Abstract
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.
Conclusions
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.
REFERENCES
Banham, R., 1993. Reinsurers seek relief in computer predictions. Risk Management, (August)
14-19.
BEA (Bureau of Economic Analysis) 1993. Fixed Reproducible Tangible Wealth in the United
States, 1925-1989, January, US GPO: Washington, DC.
Behn, R. D. and J. W. Vaupel 1982. Quick Analysis for Busy Decision Makers, Basic Books:
New York.
BTFFDR (Bipartisan Task Force on Funding Disaster Relief) 1995. "Federal Disaster
Assistance," U.S. Senate, 104-4.
Changnon, S.A. (ed.) 1996. The Great Flood of 1993: Causes, Impacts, and Responses
(Westview Press: Boulder, CO).
Changnon, S. A., D. Changnon, E. R. Fosse, D. C. Hoganson, R. J. Roth, Sr., and J. M. Totsch,
1997. Effects of Recent Weather Extremes on the Insurance Industry: Major Implications for the
Atmospheric Sciences, Bulletin of the American Meteorological Society, 78:425-431.
Dlugolecki, A. F. et al., 1996. Chapter 17, Financial Services, in Climate Change 1995: Impacts,
Adaptations and Mitigation of Climate Change: Scientific - Technical Analyses. Contribution of
Working Group II to the Second Assessment Report of the IPCC (Intergovernmental Panel on Climate Change) (Cambridge University Press: London).
Englehardt, J and C. Peng, 1996. A Bayesian benefit-risk model applied to the south Florida building code. Risk Analysis, 16(1):81-91.
Flavin, C. 1994. Storm Warnings, Worldwatch. 7(6):10-20.
Gray, W. M., J. D. Sheaffer, and C. W. Landsea, 1997: Climate trends associated with multidecadal variability of Atlantic hurricane activity, pp. 15-53 in Hurricanes, Climate and Socioeconomic Impacts, edited by H. F. Diaz and R. S. Pulwarty, S
pringer: Berlin.
Hebert, P. J., J. D. Jarrell and M. Mayfield, 1996. The Deadliest, Costliest, and Most Intense United States Hurricanes of this Century (And Other Frequently Requested Hurricane Facts) NOAA Technical Memorandum NWS NHC-31 (February). Coral Ga
bles, FL: NHC.
IIPLR/IRC (Insurance Institute for Property Loss Reduction and Insurance Resource Council), 1995. Coastal Exposure and Community Protection: Hurricane Andrew's Legacy, April, Boston, MA.
Jarrell, J. D., P. J. Hebert and M. Mayfield, 1992. Hurricane Experience Levels of Coastal County Populations From Texas to Maine. NOAA Technical Memorandum NWS NHC-46 (August). Coral Gables, FL: NHC, 152 pp..
Landsea, C. W., 1993. A climatology of intense (or major)
Atlantic hurricanes. Monthly Weather Review,
121:1703-1713.
Landsea, C. W. 1991. West African Monsoonal Rainfall and Intense Hurricane Associations, Colorado State University Department of Atmospheric Science, Paper No. 484, Fort Collins, CO.
Landsea, C. W., N. Nicholls, W. M. Gray, L. A. Avila, 1996. Downward trends in the frequency of intense Atlantic hurricanes during the past five decades, Geophysical Research Letters, 23:1697-1700.
Neumann, C. J, B. R. Jarvinen, C. J. McAdie, and J. D. Elms, 1993 (fourth revision). Tropical Cyclones of the North Atlantic Ocean, 1871-1992, NCDC/NHC Historical Climatology Series 6-2, Asheville, NC.
Newsweek 1996. January 21 issue.
Noonan, B., 1993. Catastrophes: The new math. Best's Review, (August) 41-83.
Patton, C.V. and D. S. Sawicki 1986. Basic Methods of Policy Analysis and Planning. Prentice hall: Englewood Cliffs, NJ.
Pielke, Jr., R. A. 1995, Hurricane Andrew in South Florida: Mesoscale Weather and Societal Responses. National Center for Atmospheric Research: Boulder, Colorado.
Pielke, Jr., R. A. 1997. Reframing the U.S. hurricane problem, Society and Natural Resources, 10:485-499.
Pielke, Jr., R A. and R. A. Pielke, Sr. 1997. Hurricanes: their nature and impacts on society, John Wiley: Chichester.
Sheets, R., 1992. Statement before Hearing of the Senate Committee on Banking, Housing, and
Urban Affairs, Subcommittee on Housing and Urban Affairs, hearing on "The National Flood Insurance Reform Act of 1992," 27 July, 102-913.
Simpson, R. H., 1974. The hurricane disaster potential scale, Weatherwise 27:169 and 186.
West, C. and D. Lenze, 1994. Modeling the regional impact of natural disaster and recovery: A general framework and an application of Hurricane Andrew. International Regional Science Review, 17(2):121-150.
U.S. White House, 1950. Economic Report of the President. Washington, DC: US GPO.
U.S. White House, 1996. Economic Report of the President. Washington, DC: US GPO.
TABLES
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).
