Reference Information: Pielke, Jr., R. A. and C. W. Landsea 1998.
"Normalized Hurricane Damages in the United States: 1925-1995,"
Weather and Forecasting, 13: 621-631. A reprint is available on-line at

January 28, 1998

Roger A. Pielke, Jr. (
Environmental and Societal Impacts Group
National Center for Atmospheric Research
P.O. Box 3000
Boulder, CO 80307-3000

Christopher W. Landsea (
Hurricane Research Division
4301 Rickenbacker Causeway
Miami, FL 33149

© Copyright 1998 American Meteorological Sociey
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Hurricanes are the costliest natural disasters in the United States (BTFFDR 1995). Understanding how both hurricane frequencies and intensities vary from year to year as well as how this is manifested in changes in damages that occur is a topic of great interest to meteorologists, public and private decision makers and the general publi c alike. Previous research into long-term trends in hurricane-caused damage along the United States coast has suggested that damage has been quickly increasing within the last two decades, even after considering inflation. However, to best capture the year-to-year variability in tropical cyclone damage, consideration must also be given toward two additional factors: coastal population changes and changes in wealth. Both population and wealth have increased dramatically over the last several decades an d act to enhance the recent hurricane damages preferentially over those occurring previously. More appropriate trends in the United States hurricane damages can be calculated when a normalization of the damages are done to take into account inflation, and changes in coastal population and wealth.

With this normalization, the trend of increasing damage amounts in recent decades disappears. Instead, substantial multidecadal variations in normalized damages are observed: the 1970s and 1980s actually incurred less damages than in the preceding few decades. Only during the early 1990s does damage approach the high level of impact seen back in the 1940s through the 1960s, showing that what has been observed recently is not unprecedented. Over the long-term, the average annual impact of damages in the continental United States is about $4.8 billion (1995 $) - substantially more th an previous estimates. Of these damages, over 83% are accounted for by the intense hurricanes (Saffir-Simpson 3, 4 and 5), yet these make up only 21% of the U.S.-landfalling tropical cyclones.

I. Introduction- Why Trends Matter

In recent years, decision makers in government, insurance, and other sectors have demonstrated increasing concern about the actual and potential impacts of weather and climate on society. To a significant degree, concern has been motivated by expectations that human-induced climate change will result in increasingly greater weather-re lated impacts to society. Concern has also been motivated by actual increases in weather-related impacts documented in recent years. Understanding these impacts in terms of trends, causes, and projections has significance for a range of policy decisions related to disaster mitigation and the international negotiations on climate change.

This paper focuses on trends in hurricane impacts in the United States because of the relatively well-documented information available on trends in hurricane climatology, economic impacts, and societal factors underlying those impacts.(1) Recent increases in the impacts of hurricanes in t he United States have focused attention on them. In addition, the increased damages related to hurricanes has been attributed to climate change by the U.S. Senate, many in the insurance industry, and Newsweek magazine, among many others (BTFFDR 19 95, Dlugolecki et al. 1996, Newsweek 1996). Recent research indicates that this attribution has been made incorrectly, leading to a conclusion that the factors responsible for documented trends in hurricane impacts are widely misunderstood (Lands ea et al. 1996, Pielke 1997). The purpose of this paper is to examine trends in hurricane impacts in the United States in order to provide researchers and policy makers with reliable information on which to base their expectations of future impacts.

II. Trend Data The impacts of weather on society have been defined according to a threetiered sequence (Changnon 1996): "Direct impacts" are those most closely related to the event, such as property losses associated with wind damage. "Secondary impacts" are those rel ated to the direct impacts. For example, an increase in medical problems or disease following a hurricane would be a secondary impact. "Tertiary impacts" are those which follow long after the storm has passed. A change in property tax revenues collecte d in the years following a storm is an example of a tertiary impact. The impacts discussed in this paper are direct impacts. Table 1 shows the direct impacts associated with Hurricane Andrew's landfall in South Florida in 1992.

Data on the economic impacts of hurricanes is published annually in the Monthly Weather Review and is summarized in Hebert et al. (1996).(2) Figure 1 shows the annual record of total hurricane losses (direct damages, inflation adjusted) in the United S tates from 1900-1995. An independent record of estimated losses to the insurance industry is kept by Property Claims Services, Inc. and is shown for the period 1950-1995 in Figure 2. Both figures show more events and more extreme events in recent deca des and years. Viewing these trend data, it would seem logical to conclude that hurricanes have become more frequent and severe as compared to earlier this century. Indeed, a 1995 U.S. Senate report asserted that hurricanes "have become increasingly fr equent and severe over the last four decades as climatic conditions have changed in the tropics" (BTFFDR 1995, p.23). Many insurers, as well, have concluded that hurricanes have become more frequent (Dlugolecki et al. 1996). In fact, the past several decades have seen a decrease in the frequency of intense hurricanes and the period of 1991 to 1994 was the least active four-year period in at least fifty years (Landsea et al. 1996). This trend means that more frequent or more intense hurricanes are not the cause of increasing hurricane-related damages, rather society has become more vulnerable to the effects of hurricanes (Pielke 1997).

At least three factors account for the apparent misreading of the historical record. First, in 1997 a dollar is worth less than one tenth what it was fifty years ago due to inflation. Yet, even when one accounts for inflation, a trend of exponentially i ncreasing losses remains as is shown in Figures 1 and 2. A second factor is that changing population patterns and demographics underlie the loss record. A storm that made landfall many years ago would cause significantly greater damage today simply because there are more people and property located in vulnerable coastal locations. Consider that in 1990, Dade and Br oward counties in South Florida were home to more than the number of people who lived in 1930 in all 109 counties from Texas through Virginia along the Gulf and Atlantic coasts (Pielke 1995). Figure 3 illustrates the rapi d growth that has occurred in southeast Florida. A third and final factor for the misreading of the historical record is that people today are simply wealthier in terms of their possessions than were people years ago. Coastal residents today have more t o lose. For these important reasons, interpreting the hurricane loss record is fraught with difficulties.

It is possible to "normalize" the historical loss record to values which are more representative in today's context (Changnon et al. 1997). Researchers have used several types of tools to improve upon the actual loss record to better understand past impacts (Dlugolecki et al. 1996). In the "stage damage" approach, people or property subject to risk of hurricane impacts are inventoried based on a number of key dimensions (e.g., number, type, and location of structures) and then, based on the inventory, a computer model is created to estimate losses from a particular event's impact. A number of companies (such as Property Claims Services) run these models (Banham 1993). Table 2 illustrates the output of one such model. In "simulation" approaches modeling is used as well, but the focus is not on a particular event but instead on a family of events and the co rresponding frequency and magnitude distribution of impacts. Companies such as Applied Insurance Research and EQE International run these sorts of models (Banham 1993), an example of which is shown in Table 3.

Catastrophe models are only as good as the assumptions which underlie them. For instance, prior to hurricane Andrew models such as these led hurricane loss experts to conclude that the worst case scenario for a hurricane impact along the U.S. coast would be around $10 billion (e.g., Sheets 1992). (3) Even in the immediate aftermath of Andrew many estimates of damages were off by significant amounts (Noonan 1993). The primary reason that the model estimates were off for this particular event was a number of important factors not included in the models that only became apparent in the wake of the disaster. Of course, these models are designed for the specific use by the insurance industry, and thus may not meet the needs of other decision makers. While different decision makers have different needs for impact information (i.e., timeliness, accuracy, etc.), large errors in impact estimates can have significant negative influences on specific decisions. Conversely, certain decisions can be improved with accurate impact information. West and Lenze (1994, p.145) ask "how do we determine whether a model has 'correctly' simulated an impact?" They find that "at present, most evaluation in regional impact analysis is confined to the fairly simple and non-rigorous step of asking whether the results look 'reasonable'" and they recommend further research in the area of model evaluation.

In order to provide researchers and decision makers with a more accurate picture of trends in hurricane impacts in the United States, we have normalized past damages to 1995 values using a simple, transparent methodology (Behn and Vaupel 1982, Patton and Sawicki 1986). This methodology may also be useful as an independent check on the output of the more complex catastrophe models.

III. Normalized Data

To normalize past impacts data to 1995 values, it is assumed that losses are proportional to three factors: inflation, wealth, and population. The result of normalizing the data will be to produce the estimated impact of any storm as if it had made landfall in 1995 (cf. Changnon et al. 1997).

Inflation is accounted for using the implicit price deflator for Gross National Product, as reported in the Economic Report of the President (US GPO 1950, 1996). Wealth is measured using an economic statistic kept by the U.S. Bureau of Economic Analysis called "Fixed Reproducible Tangible Wealth" and includes equipment and structures owned by private business, owner-occupied housing, nonprofit institutions, durable goods owned by consumers, as well as government-owned equipment and structures (BEA 1993). Wealth is accounted for in the normalization using a ratio (inflation-adjusted) of today's wealth to that of past years (end of year gross stock from Table A15, BEA 1993). (4) Because the measure of wealth is based on national figures, we have adjusted it back to per capita by removing from it the relative change in the entire U.S. population. Wealth data are available from 1925, consequently the normalization begins with that year.

The final factor is population change based on data from the U.S. Census for each of the 168 coastal counties that lie along the coast from Texas to Maine. (5) The population factor is defined as the change in population of the affected coastal county (or counties).

To summarize, the normalization method is determined as follows:

NL95 = a storm's loss normalized to 1995 value

y = year of storm's impact

c = county(ies) of storm's maximum intensity at landfall6

Ly = storm's loss in year y, in current dollars (i.e., not adjusted for inflation).

Iy = inflation factor, determined by the ratio of the 1995 implicit price deflator for Gross National Product to that of year y.

Wy = wealth factor, determined by the ratio of the inflation adjusted 1995 fixed reproducible tangible wealth expressed as per capita to that of year y.

Py, c = population factor, determined by the ratio of the change in the population of the coastal county(ies) most affected by the storm from year y to 1995 to that of the entire nation in year y. County(ies) affected by the storm are defined by Jarrell et al. (1992).7

The general formula for y = 1925 to 1995 is thus,

NL95 = Ly * Iy * Wy * Py, c

For example, the 1938 New England hurricane made landfall as a Category 3 hurricane through the states of New York, Connecticut, Rhode Island and Massachusetts causing an estimated $306 million damage.(8) The population of the coastal counties impacted (Suffolk (NY), New London, Middlesex, New Haven, Fairfield (CT), Newport, Bristol, Providence, Kent, Washington (RI), Bristol (MA)) at that time was 2.336 million, while the 1995 estimated population had increased to 4.860 million, a factor of 2.08. The inflation and wealth factors are 11.75 and 2.224, respectively, between 1938 and 1995. Thus, the normalized damage that would be attributed to the 1938 New England hurricane if it struck in 1995 is the following:

$306 million (1938) x 11.75 x 2.224 x 2.080 = $16,629 million (1995)

IV. Interpretation of the Data

The normalized trend data on annual hurricane impacts from 1925-1995 is shown in Figure 4. It shows the estimated losses associated with each year's hurricane activity, as if each year's storms had made landfall in 1995.(9) It presents a much different picture than the non-normalized data. It shows that in the 1940s, 1950s, and 1960s and more frequent and costly landfalls occur red than in the 1970s and 1980s, consistent with the climatology of hurricane landfalls (Neumann et al. 1993, Landsea 1993, and Hebert et al. 1996). The normalized data also show that years with multi-billion dollar losses have been the norm rather than the exception.

In terms of the normalized data, in aggregate, hurricanes caused >$339 billion in losses over 71 years, or an annual average of about $4.8 billion, with a maximum of >$74 billion in 1926 and numerous years with no reported damage.(10) Note that the annual average is significantly higher than the $2 billion per year reported in Landsea (1993) and (Hebert et al. 1996). Of the 71 years, 35 years (about 50%) had less than $1 billion in damages. There were 19 years (about 25%) with at least $5 billion and 13 years (about 18%) with at least $10 billion. From this analysis, all else being equal, each year the U .S. has at least a 1 in 6 chance of experiencing losses related to hurricanes of at least $10 billion (in normalized 1995 dollars). Of course, in particular years climate patterns can significantly alter these odds (Gray et al. 1997), and in every ye ar beyond 1995 the stakes rise due to inexorable coastal population growth and development. Table 4 shows the breakdown of storms by decade and by the amount of damage caused. It shows that the 1940s had 8 years with more than a billion in damages, as compared to the 1980s with only 3. Perhaps more importantly, it shows that the 1940s-1960s had 7 years of greater than $10 billion in damages, as compared with 1 in the 1970s and 1 in the 1980s. Through 1995, the 1990s have unfolded more like the 1940s than the 1980s. However, it doe s seem that the United States has been fortunate with respect to the more extreme losses from the standpoint of relatively few hurricanes making landfall during the recent period of greatest development. Table 5 shows the summary results, broken down by category of storm. Table 6 further breaks down the data by segregating losses according to the population of the county in which the storm made landfall.

The intense hurricanes (Saffir-Simpson 3, 4, and 5) make up only about 21% of U.S. landfalling tropical cyclones, yet account for about 83% of the normalized damage. This is a substantially higher percentage of the damage than reported by Landsea (1993), which utilized only inflation and coastal county population changes. The study by Landsea took place prior to Andrew's landfall in 1992. The 52 intense hurricanes that struck the United States from 1925-1995 resulted in an average of $5.5 billion in damages per storm.

The 30 storms with the greatest losses over the 71 year period are shown in Table 7. Figure 5 shows the tracks of these 30 storms. Hurricane Andrew (1992) is second to another Category 4 storm which made landfall just to the north in 1926, causing >$63 billion in damages, and then made a second landfall as a C ategory 3 storm on the Florida and Alabama Gulf coasts, causing >$9 billion in damages. Third on the list is a 1944 South Florida storm (Category 3), followed by the New England Hurricane of 1938 (Category 3), both causing >$16 billion in losses.

If one uses actual coastal county population changes and conservatively assumes a 2% increase per year in combined inflation and wealth, then the list of great hurricane losses can be extended back to include three Category 4 storms which made landfall in Texas in 1900, 1915, and 1919 (Table 8). Two of these storms made landfall in the Galveston area (1900, 1915) and would appear as third and fourth on the revised list with >$26 billion and >$22 billion in losses, respectively. The 1919 storm would have resulted in >$5 billion in losses , placing it at 22nd on the revised list.


The normalized data indicate clearly that the United States has been fortunate in recent decades with regard to storm losses as compared with earlier decades. The data further refute recent claims that the rapid increase in non-normalized damages are due to climatic changes (cf. Changnon et al. 1997). When inflation, wealth, and population changes are taken into account, instead of increases, normalized damages actually decreased in the 1970s and 1980s. The 1990s, so far, are more akin to the normalize d damages that occurred during the 1940s and 1960s, and are by no means unprecedented. Thus, caution is urged in interpreting statements regarding the increasing number of "billion-dollar losses" or other such measures (e.g., Flavin 1994). With respect to hurricanes, the clearest evidence for increases in losses is changes in society, not in climate fluctuations. Indeed, a climate signal is present in the normalized data, and this is of decreased impacts in recent decades.

If the normalization methodology produces valid results, the data provide some evidence at a very general level that numbers generated through the more complex catastrophe models are reliable. This would lead to the conclusion that it is only a matter of time before the nation experiences a $50 billion or greater storm, with multi-billion dollar losses becoming increasingly frequent. Climate fluctuations which return the Atlantic basin to a period of more frequent storms will enhance the chances that th is time occurs sooner, rather than later.(11)

Acknowledgments. We would like to acknowledge useful comments and suggestions from Marilyn Baker, Bob Burpee, Stan Changnon, Bill Gray, Paul Hebert, Jerry Jarrell, Bill Landsea, Donna Bahr-Landsea, Kathy Miller, Rade Thomas Muslin, Richard Roth, and Bob Sheets as well as those anonymous reviewers. Thanks also to John Cole at the Corpus Christi NWS Office for providing updated damage estimates from the 1932 hurricane. Thanks also to officials at Property Claims Services, Inc. for providing insu rance loss data. All errors in the text are the responsibility of the authors. This research was partially supported by a grant from the Bermuda Biological Research Station's Risk Prediction Initiative on the topic of interannual tropical cyclone variability. This paper was initially prepared for a Panel on Social, Economic, and Policy Aspects of Hurricanes at the 22nd Conference on Hurricanes an d Tropical Meteorology, 19-23 May, American Meteorological Society, Fort Collins, CO.


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Table 1. Damage Estimates in South Florida Associated with Hurricane Andrew. Current dollar estimates of $30 billion in damages directly related to Hurricane Andrew in south Florida. Original sources are located in Pielke (1995).

Type of Loss Amount ($billions)
Common insured private property 16.5
Uninsured homes 0.35
Federal Disaster Package 6.5
Public infrastructure
        State 0.050
        County 0.287
        City 0.060
        Schools 1.0
        Damages 1.04
        Lost Sales 0.48
Environment 2.124
Aircraft 0.02
Flood Claims 0.096
Red Cross 0.070
Defense Department 1.412
Total 30.0

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Table 2. Example of a Stage Damage Approach. Damages are adjusted to 1992 estimates based on inflation and increased housing density. Source: Englehardt and Peng (1996).

Name Location Date 1992 Damages
(US$ billion)
Unnamed/Cat. 4 Miami 1926 39
Andrew/Cat. 4 S. Florida 1992 24
Betsy/Cat. 3 S. Florida 1965 15
Donna/Cat. 4 Sombrero Key 1960 10
Unnamed/Cat. 4 Pompano Beach1947 9
Unnamed/Cat. 3Homestead 1941 5
Unnamed/Cat. 4 Palm Beach 1928 3.5
Cleo/Cat. 2 Miami 1964 2.7
Unnamed/Cat. 3Palm Beach1949 2.6
Inez/Cat. 1 S. Florida 1966 2.2

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Table 3. Example of a Simulation Approach. Hurricane Loss Projections for Category 4 or 5 Hurricanes at Key Points Along the Coastline. Source: Applied Insurance Research in IIPLR/IRC (1995).

Location Total Insured Loss
(Billions US$ '93)
5Miami, FL 52.5
5Ft. Lauderdale, FL51.9
5Galveston, TX42.5
5 Hampton, VA 33.5
5 New Orleans, LA25.6
4 Asbury Park, NJ52.3
4New York City, NY45.0
4 Long Island, NY40.8
4Ocean City, MD 20.1

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Table 4. Number of years with extremely high (> $1 billion, > $5 billion and > $10 billion) normalized damage amounts for each decade. The column at the far right presents the total normalized damage for that particular decade.

>$1 Billion >$5 Billion >$10 Billion Per Year ($Billions)

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Table 5. Summary of normalized 1995 damage amounts (in millions of U.S. dollars) for the U.S. tropical cyclones from 1925-1995. Categories of the storm are from the Saffir-Simpson Hurricane Scale (Simpson 1974). The parentheses indicate the numb er of each category that has struck the United States. "Potential Damage" provides a relative scale for the median damage amount given that a Category 1 hurricane is scaled as "1." Values in the brackets are likely not representative of the true amounts because of the very tiny sample (2) available.

Category of Storm Mean
Percent of
Total for
Each Storm
Trop. & Subtrop. (118) $59$0 0 2.0% 0.02%
Hurr. Cat. 1 (45)$624$331 8.30.18
Hurr. Cat. 2 (29)$698$33610 6.00.21
Hurr. Cat. 3 (40) $2,978$1,4125035.0 0.88
Hurr. Cat. 4 (10) $15,358$8,224250 45.24.52
Hurr. Cat. 5 (2)[$5,973][$5,973]5003.5 [1.75]

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Table 6. Same as in Table 5, except to categorize the median damage amounts by whether the tropical cyclone struck a coastal region that is currently (1995) sparely (less than 250,000 people), medium (250,000-1,000,000) and highly (greater than 1,000,000) populated.

of Storm
Median Damage by 1995 Population Values
0-250,000 250,000-1,000,000 >1,000,000
Trop. & Subtrop.$0$0 (60)$0 (37)$0 (20)
Hurr. Cat. 1$33$16 (21)$17 (15) $232 (9)
Hurr. Cat. 2$336$140 (10)$158 (7)$1,380 (12)
Hurr. Cat. 3$1,412$1,108 (13)$2,050 (12)$2,118 (15)
Hurr. Cat. 4$8,224$2,105 (2)$8,224 (4)$22,886 (4)
Hurr. Cat. 5[$5,973]$5,973 (2)-- (0)-- (0)

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Table 7. Top 30 Damaging Hurricanes - Normalized to 1995 dollars by inflation, personal property increases, and coastal county population changes. (1925-1995). Note that Andrew is no longer the most destructive hurricane on record. Also note th at 23 of the top 30 destructive hurricanes were intense hurricanes of Saffir-Simpson scale 3 or higher. Most of the very destructive Category 1 and 2 hurricanes caused their damage through rainfall-induced flooding, often well away from the coast.

US Billion$
1. SE Florida/Alabama19264$72.303
2. ANDREW (SE FL/LA)1992433.094
3. SW Florida1944316.864
4. New England1938316.629
5. SE Florida/Lake Okeechobee1928413.795
6. BETSY (SE FL/LA)1965312.434
7. DONNA (FL/Eastern U.S.)1960412.048
8. CAMILLE (MS/LA/VA)1969510.965
9. AGNES (NW FL, NE U.S.)1972110.705
10.DIANE (NE U.S.)1955110.232
11.HUGO (SC)198949.380
12.CAROL (NE U.S.)195439.066
13.SE Florida/Louisiana/Alabama19474 8.308
14.CARLA (N & Central TX)196147.069
15.HAZEL (SC/NC)195447.039
16.NE U.S.194436.536
17.SE Florida194536.313
18.FREDERIC (AL/MS)197936.293
19.SE Florida194935.838
20.ALICIA (N TX)198334.056
21.CELIA (S TX)197033.338
22.DORA (NE FL)196423.108
23.OPAL (NW FL/AL)199533.000
24.CLEO (SE FL)196422.435
25.JUAN (LA)198512.399
26.AUDREY (LA/N TX)195742.396
27.KING (SE FL)195032.266
28.SE Florida/Georgia/S. Carolina194722.263
29.SE Florida193522.191
30.ELENA (MS/AL/NW FL)198532.064

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Table 8. Top 30 Damaging Hurricanes - Normalized to 1995 dollars by inflation, personal property increases, and coastal county population changes (1900-1995). If one uses actual county population changes and assumes a 2% increase per year in the combined GNP inflation and personal property index back to 1900, then this list can be supplemented by destructive hurricanes in 1900, 1915 and 1919 (These hurricanes are noted by asterisks in the table.):

US Billion$
1. SE Florida/Alabama19264$72.303
2. ANDREW (SE FL/LA)1992433.094
3. *N Texas (Galveston)1900426.619
4. *N Texas (Galveston)1915422.602
5. SW Florida1944316.864
6. New England1938316.629
7. SE Florida/Lake Okeechobee1928413.795
8. BETSY (SE FL/LA)1965312.434
9. DONNA (FL/Eastern U.S.)1960412.048
10. CAMILLE (MS/LA/VA)1969510.965
11. AGNES (NW FL, NE U.S.)1972110.705
12. DIANE (NE U.S.)1955110.232
13. HUGO (SC)198949.380
14. CAROL (NE U.S.)195439.066
15. SE Florida/Louisiana/Alabama194748.308
16. CARLA (N & Central TX)196147.069
17. HAZEL (SC/NC)195447.039
18. NE U.S.194436.536
19. SE Florida194536.313
20. FREDERIC (AL/MS)197936.293
21. SE Florida194935.838
22. *S Texas191945.368
23. ALICIA (N TX)198334.056
24. CELIA (S TX)197033.338
25. DORA (NE FL)196423.108
26. OPAL (NW FL/AL)199533.000
27. CLEO (SE FL)196422.435
28. JUAN (LA)198512.399
29. AUDREY (LA/N TX)195742.396
30. KING (SE FL)195032.266

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Footnote 1

The term "hurricane" is used throughtout the paper as a generic term to include sub-tropical storms, tropical storms, and hurricanes (Landsea 1993).

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Footnote 2

References to primary sources can be found in Landsea (1991) for 1949-1989 and Pielke and Pielke (1997) for 1981-1996.

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Footnote 3

In 1992, Robert Sheets, then director of the National Hurricane Center, stated prior to hurricane Andrew's impact, before a Congressional committee that the 1926 Miami hurricane would likely result in up to $35 billion in damages, but that many thought th is number too high (Sheets 1992).

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Footnote 4

The data are provided through 1989. For the period 1990-1995 we assume a constant annual increase in wealth equal to the average annual increase from 1980-1989.

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Footnote 5

Because the U.S. Census is taken every ten years, we have interpolated to estimate population for particular years.

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Footnote 6

Five storms in particular caused most of their significant damage inland due to flooding: Diana (1955), Doria (1971), Agnes (1972), Eloise (1975), and Alberto (1994). We have used the inland county population data for these cases.

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Footnote 7

We utilized the county(ies) that Jarrell et al. (1992) listed with the highest category of impact for each storm. Some hurricanes affected with the highest category just one county (e.g., Andrew (1992), Dade County), while others impacted many counties ( see the example of the New England 1938 hurricane).

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Footnote 8

The methodology is very sensitive to the accuracy of the reported damage at the time of the original event. Reliable data which show that past storms had a greater or lesser impact would alter our results.

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Footnote 9

Note that the normalized estimates are conservative because the adjustment for population neglects coastal development resulting from vacation properties.

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Footnote 10

We considered the possibility that a hurricane making landfall years ago in a remote, sparsely populated area might result in no reported damages, yet would cause significant damage today, especially if that region was now more populated. In the historic al record we identified 16 storms which made landfall with no reported damages. Of these, 12 were Category 1 storms which made landfall in regions which are only sparsely or moderately populated today, and thus would likely cause minimal damages. Four s torms, 1928, 1929, 1933, and 1939, made landfall as Category 2 storms, three in sparsely populated regions and one in a densely populated region. Based on our normalization, the three landfalls over sparse population would likely result in >$100 million and the one landfall over dense population would likely result in >$1 billion (dollar estimates from Table 5). The addition of these data would not significantly alter the summary data.

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Footnote 11

A companion paper (Landsea, Pielke, and Mestas-Nunez, submitted to Climatic Change) will put these results into the context of the observed variations of Atlantic basin and U.S. landfalling tropical cyclone frequencies.

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