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.     

An Integrated Approach for Assessing the Impact of Large-Scale Future Floods on a Highway Transport System

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.     

Mortality Patterns of Hydro‐Geomorphologic Disasters

Social impacts caused by floods and landslides in Portugal in the period of 1865–2010 are gathered in the DISASTER database. This database contains 1,902 hydro‐geomorphologic cases that caused 1,248 fatalities (81% and 19% associated with floods and landslides, respectively). The use of the DISASTER database allowed for: (i) the analysis of the frequency and the temporal evolution of fatal floods and landslides; (ii) the analysis of the spatio‐temporal distribution of fatalities; (iii) the identification of the most deadly flood and landside types; (iv) the verification of gender tendencies in mortalities; and (v) the evaluation of individual and societal risk. The highest number of flood and landslide cases and related mortalities occurred in the period of 1935–1969. After this period, the number of flood and landslide mortalities decreased, although landslide fatalities remained higher than those registered in the period of 1865–1934. The occurrence of flood fatalities was widespread in the country, with an important cluster in the Lisbon region and in the Tagus valley, while fatalities caused by landslides mainly occurred in the north of the Tagus valley. Flash floods caused the majority of fatalities associated with floods, while falls and flows were responsible for the highest number of fatalities associated with landslides. Males were found to have the highest frequency of fatalities. In the case of floods, the majority of fatalities were found both outdoors and inside of buildings in rural areas while fatalities inside buildings were dominant in landslide cases, mostly in rural areas.     

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.     

Examining the 100‐Year Floodplain as a Metric of Risk,Loss, and Household Adjustment

An understudied, but central aspect in understanding flood impacts is the way we conceptualize, identify, and delineate risk. The 100‐year floodplain is the longstanding metric in the United States for determining and acting upon the possibility of an area being inundated. This spatial delineation guides local planning and development decisions, triggers insurance purchases and other household adjustments, and serves as the fundamental indicator for whether it is safe to build a structure on a particular site. However, increasing evidence suggests that the 100‐year floodplain is neither accurate nor sufficient in guiding communities and household decisions to mitigate the adverse economic impacts of floods. In this perspective, we examine the effectiveness of the 100‐year floodplain as an appropriate marker of risk. First, we review existing studies on location and flood damage. Next, we apply these concepts to repetitive flood losses data in Harris County, Texas. We conclude that the apparent inability of the floodplain designation to effectively capture the likelihood of property damage and potential loss of human life in coastal areas has left potentially millions of property owners unaware of the flood risk and unprepared to mitigate their adverse impacts. Relying on traditional 100‐year floodplain boundaries, local decision makers are hampered in their ability to ensure community development occurs in a resilient manner. Finally, we set forth an agenda for future research to better capture the conditions associated with flood risk and account for the large percentage of damage outside the designated floodplain.     

Prevention,Adaptation, and Threat Denial: Flooding Experiences in the Netherlands

Delta areas such as the Netherlands are more and more at risk of future flooding due to global climate change. Motivating residents living in flood-prone areas to effectively cope with local floods may lead to minimization of material losses and loss of life. The aim of this research was to investigate whether the extent to which residents had been exposed to flooding in the past was a key factor in motivating residents to effectively cope with future flooding. We also focused on the psychological variables that mediated this relationship. We conducted a survey (N = 516) among flood victims and nonvictims. We assessed subjective experiences due to past flooding, affective and cognitive appraisals, and coping responses. Results show that victims reported stronger emotions (negative and positive), and the receipt of more social support due to past flooding than did nonvictims. Moreover, victims worry more about future flooding, perceive themselves as more vulnerable to future flooding, perceive the consequences of future flooding as more severe, and have stronger intentions to take adaptive actions in the future than nonvictims. Structural equation modeling reveals that the latter effect was fully mediated by specific experiences and appraisals. Insights into factors and processes that have the potential to motivate residents to effectively cope with future floods may prove helpful in developing interventions to inform residents how to act effectively in case of an imminent flood.     

Comparing the Economic Impact of Natural Disasters Generated by Different Input–Output Models: An Application to the 2007 Chehalis River Flood (WA)

Due to the concentration of assets in disaster‐prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single‐region input–output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input–output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.     

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.     

Geographic location or experience: Using PRISM to understand how people seek flood risk information

Flooding is increasing worldwide, and with climate change, people need help understanding these changing conditions and that their flood risk may also change. This study extends the planned risk information seeking model (PRISM) into the flood risk domain and examines the antecedents that explain flood risk information seeking behavior. Using a survey reflective of the population in the state of Texas (N = 1079), this study includes an operationalization of risk perception specific to the complexity of floods and explores two key moderators in the PRISM model. Findings suggest that using PRISM to elaborate flood risk information seeking behaviors explains 48% of the variance in information seeking intent and 37% of the variance in affective risk perception. Using multigroup modeling, the findings also reveal that simply living in an area at high risk for floods does not significantly impact any relationships in the model. However, having experience with flooding increases the strength of risk perception paths—in particular, perceived probability of flood risk—and better explains flood risk information seeking. Suggestions for how to use communication to influence risk perceptions and information seeking, as well as future directions for research, are also discussed.     

Flood Protection Diversification to Reduce Probabilities of Extreme Losses

Recent catastrophic losses because of floods require developing resilient approaches to flood risk protection. This article assesses how diversification of a system of coastal protections might decrease the probabilities of extreme flood losses. The study compares the performance of portfolios each consisting of four types of flood protection assets in a large region of dike rings. A parametric analysis suggests conditions in which diversifications of the types of included flood protection assets decrease extreme flood losses. Increased return periods of extreme losses are associated with portfolios where the asset types have low correlations of economic risk. The effort highlights the importance of understanding correlations across asset types in planning for large‐scale flood protection. It allows explicit integration of climate change scenarios in developing flood mitigation strategy.     

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.     

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.     

Benefits of and strategies to update premium rates in the US National Flood Insurance Program under climate change

The United States’ National Flood Insurance Program (NFIP) has accumulated over $20 billion in debt to the US Treasury since 2005, partly due to discounted premiums on homes in flood-prone areas. To address this issue, FEMA introduced Risk Rating 2.0 in October 2021, which is able to assess and charge more accurate and equitable rates to homeowners. However, rates must be continually updated to account for increasing flood damage caused by sea level rise and more intense hurricanes due to climate change. This study proposes a strategy to adopt updated premium rates that account for climate change effects and address affordability and risk mitigation issues with a means-tested voucher program. The strategy is tested in a coastal community, Ortley Beach, NJ, by projecting its future flood risk under sea level rise and storm intensification. Compared with using static rates for all the properties in Ortley Beach, the proposed strategy is shown to reduce the NFIP's potential losses to the community from 2020 to 2050 by half (from $4.6 million to $2.3 million), improve the community's flood resistance, and address affordability concerns. Sensitivity analysis of varying incomes, loan interest rates, and conditions for a voucher indicates that the strategy is feasible and effective under a wide range of scenarios. Thus, the proposed strategy can be applied to various communities along the US coastline as an effective way of updating risk-based premiums while addressing affordability and resilience concerns.     

Public Perception of the Risks of Floods: Implications for Communication

Floods in the U.S. kill an average of 162 people each year and cause $3.4 billion in property damage. Flood control programs have been successful in lowering, but not eliminating, the risks to lives and property. Since the late 1960s, the federal government has emphasized flood insurance as a primary tool for improving location and flood-proofing decisions, as well as for reimbursing flood losses. Since only 12.7% of houses in flood plain areas are covered by flood insurance, the program has been ineffective. We interviewed people living in three communities that had recently been flooded. Most people had little knowledge of the cause of floods or what could be done to prevent damage. People who work and who are better educated know more and are more likely to have flood insurance. Current government publications about flood risks are not likely to be understood by those at risk. There is little effective communication about the nature and magnitude of the risks and what individuals can do to protect their lives and property and lower their financial risks. The risk management program should both emphasize communication and enforcement of the current law requiring people at risk who hold federally funded loans to be insured.     

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.     

Risk Analysis and Management of Dam Safety

The current safety criteria for a high hazard dam focus on protecting the dam during a large flood. While protecting the dam does help to protect downstream people and property, the two objectives are not the same. Instead, the criteria should focus on lowering property damage (including damage to the dam) and preventing flood deaths. High hazard dams must survive a design flood in the current safety criteria. However, experts don't agree on the size of the peak flow that meets this criteria. Statistical hydrologists have proposed an alternative to using professional judgment to specify the design flood. Unfortunately, peak flow distributions cannot be estimated with confidence for extreme floods given available data. A major safety goal is to prevent deaths from floods. Preventing deaths is a major reason for constructing the spillway to handle extreme floods so that the dam doesn't fail due to overtopping. However, even if the dam doesn't fail, the spilled floods could cause many deaths. A better approach is to warn people to get them out of harm's way if a flood is coming. Retrofitting existing dams that could pass a “probable maximum flood” (PMF) when built is almost never a good use of funds. Instead, funds would be spent better by focusing on preventing damage from small floods, lowering the damage from medium-sized floods, and warning people in the event of a flood that could pose risks to life.     

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent-based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.     

Projections of Future Extreme Weather Losses Under Changes in Climate and Exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state‐of‐the‐art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra‐tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.     

Insurance,Public Assistance,and Household Flood Risk Reduction: A Comparative Study of Austria,England, and Romania

In light of increasing losses from floods, many researchers and policymakers are looking for ways to encourage flood risk reduction among communities, business, and households. In this study, we investigate risk‐reduction behavior at the household level in three European Union Member States with fundamentally different insurance and compensation schemes. We try to understand if and how insurance and public assistance influence private risk‐reduction behavior. Data were collected using a telephone survey (n = 1,849) of household decisionmakers in flood‐prone areas. We show that insurance overall is positively associated with private risk‐reduction behavior. Warranties, premium discounts, and information provision with respect to risk reduction may be an explanation for this positive relationship in the case of structural measures. Public incentives for risk‐reduction measures by means of financial and in‐kind support, and particularly through the provision of information, are also associated with enhancing risk reduction. In this study, public compensation is not negatively associated with private risk‐reduction behavior. This does not disprove such a relationship, but the negative effect may be mitigated by factors related to respondents' capacity to implement measures or social norms that were not included in the analysis. The data suggest that large‐scale flood protection infrastructure creates a sense of security that is associated with a lower level of preparedness. Across the board there is ample room to improve both public and private policies to provide effective incentives for household‐level risk reduction.     

Insurability and Mitigation of Flood Losses in Private Households in Germany

In Germany, flood insurance is provided by private insurers as a supplement to building or contents insurance. This article presents the results of a survey of insurance companies with regard to eligibility conditions for flood insurance changes after August 2002, when a severe flood caused 1.8 billion euro of insured losses in the Elbe and the Danube catchment areas, and the general role of insurance in flood risk management in Germany. Besides insurance coverage, governmental funding and public donations played an important role in loss compensation after the August 2002 flood. Therefore, this article also analyzes flood loss compensation, risk awareness, and mitigation in insured and uninsured private households. Insured households received loss compensation earlier. They also showed slightly better risk awareness and mitigation strategies. Appropriate incentives should be combined with flood insurance in order to strengthen future private flood loss mitigation. However, there is some evidence that the surveyed insurance companies do little to encourage precautionary measures. To overcome this problem, flood hazards and mitigation strategies should be better communicated to both insurance companies and property owners.