Parcel-Scale Earthquake Loss Estimation with HAZUS: A Case Study in Salt Lake County, Utah

By Moffatt, Stuart F.; Cova, Thomas J. | Cartography and Geographic Information Science, January 2010 | Go to article overview

Parcel-Scale Earthquake Loss Estimation with HAZUS: A Case Study in Salt Lake County, Utah


Moffatt, Stuart F., Cova, Thomas J., Cartography and Geographic Information Science


Introduction

Earthquake loss estimation studies are typically performed at a regional scale, using counties or tracts because accurate hazard and building data at finer scales are difficult to obtain (Whitman et al. 1997; FEMA 2003). Outputs from these studies generally include physical and socio-economic loss estimates which provide city, county, or state emergency managers with an understanding of the impact of earthquakes on their jurisdictions, with an accepted probability of error and uncertainty. This can offer valuable guidance for decisions regarding preparation, mitigation, response, and recovery. However, regional earthquake impact studies are not able to produce results for individual parcels where finer-grained methodologies are necessary to account for unique building characteristics (Aslani and Miranda 2005). While costly building-specific studies may be possible for owners of large facilities, private homeowners are left to make mitigation decisions with limited knowledge of potential losses (Kunreuther 2001).

The objective of this study is to produce structural loss estimates for single-family residences due to earthquake hazards, using Salt Lake County as a case study. Issues of resolution and associated detail for both earthquake hazard and building inventory data are addressed. The Hazards U.S. (HAZUS-MH or, simply, HAZUS) loss estimation software and methodology from the Federal Emergency Management Agency (FEMA) was selected for this study. In particular, the study leveraged the functionality of the Advanced Engineering Building Module (AEBM) in HAZUS.

The central question of this research is: How much does the precision of earthquake hazard and building inventory attributes affect the outcome of building-specific earthquake loss estimation? The attributes for earthquake hazard are peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration at 1.0 sec (SA10), and spectral acceleration at 0.3 sec (SA03). These attributes are collectively referred to as "PESH"--potential earth science hazards--in the literature and this study. The attributes for building inventory are location, tax assessor value, and building classification. Property values and secondary structures are not included.

The next section comprises a brief literature review on earthquake loss estimation. The following section outlines methods for parcel-level analyses. The results and discussion are presented in the next two sections, and the paper ends with concluding statements.

Background

Earthquake loss estimation research has experienced major advancements over the past several decades (Calvi et al. 2006). With the introduction of desktop computer capability in general, and geographic information systems (GIS) specifically, there has been an increased emphasis on a higher level of detail (using local data) to improve the accuracy of loss estimates (Chang et al. 9000; Grossi 2000; Shinozuka et al. 1997). The field spans many disciplines, including engineering, physical and social science, and public/private insurance (Kunreuther 2001). Detailed methodologies now exist to characterize earth science hazards and physical and socioeconomic losses, which provides a more accurate picture of the potential impact of earthquakes on the human environment (Whitman et al. 1997).

Current approaches depend on geographic input variables including earthquake hazard values, building inventories, transportation networks, utility lifelines, and population demographics (FEMA 2003). Different data collection techniques exist to augment default HAZUS inputs. For instance, building inventories can be obtained through rapid visual screening inspections (McCormack and Rad 1997) or from tax office records (Emmi and Horton 1993). Ground-shaking variables can be computed within HAZUS, or experts in seismology can provide GIS-based hazard maps (FEMA 2003). Likewise, output variables include probabilities for several measurements: structural and nonstructural damage states, direct and indirect economic losses, and shelter and casualties (Kircher et al. …

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