Loss prevention and distribution must begin well before a flood event at multiple levels. However, the benchmarks and terminology we use to manage and communicate flood risk may be working against this goal. U.S. flood policy is based upon a flood with a one percent chance of occurring in any year. Commonly called the "hundred year flood," it has been upheld as a policy criterion, but many have questioned the effectiveness of hundred year flood terminology in public communication. This research examined public perceptions of the hundred year flood and evaluated the comparative effectiveness of this term and two other methods used to frame the benchmark flood: a flood with a one percent chance of occurring in any year and a flood with a 26 percent chance of occurring in thirty years. This research also explored how flooding and flood risk messages fit into the larger context of people's lives by modeling the relationships between flood related understanding, attitude and behavior and the situational and cognitive contexts in which these factors are embedded. The final goal was to come up with locally based suggestions for improving flood risk communication. Data were collected in the Towns of Union and Vestal, New York. Participants were adult residents of single family homes living in one of two FEMA designated floodplains. Face to face surveys and focus groups were used to gather information on respondents' flood experience and loss mitigation activities; general perception of flood risk and cause; flood information infrastructure; perceptions associated with specific flood risk descriptions; and basic demographic data. Focus groups were also asked to suggest improvements to flood risk communication. Results indicated that experience was the most influential factor in perception and behavior. Additionally, there was little evidence that understanding led to "appropriate" behavior. The 26 percent chance description was the most effective when both understanding and persuasion were included, but interpretations of probabilistic flood risk messages were highly individualized. Finally, regulatory practice likely influences attitude and behavior and may emphasize the likelihood of a particular flood at the expense of the possibility of flooding in general.

In response to the rising costs of floods, the United States has adopted sophisticated programs to mitigate the loss of life and property. However, the efficient implementation of certain aspects of flood policy has taken precedence over effective communication. The scope of the National Flood Insurance Program and the efficient coding of "the 100 year flood" have led to a pervasive use of the term in both formal and informal risk communication. When officials began consciously communicating flood policy to the public, they assumed a narrow "engineering" model and did not fully anticipate the influence of informal communication on the perception of flood risk. The effectiveness of the "100 year flood" as a means to change attitudes or motivate behaviors was not assessed. Nor was its utility in increasing public understanding of flood risk. New explanatory methods have been introduced, but they, too, have yet to be tested. This project evaluated the effectiveness of four methods commonly used to communicate the risk associated with policy's benchmark flood. These include: a 100 year flood; a flood with a 1 percent chance of occurring in any year; a flood with a 26 percent chance of occurring in 30 years; and a flood risk map available through Project Impact. Data were collected using a structured face to face questionnaire survey of residents living in Wimberley, Texas. Respondents included individuals who lived inside the boundaries of official flood plains, as well as those who did not. Comparable questions regarding uncertainty, perceived need for protection, and levels of concern were asked using each of the four methods of description. Qualitative observations were made during both the interviews and the collection of secondary data. Results showed a significant disjuncture of understanding and persuasion with each method; potentially serious problems with the 26 percent chance method; and a preference for concrete references in describing risk. It is recommended that use of the 26 percent chance method be discontinued. Both the 100 year flood and the map performed better than expected; these descriptions are recommended with reservations in lieu of more contextually appropriate methods of communication and policy formation.

Thesis (M.M.P.)--University of Delaware, 2006. / Principal faculty advisor: George R. Parsons, Dept. of Marine and Earth Studies. Includes bibliographical references.

This thesis presents an analysis of subjective risk information and participation in the National Flood Insurance Program (NFIP). Data are taken from a survey of residents in flood-prone coastal regions in the southeastern U.S. Regression models are constructed to better understand factors affecting individuals’ perceived risk ambiguity related to flood risk and the role of risk preferences, risk perceptions, and especially risk ambiguity, on the decision to purchase flood insurance. This is the first study not only of the influence of risk ambiguity on NFIP participation, but also of the impact of using different risk perception measures. Results indicate that NFIP participation is significantly affected by mean perceived risk, but the influence of range/variance of perceived risk, which presents one’s perceived ambiguity, is mixed.

The National Flood Insurance Program (NFIP) introduced the Community Rating System (CRS) to encourage community-level flood mitigation and increase individual-level flood insurance uptake. This study analyzes what factors influence communities to participate in the CRS and conditional on participation, what factors affect the type and intensity of participation over time? I used historical data of policies-in-force, claims, CRS participation, and U.S. Census American Community Survey for all NFIP communities in 11 key states to answer these questions. The results show socio-demographic factors significantly and positively predict communities' likelihood of participating in the CRS, whilst flood risk variables of claims and policies held do not. Further, flood risk variable (claims payment received) as well as socio-demographic variable (income) positively influence the intensity of communities' participation in the CRS over time.

No description available.

Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse / landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery.) Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by providing an efficient way to revise the National Flood Insurance Program maps.

Thesis (M. En.)--Miami University, Institute of Environmental Sciences, 2005. / Title from first page of PDF document. Document formatted into pages; contains [1], v, 169, [1] p. : ill. Includes bibliographical references (p. 36).

No description available.

<p dir="ltr">The National Flood Insurance Program (NFIP), which is run by the U.S. Federal Emergency Management Agency (FEMA), is presently under huge debt to the U.S. treasury. The debt is primarily caused by low flood insurance take-up rate, low willingness to pay for flood insurance, and large payouts after major disasters. Addressing this insolvency problem requires the NFIP to understand (1) what drives the demand for flood insurance so that it can be increased, (2) how risk factors contribute towards large flood insurance payouts so that effective risk reduction policies can be planned, and (3) how to predict the future flood insurance payouts so that the NFIP can be financially prepared. This research has answered these three fundamental questions by developing empirical models based on historical data. To answer the first question, this research has developed a propensity score-based causal model that analyzed one of the key components that influences the demand for flood insurance – the availability of post-disaster government assistance. It was found that the availability of the federal payout in a county in a year increased the number of flood insurance policies by 5.2% and the total insured value of the policies by 4.6% in the following year. Next, this research has developed Mixed Effects Regression model that quantified the causal relationships between the annual flood insurance payout in a county and flood related risk factors such as flood exposure, infrastructure vulnerability, social vulnerability, community resilience, and the number of mobile homes in the county. Based on the derived causal estimates, it was predicted that climate change, which is expected to increase flood exposure in coastal counties, will increase the annual NFIP payout in New Orleans, Louisiana by $2.04 billion in the next 30 years. Lastly, to make the NFIP financially prepared for future payouts, this research has developed a predictive model that can predict the annual NFIP payout in a county with adequate predictive accuracy. The predictive model was used to predict the NFIP payout for 2021 and it was able to predict that with a 9.8% prediction error. The outcomes of this research create new knowledge to inform policy decisions and strategies aimed at fortifying the NFIP. This includes strategies such as flood protection infrastructure, tailored disaster assistance, and other interventions that can bolster flood insurance uptake while mitigating the risk of substantial payouts. Ultimately, this research contributes to sustaining the NFIP's ability to provide vital flood insurance coverage to millions of Americans.</p>