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<br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />.1 <br />I <br />I <br />I <br />I <br />I <br />I <br /> <br />and Ohio basins, parts of the mid-Atlantic region, Califomia and Texas. Flood damage over <br />$100 million occurs relatively frequently, especially in Missouri, California, and Texas. <br /> <br />Perception of flood damage in a state is influenced by historical experience. A state's <br />median damage can be taken as the expectation of the flood damage threat in a "typical" year, its <br />maximum damage as the public view of a "major flood". These categories are useful in <br />describing how state perspectives on flood damage might differ. Although some states in each <br />category have experienced massive flood damage (over, say, $1 billion), such damage occurs <br />most frequently in the high vulnerability category. <br /> <br />One might expect that reporting of flood damage by NWS field offices would be <br />influenced by the flood history of an area. In low vulnerability states, floods causing over $1 <br />million damage are notable events and seem unlikely to go unreported. Conversely, in high <br />vulnerability states, damage of $5 million or more occurs frequently so smaller damages might <br />seem unremarkable and be easily ignored. <br /> <br />However, the analysis in Section 5 indicates that these expectations are false. In <br />California, a high vulnerability state, the NWS often reports damage under $5 million, but no <br />NWS estimates were provided in two years when the state claimed substantial damage (1979 and <br />1984).' Likewise in Colorado, a low vulnerability state, damage of $24 million went virtually <br />unreported in 1983 (the NWS estimate is $140,000). From these examples and others in Section <br />5, we conclude that omissions of estimates in the $5 - 25 million range in the NWS data sets are <br />not systematically related to the size of a state or its typical damage level; rather, the omissions <br />can be considered random inconsistencies in data collection operations. <br /> <br />C. Implications for Analysis of State Damages <br />States typical of the three vulnerability categories are shown in Figures 6-3(a-c) and Table <br />6-2. California represents the high vulnerability states, Alabama the medium vulnerability states, <br />and Maine the low vulnerability states. In all three states, damage totals for the full 41 year <br />period (Table 6-2) would be affected little by occasional omission of damage under $1 million. <br />Indeed, Califomia and Alabama totals would be affected little by a few $25 million omissions. <br />But in Maine, a $25 million flood is relatively large, representing over 10% of total damage. Its <br />omission could greatly influence the result of, say, a comparison of damages during two time <br />periods. Furthermore, since floods in Maine involve relatively low damage there is less <br />aggregation of damage estimates, therefore less tendency for errors to average out. <br /> <br />For low vulnerability regions, we recommend spatial aggregation to reduce the impact of <br />errors and omissions. Several contiguous regional groupings of states with similar frequency <br />distributions are suggested in the second column of Table 6-1. For example, estimates of <br />damage in New England are expected to be more reliable than estimates of damage in Maine. <br />. <br />Other groupings might be appropriate depending on the purpose of a particular analysis. <br /> <br />5 The largest known omission - of $50 million damage in California in 1979 - occurred when NWS data <br />collection had been seriously curtailed. It has been corrected in the revised data sets that we provide. <br /> <br />49 <br />