier inertia parameter model. At one site, substantial differences in the annual failure probability distribution are observed between the new and existing approaches. At the other, the beach only becomes vulnerable after a significant reduction of the crest height with its mean annual failure probability close to that presently predicted. It is concluded that further application of multivariate approaches is likely to yield more effective flood risk management.     

The BSE Risk of Processing Meat and Bone Meal in Nonruminant Feed: A Quantitative Assessment for the Netherlands

The total ban on use of meat and bone meal (MBM) in livestock feed has been very successful in reducing bovine spongiform encephalopathy (BSE) spread, but also implies a waste of high-quality proteins resulting in economic and ecological loss. Now that the BSE epidemic is fading out, a partial lifting of the MBM ban might be considered. The objective of this study was to assess the BSE risk for the Netherlands if MBM derived from animals fit for human consumption, i.e., category 3 MBM, would be used in nonruminant feed. A stochastic simulation model was constructed that calculates (1) the probability that infectivity of undetected BSE-infected cows ends up with calves and (2) the quantity of infectivity ( Q_inf ) consumed by calves in case of such an incident. Three pathways were considered via which infectivity can reach cattle: (1) cross-contamination in the feed mill, (2) cross-contamination on the primary farm, and (3) pasture contamination. Model calculations indicate that the overall probability that infectivity ends up with calves is 3.2%. In most such incidents the Q_inf is extremely small (median = 6.5 × 10⁻¹² ID₅₀; mean = 1.8 × 10⁻⁴ ID₅₀), corresponding to an average probability of 1.3 × 10⁻⁴ that an incident results in ≥1 new BSE infections. Cross-contamination in the feed mill is the most risky pathway. Combining model results with Dutch BSE prevalence estimates for the coming years, it can be concluded that the BSE risk of using category 3 MBM derived from Dutch cattle in nonruminant feed is very low.     

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.