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Using a natural experiment created by the 1982 Coastal Barrier Resources Act (CBRA), we measure the extent to which insurance premiums vary between private and publicly backed flood insurance policies. The CBRA resulted in homeowners living in neighboring housing markets in which some have access to the federally backed National Flood Insurance Program (NFIP) and others do not. Flood risks and other features of the neighborhoods are otherwise indistinguishable. Those without access to publicly backed insurance must purchase private insurance if they desire coverage. We compare insurance premiums and uptake in the two markets and find that premiums for private coverage are significantly higher than public rates (after controlling for other factors in a reduced-form regression), implying a subsidy by NFIP. We also find a much lower uptake of insurance in the areas without federally backed insurance. Our results are based on a mail survey of residents in two US coastal communities with a 50% response rate: North Bethany Beach, Delaware, and North Topsail Beach, North Carolina. We also present results related to perceptions of flood hazards, of being under- or overinsured, and measures taken to mitigate flood damage. [ABSTRACT FROM AUTHOR]

In the United States, requirements for flood insurance, development restrictions, and federal buyout program eligibility rely on regulatory designation of hazardous zones, i.e., inside or outside the 100-year floodplain. Extensive research has investigated floodplain development patterns across different geographies, times, and scales, yet the impacts, and potential unintended consequences, of floodplain policies beyond their boundaries have not been empirically examined. We posit that the regulatory 100-year floodplain presents a "safe development paradox", whereby attempts to reduce flood risk paradoxically intensifies it by promoting development in and near flood-prone areas. We conducted the first comprehensive national assessment of historical and future development patterns related to the regulatory 100-year floodplain, examining the spatial distribution of developed land within increasingly distant 250-m zones from floodplain boundaries. We found a disproportionate concentration of developed land (24% or 89,080 km2 of developed land by 2019) in zones immediately adjacent to the floodplain, a trend observed at the national, state, and county levels. Nationwide projections suggest that approximately 22% of all anticipated growth from 2020 to 2060 is likely to occur within 250 m from the 100-year floodplain, equivalent to 6,900 km2 of new development (SD = 2,842 km2). Understanding and anticipating the influence of flood management policies on current and future land use decisions is crucial for effective planning and mitigation strategies. [ABSTRACT FROM AUTHOR]

This study uses panel data for the 50 states of the U.S. plus the District of Columbia from 1998 to 2018 to examine pricing effects of climate change on housing and insurance markets. We apply the mean group estimator and separate short-run from long-run impacts. The results indicate long-run relationships between the housing/insurance market and climate change, although no significant impacts of climate change on these markets appear in the long-run. In the short-run, we find significant negative impacts of fire occurrences on housing prices and mortgage rates, and significant positive impacts of flooding on insurance markets. Further analysis of states exposed to severe climate risks indicates no short- or long-run impacts of climate change. Thus, while realtors and insurance providers may price some climate-risks into mortgage and insurance in the short-run, they do not in the long-run. [ABSTRACT FROM AUTHOR]

The complexity of flood risk models is intrinsically linked to a variety of sources of uncertainty (hydrology, hydraulics, exposed assets, vulnerability, coping capacity, etc.) that affect the accuracy and reliability of the analyses. Estimating the uncertainties associated with the different components allows us to be more confident in the risk values on the ground, thus providing a more reliable assessment for investment, insurance and flood risk management purposes. In this study, we investigate the flood risk of the entire Central Apennines District (CAD) in Central Italy using the laRgE SCale inUndation modEl – Flood Risk (RESCUE-FR), focusing on the interaction between the uncertainty in the hydraulic Manning parameter and the risk variability. We assess the coherence between the quantile flood risk maps generated by our model and the official risk maps provided by the Central Apennines District Authority (CAD Authority) and focusing on three specific zones within the CAD region. Thus, RESCUE-FR is used to estimate the expected annual damage (EAD) and the expected annual population affected (EAPA) across the CAD region and to conduct a comprehensive uncertainty analysis. The latter provides a range of confidence of risk estimation that is essential for identifying vulnerable areas and guiding effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Subsidized insurance against extreme weather events improves affordability among households in at‐risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at‐risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood‐prone properties. This fully offsets the negative direct effects of flooding on property prices, with high‐income areas and high‐value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution. [ABSTRACT FROM AUTHOR]

In recent years, the Federal Emergency Management Agency (FEMA) has sought to enforce home elevation policies more strictly across coastal Southeast Louisiana. These policies are meant to protect homes from flooding resulting from tropical storms. Such policies, however, are widely reviled by coastal community residents. This paper presents the results of ethnographic research conducted in Lower Plaquemines Parish investigating the reasons why coastal community residents hold such antipathy towards home elevation requirements. Above all, we find that there is enormous anxiety and deep resentment concerning the impacts of home elevation requirements. We show that such mandates are fundamentally at odds with vernacular architectural adaptations—especially the utilisation of trailers and other forms of modular housing—and they interfere with key features of residential mobility practices. Furthermore, many residents perceive FEMA home elevation policies as an element of a broader government conspiracy to depopulate the coast and drive out coastal community residents. We argue that the FEMA policies would be better informed and more effective based on a thorough ethnographic consideration of both the socioeconomic context of coastal communities and vernacular architectural strategies as they relate to issues of risk reduction and disaster recovery. Rather than forcing coastal communities into a one-size-fits-all risk reduction policy, we argue for a more flexible and locally informed approach building on existing cultural practices, social systems, and inherited knowledge. [ABSTRACT FROM AUTHOR]

The 27th Conference of the Parties to the United Nations Framework Convention on Climate Change in Egypt made a historical decision on disaster loss and damage for vulnerable countries. Even though parties agreed to salvage vulnerable nations with generous support, a fundamental question remains as to the extent to which these vulnerable countries can accurately identify the most vulnerable communities/areas. In Sri Lanka, informal businesses are particularly vulnerable to floods, but their informal status has limited the extent to which they could receive flood protection, including disaster-resilient infrastructure developments. The main objective of this paper, therefore, is to identify challenges that informal businesses have experienced in dealing with flood disaster risks. In doing so, we analyzed government policies and laws that are related to business formalization and disaster management. We also conducted interviews with key informants to verify our data. Our analysis found that the Sri Lankan government requires informal business owners to follow complicated rules to register their businesses. For these owners, who are not highly educated, these processes and fear of high tax rates discouraged registering their businesses. The central government tends to prioritize flood mitigation actions for formalized business areas. Informal businesses are not usually covered by flood insurance and compensation. In conclusion, we emphasize the need to establish widely available legal and administrative support for informal businesses to register. Adopting business continuity plans (BCPs) and keeping standardized business records also help businesses minimize flood loss and damage. [ABSTRACT FROM AUTHOR]

With the impact and cost of continued river degradation and climate changeinduced extreme rainfall and floods, there is growing urgency to achieve and maintain good river health to meet global sustainable development goals. Upscaling and working with nature-based solutions at regional, catchment and fluvial corridor scales is critical. One way to achieve this is to work within a recovery-enhanced approach to river rehabilitation, incorporating processes of geomorphic and vegetative recovery to build fluvial corridors in areas where they have become fragmented. Geomorphologically-informed cost-benefit analysis has not previously been applied in a riverine or riparian context, at the catchment or regional scale needed. We applied Marxan in a novel manner to undertake the first cost-benefit analysis for geomorphologically-informed rehabilitation of river systems. We estimated that to rehabilitate 75,500 km of streams in coastal catchments of NSW will cost $8.2 billion, comparing favourably to recent single flood event insurance losses, and projected future losses. We developed Marxan scenarios based on three broad approaches to river management: ad hoc and reactive, working with recovery and corridors, across current and future time periods. We found there are considerable current and flow-on future financial and non-financial benefits, and lower initial total and per-hectare rehabilitation costs, by fully adopting working with recovery or corridors approaches and moving away from the ad hoc and reactive approaches which dominate current practice. Implementing targeted rehabilitation based on a rolling sequence over time of corridors scenarios provides optimal holistic solutions to improve geomorphic condition and enhance recovery potential at landscape-scale. Our study demonstrates the use of Marxan as an accessible tool to address prioritisation complexity, and to run and cost landscape-scale rehabilitation scenarios over time. Our study also demonstrates the positive offsite feedbacks that occur through multiplier effects, as recovery occurs, and corridors are built. Geomorphologically-informed decision making becomes more robust, transparent, cost-effective, consistent across catchments, and adaptive to local situations and evolving river management priorities. [ABSTRACT FROM AUTHOR]

The United States National Flood Insurance Program (NFIP) relies on floodway and encroachment line standards to regulate floodplain development and minimize flood damage while ensuring public safety. A regulatory floodway includes a watercourse's main channel and adjacent floodplain areas critical for safely conveying a specified flood flow (the base flood discharge) without raising water surface elevations beyond permitted levels. The flood fringe, the remaining floodplain area inundated by the regulatory flood, can be developed under specific conditions. This paper introduces an optimization-based method to set encroachment limits while minimizing the value-weighted land area within the regulatory floodway, thereby maximizing the developable land in the flood fringe. The procedure organizes the complex decision process into a series of individual choices made at each cross section of a stream reach. These choices affect water surface elevations while meeting specific criteria, focusing primarily on limiting water level increases. Furthermore, floodplain encroachments are required to reduce a specified cross-section property on both sides of the floodplain equally. Two cross-section properties are considered in this study: conveyance and flow area. The NFIP's required approach is equal conveyance reduction. However, the cross-section flow area is more equitable for defining regulatory boundaries with long-lasting consequences. The computational method utilizes dynamic programming to minimize the value-weighted land area within the floodway. Additionally, it reduces the occurrence of irregular floodway boundaries and the need for discretionary modifications to encroachment limits, potentially eliminating such adjustments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

States not only govern markets, but they also create them, often with the intention of expanding or improving the delivery of specific policy objectives. This article outlines one way they do this: prefiguration. States prefigure markets, and private market actors, when they imagine and instantiate new market products, logics, and practices. I illustrate prefiguration through an analysis of the history of the National Flood Insurance Program (NFIP), the federal program that underwrites flood insurance in the United States. From the time of the NFIP's establishment, policymakers and officials have fashioned and continuously reformed a public program, and an insurance product, in ways that emulate an imagined primary private flood insurer. In doing so, though, they have gradually established the conditions under which private flood insurers can do business. This article contributes to scholarship on state 'marketcraft'. Whereas many scholars have addressed why governments turn to markets in the first place, and the consequences of doing so, this article offers a closer examination of what takes place in between: the specific activities that governments undertake as they pursue market creation. [ABSTRACT FROM AUTHOR]

Rapid and accurate maps of floods across large domains, with high temporal resolution capturing event peaks, have applications for flood forecasting and resilience, damage assessment, and parametric insurance. Satellite imagery produces incomplete observations spatially and temporally, and hydrodynamic models require tradeoffs between computational efficiency and accuracy. We address these challenges with a novel flood model which predicts surface water area from the U.S. National Water Model using a convolutional neural network (NWM‐CNN). We trained NWM‐CNN on 780 flood events, at a 250 m resolution with an RMSE of 4.58% on held out validation geographies. We demonstrate NWM‐CNN across California during the 2023 atmospheric rivers, comparing predictions against Sentinel‐1 mapped flood observations. We compared historical predictions from 1979 to 2023 to flood damage reports in Sacramento County, California. Results show that NWM‐CNN captures inundation extent better than the Height Above Nearest Drainage (HAND) approach (25%–36% RMSE, respectively). Plain Language Summary: We use machine learning (ML) to map floods quickly and accurately over large areas, which can help with predicting flooded extent, understanding impact, and aiding flood insurance and response. On their own, satellite images don't catch everything because they may be obscured or unavailable at the peak of a flood. Computer models that predict floods require a trade‐off between speed, accuracy, and resolution. Our solution uses ML to learn from the U.S. National Water Model and satellite images from past floods to predict how much of an area will be covered in water. We demonstrate this on floods in California in 2023 caused by atmospheric rivers, and we looked back at floods in Sacramento County from 1979 to 2023. We compared our method to another commonly used model and found ours was more accurate, making it a promising tool for future flood mapping and response planning. Key Points: Convolution neural networks (CNN) are suitable for rapid modeling of surface water dynamics for large‐scale inundation mappingWe deploy a CNN for continuous flood mapping across all of California during the devastating 2023 atmospheric river (AR) eventsInundation extent across Sacramento is more accurately predicted with CNN than the Height Above Nearest Drainage (HAND) [ABSTRACT FROM AUTHOR]

I study house price dynamics following Hurricane Sandy to explain the common puzzling finding of a price drop followed by a complete price recovery. Applying a quasi‐experimental difference‐in‐differences research design on Zillow parcel‐level sales data combined with Federal Emergency Management Agency data on damaged structures, I show that the extent of direct damages drives the decline in house prices. The extent of remodeling expenditures, as estimated from building permits, is found to be responsible for causing the return of prices to pre‐storm levels. Comparing flood insurance take‐up rates in the affected and non‐affected areas within floodplains and similarly for outside floodplains, I find no revision in perceived risk in the floodplain. In contrast, there has been an increase in flood insurance take‐up rates in affected areas outside of floodplains after the hurricane. [ABSTRACT FROM AUTHOR]

Obtaining accurate first-floor elevation (FFE) data within Galveston historic districts is limited or nonexistent and is often a function of securing elevation certificates (ECs). FFEs are critical in assessing flood damage and risk for structures and individuals concerning base flood elevations (BFEs). ECs are primarily written for flood insurance and are challenging to obtain through local municipalities and private insurance companies. The lack of easily accessible and accurate FFE data is a common standard across the US. This study used a drone-based approach coupled with photogrammetry to accurately derive elevation measures of structures within historic districts (Diaz et al. 2022) to address this problem. Specifically, we address the following research question: What is the flood vulnerability of historic districts in Galveston, Texas, and how can digital technology be leveraged to inform historic resource and broader built environment risk frameworks? [ABSTRACT FROM AUTHOR]

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Following the Great Mississippi Flood of 1927, U.S. private insurers abandoned flood coverage after deeming it incalculable, precipitating the National Flood Insurance Program (NFIP) in 1968. The NFIP continued to underwrite illegible risk in the public interest. For decades, hydraulic models were limited to simplified "one-dimensional" simulations ill-equipped to characterize uncertainty to the standard of market carriers. However, recent advancements in multidimensional characterization have galvanized the flood sector. The NFIP has licensed some of the most expansive 2-D models to date from eager tech firms; it has pledged to modernize its risk portfolio, invest in financing, and attract private carriers in order to "lift all boats" within the flood sector. This article intervenes by examining the "multidimensional turn" as a fix for "crises of calculation." The article rejects teleological narratives crediting models for "changing how we think" about flood, and illustrates how underwriters enframe illegible floodplains as unruly problems. The incalculability of flood risk is an "insurantial logic" naturalized as a physical truth. Economic geographers have interrogated the materiality of such truths as integral to the production of nature under capitalism. The article examines how invested state and non-state actors operationalize impediments to legible risk to realize their financial interests. It further argues that FEMA's costly efforts to realize an allusive market are undermined by intractable conflicts between pure-market and affordable coverage. The NFIP will likely continue to do the heavy lifting with respect to underwriting, and selective geographies of private coverage will mirror the drive for surplus value. [ABSTRACT FROM AUTHOR]

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

Insurance is an important element of flood risk management, providing financial compensation after disastrous losses. In a competitive market, insurers need to base their premiums on the most accurate risk estimation. To this end, (recent) historic loss data are used. However, climate variability can substantially affect flood risk, and anticipating such variations could provide a competitive gain. For instance, for a year with higher flood probabilities, the insurer might raise premiums to hedge against the increased risk or communicate the increased risk to policyholders, encouraging risk-reduction measures. In this explorative study, we investigate how seasonal flood forecasts could be used to adapt flood insurance premiums on an annual basis. In an application for Germany, we apply a forecasting method that predicts winter flood probability distributions conditioned on the catchment wetness in the season ahead. The deviation from the long term is used to calculate deviations in expected annual damage, which serve as input into an insurance model to compute deviations in household insurance premiums for the upcoming year. Our study suggests that the temporal variations in flood probabilities are substantial, leading to significant variations in flood risk and premiums. As our models are based on a range of assumptions and as the skill of seasonal flood forecasts is still limited, particularly in central Europe, our study is seen as the first demonstration of how seasonal forecasting could be combined with risk and insurance models to inform the (re-)insurance sector about upcoming changes in risk. [ABSTRACT FROM AUTHOR]

With a social network adjacency matrix constructed from the Facebook Social Connectedness Index (SCI), this paper examines whether social learning facilitates climate risk perception updates to inform climate adaptation. We find that Hurricanes Harvey and Irma‐induced regional flooding increased flood insurance policies nationwide to the extent of each county's social network proximity to the flooded areas, with a corresponding update in climate risk perception. Social learning resulted in an additional 250,000 policies in flooded counties and 81,000 policies in unflooded counties over 3 years. We find evidence of the salience effect but no support for adverse selection or over‐insurance. [ABSTRACT FROM AUTHOR]

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two‐thirds of flood‐exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster‐scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take‐up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take‐up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short‐lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take‐up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non‐primary residences. Overall, these findings suggest that relying on households to self‐adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap. Plain Language Summary: Disaster‐scale floods are expected to become more frequent in a warming climate, impacting areas where insurance is not mandated. Many flood‐exposed properties are currently uninsured, potentially undermining the climate resilience of communities. We investigate whether households might adapt to the changing flood risk by purchasing and maintaining insurance over time. We find that while disaster‐scale floods increase insurance demand, this effect is temporary, even among counties experiencing consecutive disaster‐scale flood years. This effect is more salient among inland counties as well as among homeowners whose primary residence is exposed to a disaster‐scale flood. We conclude that relying on households to self‐adapt by maintaining insurance is insufficient for closing the insurance protection gap. Key Points: Increasing flood risk is impacting areas where flood insurance is not currently mandatedConsecutive disaster‐scale flood years increase insurance take‐up, but this effect diminishes over timeRelying on the autonomous adaptation of households will be insufficient for closing the insurance protection gap [ABSTRACT FROM AUTHOR]

The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We integrate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the economic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregular floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event. [ABSTRACT FROM AUTHOR]

Centralized insurance can be found in both the private and public sectors. This paper provides a microeconomic study of the risk‐sharing mechanisms in these markets, where multiple policyholders interact with a centralized monopolistic insurer. With minimal assumptions on the risk preferences of the market participants, we characterize Pareto optimality in terms of the agents' risk positions and their assessment of the likelihoods associated with their loss tail events. We relate Pareto efficiency in this market to a naturally associated cooperative game. Based on our theoretical results, we then consider a model of flood insurance coverage via an illustrative example. The lessons drawn from our theoretical results and this example lead to important policy implications for the existing National Flood Insurance Program in the United States. [ABSTRACT FROM AUTHOR]

We study the role of insurance in improving household and community disaster recovery. Our analysis harnesses both a unique survey of residents impacted by four land‐falling hurricanes in the United States and foot traffic data on visits to local businesses. Households sort into two groups when it comes to financing recovery: those primarily using property insurance and those largely uninsured, instead relying more on friends and family. Insurance improves households' ability to participate in the local economy. Postflood visitation rates to many local businesses increase with greater flood insurance take‐up rates. Expanded participation in the local economy is consistent with insurance reducing financial constraints; indeed, insured households are less likely to report experiencing high financial burdens in both the short and longer‐run postdisaster and are less likely to have unmet funding needs. Despite this, motivating the purchase of disaster insurance remains challenging, particularly among low‐income households who perceive insurance as less useful. [ABSTRACT FROM AUTHOR]

Agriculture in Bangladesh is susceptible to numerous climatic hazards, especially flooding. Smallholder farmers in the country have been losing agricultural productions to flooding because they lack flood insurance for decades. Several studies that used the contingent valuation method (CVM) to investigate farmers' willingness to pay (WTP) for insurance demand through monetary payment mode reported that small farmers suffer considerable financial constraints that limit their ability to pay insurance premiums. To avoid this concern, present study designed a novel econometric approach to assessing the validity and reliability of non-monetary WTP estimates in the context of environmental valuation research. By comparing monetary and non-monetary payment systems using the CV method, the study offers non-monetary flood insurance options for Bangladeshi smallholder farmers. For this purpose, 600 farmers in four flood-prone districts of northern Bangladesh were interviewed about their preferences for a hypothetical flood insurance market. To assess the accuracy and precision of non-monetary WTP estimates, a multivariate regression model was used. The results show that farmers with lower incomes favored non-monetary payment choices, and approximately 26% of the overall sample opted to contribute a portion of their seasonal rice crop rather than cash to cover the cost of flood insurance. Regression results show that farmers' in-kind WTP decisions were influenced by a variety of factors, including farmland elevation, frequency of flood returns, non-farm income, and commercialization in agricultural farming, demonstrating the validity and consistency of the non-monetary payment alternative. Findings imply that subsidy policies are insufficient to expand the adoption of non-monetary flood insurance market in Bangladesh. [ABSTRACT FROM AUTHOR]

Floods are consistently ranked as the most financially devastating natural disasters worldwide. Recent flood events in the Netherlands, Caribbean, and US have drawn attention to flood risks resulting from pluvial and fluvial sources. Despite shared experiences with flooding, these regions employ distinct approaches and flood management strategies due to differences in governance and scale—offering a three-site case study comparison. A key, yet often lacking, factor for flood risk and damage assessments at the parcel level is building elevation compared to flood elevation. First-floor elevations (FFEs) are a critical element in the vulnerability of a building flooding. US-based flood insurance policies require FFEs; however, data availability limitations exist. Drone-based FFEs were measured in all locations to assess the flood vulnerabilities of structures. Flood vulnerability profiles revealed 64% of buildings were vulnerable to a form of inundation, with 40% belonging to "moderate" or "major" inundation, and inundation elevation means (IEMs) of −0.55 m, 0.19 m, and 0.71 m within the US, Netherlands, and Puerto Rico sites, respectively. Spatial statistics revealed FFEs were more responsible for flood vulnerabilities in the US site while topography was more responsible in the Netherlands and Puerto Rico sites. Additional findings in the Puerto Rico site reveal FFEs and next highest floor elevations (NHFEs) vulnerable to future sea level rise (SLR) flood elevations. The findings within the Netherlands provide support for developing novel multi-layered flood risk reduction strategies that include building elevation. We discuss future work recommendations and how the different sites could benefit significantly from strengthening FFE requirements. [ABSTRACT FROM AUTHOR]

Anticipated increase in future river flood risk highlights the need for effective flood insurance, as it enables hedging against this risk. However, its design varies significantly across countries. This study contributes to the debate on designing flood insurance mechanisms from an economy-wide perspective, considering both socioeconomic and climate changes. We apply a multi-regional computable general equilibrium (CGE) model for 2050 and find that, under current insurance market systems, flood risk causes regional GDP losses of up to −0.5%, societal welfare losses of up to −1%, and private and public consumption losses of up to −0.5% and −2.4%, respectively. These estimates are all relative to a scenario without flood risk. Our results indicate that flood risk intensifies pressure on public budgets. We find that insurance market reforms, including a higher degree of risk-sharing, mandatory purchase requirements, and public reinsurance, can alleviate adverse welfare effects and the burden on public budgets. [ABSTRACT FROM AUTHOR]

Timely acquisition of rescue target information is critical for emergency response after a flood disaster. Unmanned Aerial Vehicles (UAVs) equipped with remote sensing capabilities offer distinct advantages, including high-resolution imagery and exceptional mobility, making them well suited for monitoring flood extent and identifying rescue targets during floods. However, there are some challenges in interpreting rescue information in real time from flood images captured by UAVs, such as the complexity of the scenarios of UAV images, the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform. Thus, we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets (i.e., pedestrians and vehicles trapped by floods). The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model. The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer. Our experimental results demonstrate that the Intersection over Union (IoU) for flood water extraction reaches an impressive 80%, and the IoU for real-time flood water extraction stands at a commendable 76.4%. The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue. [ABSTRACT FROM AUTHOR]

Insurance is an important element of flood risk management providing financial compensation after disastrous losses. In a competitive market, insurers need to base their premiums on the most accurate risk estimation. To this end, (recent) historic loss data is used. However, climate variability can substantially affect flood risk, and anticipating such variations could provide a competitive gain. For instance, for a year with higher flood probabilities, the insurer might raise premiums to hedge against the increased risk or communicate the increased risk to policyholders encouraging risk-reduction measures. In this explorative study, we investigate how seasonal flood forecasts could be used to adapt flood insurance premiums on an annual basis. In an application for Germany, we apply a forecasting method that predicts winter flood probability distributions conditioned on the catchment wetness in the season ahead. The deviation from the long-term is used to calculate deviations in Expected Annual Damage which serve as input into an insurance model to compute deviations in household insurance premiums for the upcoming year. Our study suggests that the temporal variations in flood probabilities are substantial, leading to significant variations in flood risk and premiums. As our models are based on a range of assumptions and as the skill of seasonal flood forecasts is still limited, particularly in Central Europe, our study is seen as first demonstration of how seasonal forecasting could be combined with risk and insurance models to inform the (re-)insurance sector about upcoming changes in risk. [ABSTRACT FROM AUTHOR]

Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3d-FM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters (water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force) simulated by the model and National Flood Insurance Program (NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations. [ABSTRACT FROM AUTHOR]

Flood is always a source of social lamentation, huge infrastructural losses and disruption to economic activities in Bhagirathi Sub-basin in India. Climate variability and increasing flood incidents have created a dilemma for social, economic and environmental conditions of the affected communities. These implications necessitate assessing overall flood vulnerability to minimize their short and long-term impacts. This study presents a comprehensive analysis of composite vulnerability among the flood affected communities in Bhagirathi Sub-basin. Data for analyzing composite flood vulnerability were derived from an in-depth survey of 432 households selected through stratified random sampling method in the Sub-basin. Domains of vulnerability such as quality of life, social & economic status, health impacts, ecological implications, losses and adaptation were examined. A total of 95 indicators of these domains were considered to prepare composite vulnerability index of the selected villages. Relationship between vulnerability and households' characteristics was ascertained using cross tabulation and multinomial logistic regression. Analysis of composite vulnerability index (CVI) revealed very high vulnerability in Nutanhat, Bakkhali, Jhara, Gopalpur, Jayarampur, Titiha, Uchildaha and Mohanpur villages. High vulnerability was observed in Banagram, Mayapur, Amravati, Gobindapur, Raichak Boltala, Talim Nagar Minakhan and Majhirmana villages while Kalna Municipality was found under moderate vulnerability. High losses, ecological & health implications and low socioeconomic conditions of the households aggravated very high to moderate vulnerability in these villages. Gender, income and land possession were found strongly associated with high vulnerability while flood insurance, farming purposes and changes in rainfall pattern were identified inducing moderate vulnerability. CVI analysis assisted in identifying the priority villages for effective policy implications. The study calls for policy implications for lessening the impact of flood in the Sub-basin. [ABSTRACT FROM AUTHOR]

The latest report by the Intergovernmental Panel on Climate Change (IPCC) warns of an increase in heavy rainfall events due to global warming and climate change, which can result in significant economic costs for insurance companies and businesses. To address this challenge, insurance companies are focusing on developing new risk management strategies and offering new products such as flood insurance. However, the article argues that effective and feasible coordination shortens recovery time and can therefore drastically reduce the financial costs of a crisis—that is, the insurance costs. The paper analyses the deficit in crisis management during heavy rain events in Germany, based on the 2021 Ahr valley flood. The analysis is conducted based on document analysis and interviews and focuses on three areas of deficit: coordination between crisis staffs and (1) civil society, (2) emergency responders, and (3) political leaders. The paper highlights the importance of coordination during a crisis, which can help to address the crisis more efficiently and effectively, minimise damage and get communities back on their feet faster. The paper recommends policy changes to improve interface management and disaster management coordination. [ABSTRACT FROM AUTHOR]

Future flood risk assessments typically focus on changing hazard conditions as a result of climate change, where flood exposure is assumed to remain static or develop according to exogenous scenarios. However, this study presents a method to project future riverine flood risk in Europe by simulating population growth in floodplains, where households' settlement location decisions endogenously depend on environmental and institutional factors, including amenities associated with river proximity, riverine flood risk, and insurance against this risk. Our results show that population growth in European floodplains and, consequently, rising riverine flood risk are considerably higher when the dis-amenity caused by flood risk is offset by insurance. This outcome is particularly evident in countries where flood risk is covered collectively and notably less where premiums reflect the risk of individual households. This study finds that flood insurance policy design affects economic development in floodplains and, consequently, flood risk in Europe. Therefore, the authors advocate for flood insurance design to be integrated in climate change adaptation policy. [ABSTRACT FROM AUTHOR]

Effective flood risk management (FRM) requires a mix of policy instruments that reduces, shares, and manages flood risk. The social acceptability of these policy instruments—the degree of public support or opposition to their use—is an important consideration when designing an optimal mix to achieve FRM objectives. This paper examines public attitudes toward FRM policy instruments based on a national survey of Canadians living in high-risk areas. Respondents were asked their views on flood maps, disaster assistance, flood insurance, flood risk disclosure and liability, and property buyouts. The results indicate that all five policy instruments have high social acceptability, but they must be calibrated to ensure access to flood risk information and achieve a fair distribution of FRM costs among key stakeholders. [ABSTRACT FROM AUTHOR]

For residential structures in America's coastal communities, resilience is an essential system quality. Such resilience is best conceptualized as a given system's ability to anticipate, respond to, recover from, and adapt to disruption. Practically, the quantification of parcel-scale resilience requires detailed temporal data and the ability to assess various indicators of system performance variability. This paper considers expanded datasets related to wave and storm surge damage for two catastrophic disruptions: Hurricane Ike in Galveston County, Texas, and Hurricane Irma in Monroe County, Florida. Data mining suggests that appraised property values for single-family residences may be used to characterize a given parcel's post-disruption economic response and rebound behavior. Such rebound varies, and the authors offer four potential indicators of such variability for future study: the severity of the initial disruption, a parcel's location, a parcel's construction, and a given property's flood insurance coverage. In general, following a hurricane, recorded property values appear to serve as another feasible measure of a given disaster-affected system's resilient performance. Ultimately, these findings not only offer another mechanism by which community leaders can effectively manage resilience but also suggest that FEMA must continue to improve the National Flood Insurance Program. [ABSTRACT FROM AUTHOR]