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Flood Losses

Compilation of Flood Loss Statistics

The flood loss information provided below can only be considered approximate. There is no one agency in the United States with specific responsibility for collecting and evaluating detailed flood loss information. The National Weather Service (NWS), through its numerous field offices provides loss estimates for significant flooding events. However, this task is ancillary to the prime mission of the NWS which is to provide forecasts and warnings of hydrometeorological events. The Agency's focus is on predicting the events that lead to death and damage, rather than on an assessment of the consequences of the events it predicts. Because of this, the quality of resulting flood loss estimates may be uneven, depending on other operational constraints at a particular field offices.

Accurate flood loss estimates would require a concerted effort, based on the availability of substantial resources. There is no central clearinghouse to report flood losses. Our societal infrastructure almost guarantees poor estimates. State and municipal losses are often self-insured. Some portion of the cost to repair a washed out road or bridge might be covered in a budget line item for routine maintenance. Another portion may be financed by a separate line item in the next year's budget. In some cases, a structure may be replaced by one of higher quality, costing more than the replacement value or repair costs of the original structure. Finally, for situations where a governmental entity (city, county, state, etc.) carries no third party insurance, it may decide to forgo some repairs.

For homeowners and businesses, some will either not have insurance or be under insured. The costs for this sort of repair is almost impossible to establish. For those that are insured, claims may not fully reflect actual losses. Agricultural losses are also hard to accurately estimate.

Loss/damage estimates are reported in many different ways. Totals are may be available on state and county levels. Depending on who is providing them, they may not comprehensively include all damages. In addition, industry-wide estimates (e.g., river transportation/barges, railroads, etc.) covering multiple states are often available. Funding and aid supplied by various agencies of the Federal government may also provide information on losses (e.g., FEMA, Dept. of Agriculture, Small Business Administration, etc.) over a region. Often, there is usually not enough information to easily determine the degree of overlap among these various sources of loss estimates. Flood losses that "fall between the cracks" of the current system could, however, compensate for possible "double counting."  Unfortunately, there is usually no easy way to reconcile information from different reporting systems.

Finally, deciding what constitutes a loss is not always as simple as it might seem. Certainly the capital cost to repair or replace a bridge that has been washed out is easily identified as a loss. However, if flooding prevents a farmer from planting a crop, what is the value of the loss. The farmer may not have experienced a loss literally, since he did not plant a crop and did not lose the crop but he may have been denied potential income. What if he/she planted later in the season and had reduced yield because of a shorter growing period or because he/she chose to plant a lower-profit crop? How is this loss calculated?

In another example, what about the barge operator or the business owner who has to cease operations? In addition to the business owner's repair costs there are his lost income, and the lost income of his employees who may be laid off. In order to make this wide range of economic impacts due to flooding tractable, loss statistics can be partitioned into direct and indirect damages. Direct damages are the costs to repair such things as damaged buildings, washed out railroad beds, bridges, etc. Indirect damages include such categories as lost wages because of business closures. There is no universally agreed upon demarcation between what constitutes a direct and an indirect loss.

The above factors only highlight some of the more significant impediments to accurate determination of flood losses. In the case of NWS loss estimates, what is included is the "best estimate" of direct damages due to flooding that results from rainfall and/or snowmelt. It does not include flooding due to winds, such as coastal flooding (e.g., hurricane storm surges). Because of the complexity of the problems and the limited resources available for extensive evaluation of the quality of the data, the estimates provided here should only be considered approximate.

In the table below, the data are for water years, starting in October and ending in September.  For example, Water Year 1993 starts on October 1, 1992, and ends on September 30, 1993. The quality of the older data is subject to some question. The more recent data are generally more reliable, but while the damage amounts for individual years are not precise, they provide reasonable indications of relative changes over time.

The damage figures in the second column are in thousands of dollars. The second column provides "unadjusted" damage amounts. That is, the damage as reported in the year it occurred, not adjusted for inflation. The third column is a Construction Cost Index, used to adjust for inflation. The next column to the right is the adjustment factor applied to the unadjusted estimates to get the column damages estimates "adjusted" to 1997 dollars.  Please note that the last column is reported in billions of dollars.  The data are also provided in graphical form.

 

Year Unadjusted
Damages
(K)
CCI
Index
Adjustment
Factor
Adjusted
Damages
(Billion)
1903 $53,116 95 64.6 $3.430
1904 $6,545 95 64.6 $0.423
1905 $11,000 95 64.6 $0.710
1906 $400 95 64.6 $0.026
1907 $15,576 101 60.7 $0.946
1908 $10,250 97 63.2 $0.648
1909 $49,134 91 67.4 $3.312
1910 $21,239 96 63.9 $1.357
1911 $7,772 93 66.0 $0.513
1912 $77,586 91 67.4 $5.230
1913 $171,387 100 61.3 $10.513
1914 $17,951 89 68.9 $1.237
1915 $14,131 93 66.0 $0.932
1916 $26,124 130 47.2 $1.233
1917 $27,330 161 38.1 $1.041
1918 $7,867 189 32.5 $0.255
1919 $3,164 198 31.0 $0.098
1920 $24,771 251 24.4 $0.605
1921 $28,647 202 30.4 $0.870
1922 $52,060 174 35.2 $1.835
1923 $52,905 214 28.7 $1.516
1924 $16,979 215 28.3 $0.484
1925 $9,923 207 29.6 $0.294
1926 $23,468 208 29.5 $0.692
1927 $347,656 206 29.8 $10.352
1928 $44,611 207 29.6 $1.322
1929 $68,098 207 29.6 $2.018
1930 $15,850 203 30.2 $0.479
1931 $2,808 181 33.9 $0.095
1932 $10,295 157 39.1 $0.402
1933 $36,679 170 36.1 $1.323
1934 $10,362 198 31.0 $0.321
1935 $127,127 196 31.3 $3.979
1936 $282,549 206 29.8 $8.413
1937 $440,730 235 26.1 $11.504
1938 $101,098 236 26.0 $2.628
1939 $13,834 236 26.0 $0.360
1940 $40,467 242 25.4 $1.026
1941 $39,524 258 23.8 $0.940
1942 $98,507 276 22.2 $2.189
1943 $199,732 290 21.2 $4.225
1944 $101,079 299 20.5 $2.074
1945 $165,796 308 19.9 $3.302
1946 $70,813 246 24.9 $1.766
1947 $272,328 413 14.8 $4.045
1948 $229,959 461 13.3 $3.060
1949 $93,931 477 12.9 $1.208
1950 $176,050 510 12.0 $2.117
1951 $1,028,741 549 11.2 $11.494
1952 $254,064 569 10.8 $2.739
1953 $122,204 600 10.2 $1.249
1954 $106,842 626 9.80 $1.047
1955 $995,491 660 9.29 $9.252
1956 $64,688 692 8.86 $0.573
1957 $360,303 724 8.47 $3.053
1958 $218,255 759 8.08 $1.764
1959 $141,255 797 7.70 $1.087
1960 $92,976 824 7.44 $0.692
1961 $154,033 847 7.24 $1.116
1962 $75,237 872 7.03 $0.529
1963 $177,946 901 6.81 $1.211
1964 $651,642 936 6.55 $4.270
1965 $788,046 971 6.32 $4.978
1966 $117,004 1019 6.02 $0.704
1967 $375,218 1074 5.71 $2.143
1968 $339,399 1155 5.31 $1.802
1969 $902,654 1269 4.83 $4.363
1970 $225,453 1381 4.44 $1.001
1971 $287,525 1581 3.88 $1.116
1972 $4,465,135 1753 3.50 $15.624
1973 $1,894,493 1895 3.24 $6.132
1974 $576,203 2020 3.04 $1.750
1975 $1,373,269 2212 2.77 $3.808
1976 $3,000,000 2401 2.55 $7.664
1977 $1,300,000 2576 2.38 $3.096
1978 $700,000 2776 2.21 $1.547
1979 $3,500,000 3003 2.04 $7.149
1980 $1,500,000 3237 1.89 $2.842
1981 $1,000,000 3535 1.74 $1.735
1982 $2,500,000 3825 1.60 $4.009
1983 $4,000,000 4066 1.51 $6.034
1984 $3,750,000 4146 1.48 $5.548
1985 $500,000 4195 1.46 $0.731
1986 $6,000,000 4295 1.43 $8.569
1987 $1,444,199 4406 1.39 $2.011
1988 $225,298 4519 1.36 $0.306
1989 $1,080,814 4615 1.33 $1.437
1990 $1,636,431 4732 1.30 $2.121
1991 $1,698,781 4835 1.27 $2.155
1992 $762,762 5004 1.23 $0.935
1993 $16,370,010 5162 1.19 $19.452
1994 $1,120,309 5320 1.15 $1.292
1995 $5,110,829 5471 1.12 $5.730
1996 $6,121,884 5596 1.10 $6.710
1997 $8,730,407 5847 1.05 $9.159
1998 $2,496,960 5944 1.03 $2.577
1999 $5,455,263 6134 1.00 $5.455

 


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Last Modified: March 08, 2000