HAZUS at Craiova Coding

HAZUS is loss estimation software created with funding from the US Federal Emergency Management Agency. The name is short for HAZards U.S. and the software estimates damage from earthquakes. HAZUS uses a geographic information system and explicit inventory of buildings and people to model losses caused by scenario earthquake events. Brian has been using HAZUS since March 1999 and he occasionally instructs E307 Basic HAZUS at FEMA's Emergency Management Institute in Emmitsburg, Maryland.

In support of San Jose State University's Collaborative for Disaster Mitigation, Brian Quinn ran HAZUS models of seven M6.8 earthquake scenarios in May and June 2001. The scenarios focus intensively on the ground motion in the San Jose, California metropolitan area and were processed with ortho-imagery to help answer the common questions from business planners and many workers. "Where is my house?" "Where is my business?" "What about my commute to work?"

For purposes of this study, the segments of each fault or lineament-based scenario were chosen to be as close to the San Jose metropolitan area as possible. By that criterion, the northern San Gregorio fault segment and southern Greenville fault segements were chosen for this study's HAZUS scenarios. The 1Hz spectral velociy pattern for the northern San Gregorio fault M 6.8 scenario is shown below where the scenario source is displayed as a black-and-white dashed line near the western coastline. HAZUS estimated a fairly intense 31inches/second (101 cm/sec) horizontal velocity near Foster City for this scenario.

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San Gregorio M 6.8 SV 1 hz

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By contrast, the southern Greenville fault M 6.8 scenario source, shown in the northeastern part of the map as a black-and-white dashed line, would be expected to produce a maximum 1Hz spectral velocity of less than 18 in/sec (60 cm/sec), in the most susceptible bay muds near Milpitas.

Greenville M 6.8 SV 1 Hz

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HAZUS ground motion also estimates spectral acceleration (SA). For the Greenville and San Gregorio scenarios, the following maps compare 3 Hz SA for the two weakest scenarios in this study.

San Gregorio M 6.8Greenville M 6.8 SA 3 Hz

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For this study, ground motion was estimated at 35-meter gridding and merged with 5-meter sampling of USGS orthophoto quarter quadrangle imagery. A recently released USGS liquefaction susceptibility map was used to control HAZUS estimates of peak ground displacement due to liquefaction. Only the very detailed soils information included in USGS Open File Report 00-444 has made this very detailed estimate possible. For the Greenville fault scenario, the following image conveys the pattern of estimated peak ground displacement (in the hardest-hit locations, not everywhere!) The imagery was only mosaicked in the square inset area shown in the previous four maps. For reference, the southernmost part of South San Francisco Bay, California, is visible in the northwest corner of the image-merged study area.

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The seven scenarios were chosen to examine the sensitivity of the Silicon Valley region loss estimate to offset from the M6.8 event, as well as the size of the HAZUS study region over which the loss was modeled. At the lower limits of loss, the San Gregorio fault, near the San Mateo County coastline, and the Greenville fault, in far eastern Alameda County, showed the following loss patterns highligted by the red arrows in the four graphs below. Estimated deaths were 20 to 80, only 1 to 3 megatons of destroyed building debris, less than 5000 displaced households, and less than 5 billion dollars total loss, including both direct loss to buildings and indirect loss due to business interruption. The larger study region's losses are shown with the darker line, and the losses estimated for the image-merged detail area around San Jose is shown with the ligher line. For most scenarios, the smaller image-merged study region shows lower losses. In the most destructive scenario, however, an area in the southwest part of the image-merged study region that is not included in the larger study region was damaged enough to model greater losses in the smaller study region than in the larger region.

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