<p> FEMA’s <i>Hazus Earthquake Model</i> (FEMA, 2018) is used frequently in the United States for scenario driven catastrophic planning, prioritizing mitigation funding, and disaster response situational awareness. The Hazus Earthquake Model requires a significant array of data for conducting earthquake loss estimations in terms of population demographics, building construction proxies, built environment estimates, critical facility locations, building infrastructure, and a variety of engineering and analysis parameters. A basic baseline of these required inputs is provided with Hazus for all 50 U.S. States, the District of Columbia, and the 5 U.S. territories. These default baseline datasets and engineering/analysis parameters however are somewhat rudimentary, and the incorporation of local data greatly increases results. Hazus enables the United States natural hazards risk assessment community to quantify risk using a robust suite of analysis options. By adapting the <i>Hazus Earthquake Model</i> for a country outside of the United States, risk management professionals across the globe can leverage the significant financial investment and over 20 years of software and scientific methodology investments undertaken by the U.S. government for their communities. While the methods and models are transferable internationally, the limited available data upon which the model is reliant minimizes its application outside of the U.S. Many global population exposure, building, and construction practice datasets do exist that could be leveraged to enable regional Hazus Earthquake modeling capability globally and a methodology for integrating these datasets into the software can facilitate technology transfer for supporting risk reduction. By conducting a quantitative comparison between modeled and actual losses from the 2015 Gorkha (Nepal) Mw 7.8 earthquake, this study aims to answer whether FEMA’s U.S.-centric Hazus Earthquake Model can be adapted for Nepal to estimate building damages, injuries, and fatalities using global exposure datasets.</p><p>
Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10979097 |
Date | 28 November 2018 |
Creators | Rozelle, Jesse Ryan |
Publisher | University of Colorado at Denver |
Source Sets | ProQuest.com |
Language | English |
Detected Language | English |
Type | thesis |
Page generated in 0.0017 seconds