Natural disasters have become so expensive that the Federal Government shares in the economic risk. Forecasting this risk is therefore a significant issue.
Insurance coverage for losses resulting from natural disasters is typically less than 20 percent of the total loss, because of limited participation in voluntary insurance coverage. The remainder of the dollar losses are covered by the Federal Government through emergency allocations, the amount of which can increase the national debt. As a result of Hurricane Andrew, where the losses may exceed 25 billion dollars, the U.S. Congress is examining the feasibility of establishing a National "insurance" fund from which uninsured losses can be paid when natural disasters strike. Forecasts of future losses based on traditional interpretations of available data produce highly variable results and seemingly yield few patterns. A new method developed by USGS scientists addresses the issue of forecasting the size and number of national disasters and their attendant losses.
The U.S. Geological Survey (USGS) is a participant in the International Decade of Natural Disaster Reduction.
The 1990s are designated as the International Decade of Natural Disaster Reduction, and the U.S. is a signatory to the United Nations' treaty. Studies by USGS researchers contribute to this treaty by defining a quantitative basis for developing models for the loss of life and property resulting from natural disasters. This research is conducted cooperatively with Prof. Sarah Tebbens of the University of South Florida, and Prof. Donald Turcotte of Cornell University. Data from the USGS, the National Oceanic and Atmospheric Administration (NOAA), the Agency for International Development, and other agencies are used to develop an understanding of how a particular natural disaster scales, or relates, to other disasters caused by the same phenomenon, and to disasters caused by other phenomena. These relationships are fundamental to the development and evaluation of national disaster planning, mitigation, and hazard reduction efforts.
In this study, USGS scientists examine the magnitude of disasters as measured by dollars and fatalities, as well as by traditional scientific parameters.
Natural high-energy events, such as hurricanes and earthquakes, are complex phenomena whose cumulative size-frequency distributions exhibit fractal scaling properties; that is, plots of logarithms of the size and cumulative frequency data follow a straight line. The slope of this line is the scaling exponent or fractal dimension (D). Preliminary results of this research, funded by the USGS G.K. Gilbert Fellowship Program, suggest that the loss of life and property due to natural disasters exhibit self-similar scaling behavior. It is this self-similar scaling property that allows use of frequent small events to estimate the rate of occurrence of less frequent, larger events. Examining the fractal behavior of loss data for disasters of all scales has important advantages because one can forecast the probability of occurrence of a disaster over a wide range of years (1 year to 1,000 years); compare one type of disaster with another; compare disasters in one region with similar disasters in another region; and, measure the effectiveness of planning and mitigation strategies.
The fractal behavior of hurricanes provides a basis for estimating their size and number.
Of all the natural disasters in the United States, hurricanes account for about two-thirds of the insured property losses. Results of analyses give characteristic fractal-scaling values that reveal two populations of storms: those with sustained wind speeds below about 85 knots, or tropical storms; and those with sustained wind speeds above 85 knots, or hurricanes. The fractal-scaling law can be used to make a probabilistic forecast of the frequency of hurricanes of any given size for a city or a region. A typical example is that for the region around Tampa Bay, Florida.
USGS studies indicate that life and property losses from earthquakes, hurricanes, floods, and tornadoes exhibit fractal scaling behavior which can be used to forecast future losses.
Earthquakes are examples of complex natural high-energy phenomena whose cumulative size-frequency distributions have long been known to exhibit fractal (power-law) scaling properties. USGS researchers have recently discovered that fractal scaling laws also apply to distributions of the loss of life and property brought on by natural disasters. Fatality data from countries with large earthquake losses during the 20th century demonstrate power-law scaling over 3 to 4 orders of magnitude in loss. These relationships provide a quantitative basis to compare losses from different geographic regions, and different time periods. The self-similar scaling properties of power-law distributions allow forecasting of larger events from the behavior of smaller events, as well as comparison of losses from other types of natural disasters. Not all disasters have the same impact. USGS researchers conclude that on an annual basis in the United States, the majority of small fatality events (10 per event) are related to floods and tornadoes; larger fatality events (1000 per event), are less frequent and are dominated by hurricanes and earthquakes. Disaster mitigation strategies need to account for these differences.