2017Natural Hazards ReviewRequires access

Flood Risk Delineation in the United States: How Much Loss Are We Capturing?

Russell Blessing, Antonia Sebastian, Samuel D. Brody

Open publisher page 112 citations

Abstract

The 100-year floodplain is the traditional indicator of flood risk and the area in which specific flood mitigation requirements are required to occur in the United States. However, recent studies have indicated that there is a growing disconnect between the 100-year floodplain and the location of actual losses. As a result, there is a strong need to understand what is undermining the efficacy of the 100-year floodplain and to generate a more accurate depiction of flood risk. However, there have been few studies that examine the characteristics of insured flood claims occurring outside the 100-year floodplain and how more advanced hydrologic models may improve flood risk delineation. This study addresses this issue by cross-validating a fairly new distributed hydrologic flood inundation model and the Federal Emergency Management Association’s 100-year floodplain with historical, parcel-level insured flood losses in two subbasins near Houston, Texas. Results illustrate that spatially distributed hydrologic models greatly improve floodplain delineation, provide important insights on the drivers of flood damage outside of the floodplain, and offer alternative ways to more effectively communicate flood risk.

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What this paper is about

The 100-year floodplain is the traditional indicator of flood risk and the area in which specific flood mitigation requirements are required to occur in the United States. However, recent studies have indicated that there is a growing disconnect between the 100-year floodplain and the location of actual losses. As a result, there is a strong need to understand what is undermining the efficacy of the 100-year floodplain and to generate a more accurate depiction of flood risk. However, there have been few studies that examine the characteristics of insured flood claims occurring outside the 100-year floodplain and how more advanced hydrologic models may improve flood risk delineation. This study addresses this issue by cross-validating a fairly new distributed hydrologic flood inundation model and the Federal Emergency Management Association’s 100-year floodplain with historical, parcel-level insured flood losses in two subbasins near Houston, Texas. Results illustrate that spatially distributed hydrologic models greatly improve floodplain delineation, provide important insights on the drivers of flood damage outside of the floodplain, and offer alternative ways to more effectively communicate flood risk.

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Available abstract

The 100-year floodplain is the traditional indicator of flood risk and the area in which specific flood mitigation requirements are required to occur in the United States. However, recent studies have indicated that there is a growing disconnect between the 100-year floodplain and the location of actual losses. As a result, there is a strong need to understand what is undermining the efficacy of the 100-year floodplain and to generate a more accurate depiction of flood risk. However, there have been few studies that examine the characteristics of insured flood claims occurring outside the 100-year floodplain and how more advanced hydrologic models may improve flood risk delineation. This study addresses this issue by cross-validating a fairly new distributed hydrologic flood inundation model and the Federal Emergency Management Association’s 100-year floodplain with historical, parcel-level insured flood losses in two subbasins near Houston, Texas. Results illustrate that spatially distributed hydrologic models greatly improve floodplain delineation, provide important insights on the drivers of flood damage outside of the floodplain, and offer alternative ways to more effectively communicate flood risk.

Key concepts: Floodplain, Flood myth, 100-year flood, Environmental science, Hydrology (agriculture), Flood insurance, Water resource management, Environmental resource management

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