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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 />I <br />I <br />I <br />I <br />I <br />I <br />I <br /> <br />D. Inadequate Estimation Methods <br />Potentially the most serious source of inaccuracy is the ad hoc approach to obtaining <br />damage estimates from each NWS field office (described in Section 2). The estimates are <br />collected by staff members who have little or no training in damage estimation and who rely on <br />diverse sources. Estimation methods used by their sources are unknown, and completeness of <br />coverage varies. Estimates are usually obtained within 2 months after a flood event and are not <br />compared by the NWS with records of actual damage. <br /> <br />Incomplete reports and omissions <br />A state emergency management official (Kay Phillips, Ohio Emergency Management <br />Agency, personal communication, 7/25/00) complains that the NWS calls her asking for a <br />damage estimate within a few weeks after a disaster. At that time, the extent of damage is <br />unknown and emergency managers are scurrying to respond to immediate needs. They have <br />some knowledge of losses to individuals, but little knowledge of damage to infrastructure, which <br />makes up a large part of total losses. Thus, in her opinion, early loss estimates tend to be much <br />too low in relation to final tabulations. <br /> <br />An example of underestimation is the NWS damage estimate for California flooding <br />associated with Hurricane Kathleen in 1976. The NWS dataset (which had not been fully <br />updated because annual summaries were discontinued that year) gave a damage estimate of $42 <br />million, whereas estimates in subsequent published reports (e.g., Montane 1999) are 3 to 4 times <br />higher. <br /> <br />Errors of omission occur when a significant flood event is overlooked entirely. For <br />example, flash floods in Califomia in July 1979 caused damage estimated at $26-50 million <br />(Montane 1999), but the NWS dataset reported no damage. <br /> <br />Potential biases <br />A substantial bias toward underestimation is expected due to incomplete reporting and <br />omission of some floods. However, we hypothesize that some damage estimates provided to the <br />NWS field offices might be biased upward if, for example, losses were exaggerated to improve <br />chances of getting state or federal assistance. Accuracy and bias in early damage estimates are <br />examined in Section 5. <br /> <br />23 <br />