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This study is a reanalysis of flood damage estimates collected by the National Weather Service (NWS) between 1925 and 2000. The NWS is the only organization that has maintained a long-term record of flood damage throughout the U.S. The NWS data are estimates of direct physical damage due to flooding that results from rainfall or snowmelt. They are obtained from diverse sources, compiled soon after each flood event, and not verified by comparison with actual expenditures. Therefore, a primary objective of the study was to examine the scope, accuracy, and consistency of the NWS damage estimates to improve the data sets and offer recommendations on how they can be appropriately used and interpreted. Data sets on the website are updated occasionally as new data becomes available.
A complete documentation of flood damage would include both direct and indirect costs associated with flooding. Direct costs are closely connected to a flood event and the resulting physical damage. In addition to immediate losses and repair costs they include short-term costs stemming directly from the flood event, such as flood fighting, temporary housing, and administrative assistance. By contrast, indirect costs are incurred in an extended time period following a flood. They include loss of business and personal income (including permanent loss of employment), reduction in property values, increased insurance costs, loss of tax revenue, psychological trauma, and disturbance to ecosystems. They tend to be more difficult to account for than direct costs.
The NWS describes its flood loss data as estimates of “direct damages” including, for example, loss of property and crops and costs of repairing damaged buildings, roads, and bridges. The NWS estimates have usually been restricted to direct physical damage, a subset of the losses generally considered to be direct costs.
The NWS damage estimates are compiled soon after each flood event, before the actual costs of repair and replacement can be known. They are not verified by comparison with actual expenditures. The estimates are gathered from diverse sources, some who use accurate estimation methods (e.g. insurance companies) and others who do not (e.g. newspapers). Therefore, NWS damage data are best described, not as “loss data”, but as “damage estimates.”
To assess the reasons for increasing flood damage and their implications for policy, decision makers need to distinguish the influences of climate, population growth and development, and policy on damage trends. For example, increased damage due to changing climate may require different policy actions than would increases that result from implementation of particular policies.
The national and state flood damage data presented here are rough estimates, useful for large-scale comparisons and analysis of trends. However, more accurate and detailed damage estimates would be needed to evaluate the effectiveness of particular mitigation measures designed to reduce flood losses. For example, the National Flood Insurance Program was created in 1968 to “assist in reducing damage caused by floods” (42 U.S.C. # 4102 (c)(3)). Researchers evaluating the program need historical data at local scales to isolate the effect of the program from all other factors influencing flood damage in particular areas.
Scientists, too, need historical flood damage data at a variety of spatial scales to analyze variations in flood damage and what contributes to them. For example, during El Niño years, southern California receives more precipitation than in the typical year. Conventional wisdom suggests that the increase in precipitation should result in an increase in damaging floods. If California’s emergency planners knew this to be the case, they could prepare for the floods that come with El Niño, possibly reducing damage. In this case, scientists looking for a causal relationship would want to determine to what degree historical high damage years in southern California are associated with El Niño events.