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Mapping the Zone: Improving Flood Map Accuracy 1 Introduction Flooding is the nation’s leading cause of disaster, contributing to nearly two-thirds of all federal disasters1 and causing approximately $50 billion in property damage in the 1990s (Downton et al., 2005). Much of the damage occurs in floodplains—the low, relatively flat areas adjoining inland and coastal waters, including areas subject to a 1 percent or greater chance of flooding in any given year.2 A house in the 1 percent annual chance (100-year) floodplain has a 26 percent chance of being damaged by flooding during a 30-year mortgage, compared to a 9 percent chance of being damaged by fire.3 Insurance companies generally consider residential flooding too costly to insure because floods can be widespread and cause catastrophic losses (Figure 1.1). The National Flood Insurance Program (NFIP) was established in 1968 to slow increasing flood disaster relief costs by offering federal flood insurance to owners of property in floodplains, provided their communities regulate new development in these areas (FEMA, 2002). The premium that property owners pay is related to their risk of flooding, which is determined by the location of their property on Flood Insurance Rate Maps (FIRMs; hereafter called flood maps) produced by the Federal Emergency Management Agency (FEMA). The accuracy of floodplain boundaries drawn on these maps directly determines how well communities and individuals understand and are insured against their true flood risk (e.g., Box 1.1). COMMITTEE CHARGE AND APPROACH This report is the second undertaken by the National Academies to examine FEMA map modernization. The first study, Elevation Data for Floodplain Mapping (NRC, 2007), assessed the data needed to map floodplains. It concluded that the existing National Elevation Dataset (NED) is not sufficiently accurate to support accurate floodplain mapping and recommended that a program be established to collect high-accuracy, high-resolution digital terrain data nationwide. This second report broadens the analysis to other factors that affect flood map accuracy, assesses the benefits and costs of more accurate flood maps, and suggests ways to improve flood mapping, risk communication, and management of flood-related data (Box 1.2). This study was initially requested by managers of FEMA’s Risk Analysis Division, and managers from the National Oceanic and Atmospheric Administration’s (NOAA’s) Coastal Services Center later added their support. Of particular interest to NOAA are the accuracy of geodetic data, which are relied on for all types of flood studies; the accuracy of bathymetric data, which are needed for storm surge modeling; and the usefulness of integrating NOAA inundation map libraries into a national map system. The committee addressed Tasks 1, 2, and 3 by gathering information from the literature and presentations 1 Of the 1,720 federal disasters declared from 1953 to 2007, flooding contributed to 1,100 disasters, severe storms to 984 disasters, and fire to 845 disasters. See <http://www.fema.gov/news/disasters.fema>. 2 Presidential Executive Order 11988. 3 See <http://www.floodsmart.gov/floodsmart/pages/flood_facts.jsp>.
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Mapping the Zone: Improving Flood Map Accuracy FIGURE 1.1 Flooding in downtown Cedar Rapids, Iowa, June 13, 2008. Floodwaters inundated about 100 city blocks, including May’s Island, where City Hall and the courthouse sit. SOURCE: Stephen Mally. Used with permission. BOX 1.1 Flooding in Conklin, New York, June 28, 2006 Improving the accuracy of maps can change the floodplain boundary and, thus, which properties are designated within the floodplain. Landowners often seek to avoid this designation,a which can have the effect of increasing insurance premiums, reducing property values, restricting development, and/or requiring costly mitigation efforts, such as raising the elevation of structures. At the same time, home and business owners have a strong financial interest in having their structures properly insured against flood damage. Residents of communities that are not in mapped 100-year floodplains often have no idea they are vulnerable to property losses until they are inundated. Yet one-third of flood insurance claims are for areas beyond the 100-year floodplain.b For example, the largest flood recorded in the Binghamton, New York, area extended beyond the 100-year floodplain to areas where flood insurance is not required, catching residents of the Conklin community off guard. As reported in the Press & Sun-Bulletin:c When Abby Mack moved into her home on Grandview Avenue in Conklin four years ago, her bank told her she didn’t need flood insurance. A check of information-based flood maps—which officials rely on to predict the frequency and impact of floods—shows her home is above the flood plain. But as the Susquehanna River continued to rise through the twilight hours of June 28, it became clear that her home and others on her street were threatened. Police evacuated bewildered residents shortly before the river began creeping up their tidy lawns and driveways and pouring into basements.… The Mack residence, which lost a hot water heater, other major appliances and carpeting, fared relatively well. The damage was much more extensive just down the street and in other places in Conklin that, according to the maps, shouldn’t have been touched by the flood.… “The whole flood plain has to be studied and re-evaluated,” said Debbie Preston, supervisor of the Town of Conklin, where the recent flood ruined hundreds of properties and forced the evacuation of the entire town. aPresentation to the committee by Patty Templeton Jones, Flood Committee of the Institute for Business and Home Safety, on November 8, 2007. bSee <http://www.floodsmart.gov/floodsmart/pages/flood_facts.jsp>. cT. Wilber, 2006, Floods 2006—Are Tier flood maps wrong? Press & Sun-Bulletin, July 9.
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Mapping the Zone: Improving Flood Map Accuracy BOX 1.2 Committee Charge The committee will Examine the current methods of constructing FEMA flood maps and the relationship between the methods used to conduct a flood map study (detailed study, limited detailed study, automated approximate analysis, or redelineation of existing hazard information), the accuracy of the predicted flood elevations, and the accuracy of predicted flood inundation boundaries. Examine the economic impacts of inaccuracies in the flood elevations and floodplain delineations in relation to the risk class of the area being mapped (based on the value of development and number of inhabitants in the risk zone). Investigate the impact that various study components (i.e., variables) have on the mapping of flood inundation boundaries: Riverine flooding The accuracy of digital terrain information Hydrologic uncertainties in determining the flood discharge Hydraulic uncertainties in converting the discharge into a floodwater surface elevation Coastal flooding The accuracy of the digital terrain information Uncertainties in the analysis of the coastal flood elevations Interconnected ponds (e.g., Florida) The accuracy of the digital terrain information Uncertainties in the analysis of flood elevations Provide recommendations for cost-effective improvements to FEMA’s flood study and mapping methods. Provide recommendations as to how the accuracy of FEMA flood maps can be better quantified and communicated. Provide recommendations on how to better manage the geospatial data produced by FEMA flood map studies and integrate these data with other national hydrologic information systems. to the committee and by conducting case studies in North Carolina and Florida (see “Case Studies” below). The results of the first three tasks formed the basis for the recommendations in Tasks 4, 5, and 6. CASE STUDIES Case studies were carried out to examine factors that affect riverine flood map accuracy and to assess the costs and benefits of more accurate flood maps. Most of the hydrologic, hydraulic, elevation, and economic analyses were carried out in collaboration with the North Carolina Floodplain Mapping Program. North Carolina was selected because flood maps developed using high-accuracy lidar data were available for nearly the entire state, enabling comparison of traditional and new data and techniques. The North Carolina studies focused on three physiographic regions, including the mountainous city of Asheville (Buncombe County), the rolling hills of Mecklenburg County, and the flat coastal plain of Pasquotank and Hertford Counties (Figure 1.2). Two coastal plain counties were analyzed because the most comprehensive development and insurance information needed for the benefit-cost analysis was available in Pasquotank County, but more comprehensive hydraulic information was available in Hertford County. The committee used benefit-cost analyses to assess the economic impacts of inaccuracies in floodplain boundaries and flood elevations (Task 2). Such methods, which are used by FEMA for determining the benefits and costs of different mapping approaches, are based on measuring economic impacts, favorable and unfavorable, in monetary terms.4 The committee’s assessment relied on FEMA reports as well as a case study in Mecklenburg and Pasquotank Counties and the City of Asheville. The case study compared the costs of creating new digital flood maps with two result- 4 Benefit-cost analysis differs from economic impact analysis, which traces direct and indirect spending effects through the economy. For example, an economic impact analysis might trace the results of a prediction of a particular type of flood to the amount of damage. A direct effect of flooding is damage to the house, and indirect effects include fewer pizzas but more plywood purchased.
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Mapping the Zone: Improving Flood Map Accuracy FIGURE 1.2 Physiographic provinces and location of counties studied in North Carolina. Hydrologic, hydraulic, and elevation data were analyzed in Buncombe, Mecklenburg, and Hertford Counties; benefits and costs were assessed in Buncombe, Mecklenburg, and Pasquotank Counties. SOURCE: North Carolina Floodplain Mapping Program. Used with permission. ing benefits: avoided flood losses for new buildings and avoided repairs to infrastructure through accurate floodplain delineation and setting flood insurance premiums to better match estimates of actual risk. The assumption was that if the benefit-cost ratio was greater than 1, even when only a subset of benefits was considered, society would gain by improving map accuracy. The analysis was based on a comparison of buildings designated as either in or out of the floodplain under different mapping approaches. The importance of accurate elevation data (Task 3) was evaluated by comparing maps made using the U.S. Geological Survey (USGS) National Elevation Dataset and lidar. For the hydrology and hydraulics analysis (Task 3), the committee followed the National Research Council (NRC, 2000) report by distinguishing two sources of uncertainty: natural variability and knowledge uncertainty. The inherent variability of nature leads to uncertainty that can never be eliminated. For example, the magnitude of future floods cannot be forecast precisely, no matter how much time, effort, or money is invested in flood modeling and mapping. In contrast, knowledge uncertainties arise from either incomplete understanding of events and processes or a lack of data, and they can be reduced with additional information. Knowledge uncertainty associated with riverine flooding (Task 3a) was examined through flood modeling and mapping case studies in Mecklenburg and Hertford Counties and the City of Asheville. Natural variability was quantified through the analysis of flood frequency from recorded annual maximum flood flows and stages at 21 USGS stream gages in the case study areas and other portions of the coastal plain of North Carolina. The NRC (2000) report examined uncertainties in discharge, water surface elevation, and economic damage, and concluded that mathematical flaws in the formal uncertainty analysis method preclude determining the precision of the uncertainty estimates. Consequently, the committee did not attempt a formal uncertainty analysis, in which uncertainties from various sources are combined mathematically to determine the total uncertainty in flood map variables. Shallow flood frequency (Task 3c) was analyzed using data from 10 USGS stream gages in southwest Florida, an area subject to shallow flooding associated
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Mapping the Zone: Improving Flood Map Accuracy with interconnected ponds. However, comprehensive case studies to quantify factors that affect the accuracy of coastal and interconnected pond maps (Tasks 3b and 3c) were not practical because capabilities to model coastal and shallow flood processes are rapidly evolving. In contrast, methods for riverine flood mapping are more mature and well established. Consequently, the committee simply outlined the accuracy and uncertainty associated with coastal flooding and interconnected ponds. ORGANIZATION OF THE REPORT This report examines FEMA’s mapping methods and recommends ways to improve flood map accuracy and to communicate and manage flood-related information. Chapter 2 describes how FEMA Flood Insurance Rate Maps are created, maintained, and used for insurance, regulatory, and other purposes. Chapter 3 examines the importance of elevation data in flood map accuracy and describes how land and water surfaces are defined relative to geodetic datums. Chapters 4 and 5 analyze factors that affect the accuracy of flood mapping of inland and coastal regions. Chapter 6 assesses the economic benefits of more accurate flood maps. Chapter 7 discusses ways to communicate flood hazard and risk. Methods used to estimate base flood elevations are summarized in Appendix A. Biographical sketches of committee members (Appendix B), a glossary of commonly used terms (Appendix C), and a list of acronyms and abbreviations (Appendix D) appear at the end of the report.
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