Flood Damage >> Earthquake

Earthquake risk analysis requires measuring the likely damage, casualties, and costs of earthquakes within a specified geographic area over certain periods of time. A comprehensive risk analysis assesses various levels of the hazard, as well as the consequences to structures Earthquake and populations, should an event occur. Appendix A defines terminology related to risk analysis.

There are two types of risk analyses - probabilistic and scenario. This study uses a probabilistic, or statistical, hazard analysis to measure the potential effects of earthquakes of various locations, magnitudes, and frequencies. In contrast to a single, or scenario, earthquake of a specific size and location, probabilistic analyses allow for uncertainties Earthquake and randomness in the occurrences of earthquakes.

To estimate average annualized loss, a number of hazard and building structural characteristics were input to the HAZUS-MH earthquake model, as described in Table 2-1. Computing annualized earthquake loss, annualized earthquake loss ratios, and Earthquake annualized casualty, debris and shelter needs was a five step process. 

In the first step, the USGS earthquake hazard data were processed into a format compatible with HAZUS-MH. In the second step, Earthquake the building inventory in HAZUS-MH was used to estimate losses at the census tract level for specific return periods. Third, HAZUS-MH computed the AEL. 

Fourth, the annualized loss values were divided by building replacement values to determine the AELRs, Earthquake and in the final step, annualized casualty, debris and shelter estimates were computed. Each of the five steps is described in this section, with greater detail supplied in Appendix C.

Table 2-1. Hazard and Building Parameters Used in the Study Parameters Used in the Study Geotechnical Parameters NEHRP soil type 'D' (thick alluvium). 2002 USGS National Seismic Hazard Map ground motion parameters for eight return Earthquake periods between 100 and 2,500 years (100, 250, 500, 750, 1,000, 1,500, 2,000, and 2,500 years).

Ground motion parameters located at the census tract centroid. Ground-failure effects (liquefaction, landslide) were not included in the analyses due to the lack of a nationally applicable database Building Inventory Parameters Basis for general building inventory exposure: 2000 U.S. Census for Earthquake residential buildings, 2002 Dun & Bradstreet for nonresidential' buildings, and 2005 R.S. Means for all building replacement costs.

Building-related direct economic losses (structural and non-structural replacement costs, contents damage, business inventory losses, business interruption, and rental income losses), debris, shelter and casualties due to ground shaking were computed. All other economic losses were ignored due to the lack of a nationally applicable database.

STEP ONE: PREPARE PROBABILISTIC HAZARD DATA The primary source of earthquake hazard data used in this study are probabilistic hazard curves developed by the USGS. These Earthquake were processed for compatibility with HAZUS. 

The curves specify ground motion, such as peak ground acceleration (PGA) and spectral acceleration (SA), as a function of the average annual frequency that a level of motion will be exceeded in an earthquake. Examples of the Earthquake USGS probabilistic hazard curves are illustrated in Figure 2-1 that show conversely average annual frequency of exceedance as a function of PGA for single points in seven major U.S. cities.

The USGS has developed this Earthquake data for the entire U.S. (see http://earthquake.usgs.gov) as part of the National Earthquake Hazards Reduction Program (NEHRP). The curves were developed for individual points in a uniform grid that covers all 50 states and Washington, DC. A USGS map illustrating PGA for an average return period of 1,000 years is shown in Figure 2-2.

The USGS hazard curves were converted to a HAZUS-compatible database of probabilistic ground shaking values. Probabilistic hazard data for the PGA, spectral acceleration at 0.3 seconds (SA@0.3), and Earthquake spectral acceleration at 1.0 second (SA@1.0) were processed for each census tract for each of the eight different return periods. 

Figure 2-3 compares a HAZUS-MH seismic hazard (PGA) map for the 1,000-year return period for California to the USGS map for the same return period to illustrate that the Earthquake re-mapping process does not significantly affect the estimated losses where there is little exposure at risk. The analysis uses the 2002 USGS National Seismic Maps.

The USGS-computed ground motions apply to rock (B/C soil) and have been used to modify the Earthquake motions so they are applicable to a soil condition that, on average, is typical for populated metropolitan areas (D soil). STEP TWO: COMPUTE BUILDING DAMAGE AND LOSS

In the second step, HAZUS-MH was used to generate damage and loss estimates for the probabilistic ground motions associated with each of eight return periods. The building damage estimates were then used as the basis for computing direct economic losses. These include building repair costs, Earthquake contents and business inventories losses, costs of relocation, capital-related, wage and rental losses. 

The analyses were completed for the entire HAZUS-MH building inventory for each of the approximately 66,000 census tracts in the U.S. These Earthquake building-related losses serve as a reasonable indicator of relative regional risk, as described in greater detail in Appendix B.

Damage and economic losses to critical facilities, transportation and utility lifelines were not considered in this study. While it is understood that these losses are a component of risk, Earthquake they are not included because the inventory currently available at a national scale are not comprehensive enough to yield meaningful estimates.

For the loss estimates, the replacement value of the building inventory was estimated. A map illustrating replacement value of buildings (by county) is shown in Figure 2-4. For this study, the replacement value is based only on the value of the building components Earthquake and omits the land value and building contents. Building components include piping, mechanical and electrical systems, contents, fixtures, furnishings, and equipment.

The building data was combined at various levels to compare replacement value between different regions. For example, Earthquake Figure 2-5 compares the replacement value by state as a percentage of total replacement value for the United States. The building exposure data help to identify concentrations of replacement value and potential areas of increased risk.


In this step, the AEL was computed by multiplying losses from eight potential ground motions by their respective annual frequencies of occurrence, Earthquake and then summing the values. Several assumptions were made for this computation. First, the losses associated with ground motion with return periods greater than 2,500 years were assumed to be no worse than the losses for a 2,500-year event. 

Second, the losses for ground motion with less than a 100-year return period were assumed to be generally small enough to be negligible, Earthquake except in California, where losses from ground motion with less than a 100-year return period can account for up to an additional 15 percent of the overall statewide AEL estimate.


The AEL is an objective measure of risk, however, since risk is a function of the hazard, building stock, and Earthquake vulnerability, variation in any of these three parameters affects the overall risk at any one site. Understanding how the parameters influence risk is key to developing effective risk management strategies. 

To facilitate that understanding for regional comparisons, the AEL was normalized by the building inventory exposure to create a loss-to-value ratio, termed the AELR, Earthquake and expressed in terms of dollars per million dollars of building inventory exposure.

Between two regions with similar AEL, the region with the smaller building inventory typically has a higher relative risk, or AELR, than the region with a larger inventory, Earthquake since annualized loss is expressed as a fraction of the building replacement value. 

For example, while Charleston, South Carolina and Memphis, Tennessee have similar AELs (see Table 3.2), the former has a higher earthquake loss ratio, since Charleston has less building inventory and building replacement value. In other words, Earthquake while the seismic risk in Charleston and Memphis is roughly the same, a comparably sized earthquake would affect a significantly larger percentage of the building inventory in Charleston.


The HAZUS-MH software provides the capability to directly compute annualized casualty estimates. However, this automated capability does not exist for annualized debris and Earthquake shelter estimates. To generate these estimates, HAZUS-MH was run to produce debris and shelter estimates for all eight return periods. 

These results then were used as inputs in a separate database utility external to HAZUS-MH to compute the annualized debris and shelter estimates. The utility used the same algorithm used by HAZUS-MH to compute the annualized economic loss Earthquake and casualty estimates (described in Appendix C).

Casualties are estimated as a function of direct structural or non-structural building damage with the non-structural-related casualties derived from structural damage output. The HAZUS methodology is based on the correlation between building damage (both structural and nonstructural) and the number and Earthquake severity of casualties. 

This method does not include casualties that might occur during or after earthquakes that are not directly related to damaged buildings such as heart attacks, car accidents, mechanical failure from power outages, incidents during post-earthquake search and rescue, post-earthquake clean-up and construction, electrocution, tsunami, landslides, liquefaction, fault rupture, dam failures, fires or Earthquake hazardous materials releases. Psychological effects of earthquakes are also not modeled.

Debris is estimated using an empirical approach for two types of debris. The first is large debris, such as steel members or reinforced concrete elements of buildings, Earthquake that requires special handling to break them into smaller pieces before removal. The second type of debris is smaller and more easily moved directly with bulldozers and other machinery and tools, and includes bricks, wood, glass, building contents and other materials.

Two types of shelter needs are estimated: the number of displaced households and Earthquake the number of individuals requiring short-term shelter. Both are a function of the loss of habitability of residential structures directly from damage or from a loss of water and power. 

The methodology for calculating short-term shelter requirements recognizes that only a portion of displaced people will seek public shelter while others will seek shelter even though their residence may have no damage or Earthquake insignificant damage because of reluctance to remain in a stricken area.

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