Earthquake loss assessments methods - Comparison and new developments shown on past and future earthquake scenarios for Iran

Disaster preparedness and risk reduction is one of the most valuable research topics from both seismological and societal aspects to save lives. Scenario loss assessments help disaster managers to conceive an idea about what to expect and how to best prepare for the disaster. Prior to providing such scenario loss estimates, it is imperative to conduct an evaluation of the utilized program and its method. In the absence of information regarding the reliability of the assessments, an evaluation of potential future losses becomes challenging and even unreliable.

There are multiple tools available for rapid earthquake loss estimation purposes. 'Quake Loss Assessment for Response and Mitigation' (QLARM) is one of the few established programs, existing since 2002. It is a computer program used by the International Centre for Earth Simulation in Geneva, Switzerland, to issue timely reports on both building damage and human losses for potentially damaging earthquakes. QLARM uses 2013 population information for approximately 2 million settlements world-wide along with the building information initially taken from the World Housing Encyclopedia. These settlements are then classified into distributions of building vulnerabilities according to the six classes in EMS98 and seismic intensity fields are estimated for each earthquake to compute the expected losses. 'Loss-Calculator' on the other hand, is a new Python program that employs a different approach than QLARM. It uses detailed building-by-building information along with the population assigned to each building based on the buildings’ size and types. The buildings in the Loss-Calculator are classified into numerous classes, following the taxonomy of the Global Earthquake Model. Losses are computed based on ground-motion fields using standard intensity measures like peak ground acceleration (PGA) or spectral accelerations (SA).

We first evaluate the accuracy of both tools for several destructive past earthquakes to explore the uncertainty range of results and identify potentially necessary calibration or improvements in the input data, e.g. the earthquake shaking, population numbers, settlement and building locations. To focus on relevant events with a high death toll, we selected earthquakes in Iran, such as the Rudbar-Manjil (M_w=7.4) and the Qayen (M_w=7.3) earthquakes with fatalities of around 40,000 and 1,500, respectively. This comparison includes not only the absolute value of the losses, but also their detailed spatial distribution. We also compare the loss assessments using different possible inputs, such as different building information and intensity measure fields to achieve the goals.

Furthermore, we estimate losses expected to occur in possible future earthquakes by computing probable earthquake scenarios. These scenarios prove the need for serious disaster preparation and highlight the likely locations of largest losses or most affected people. We introduce high-resolution spatial distributions of losses for improved disaster preparedness planning and show how the detailed knowledge of building locations can improve loss assessments.