scholarly journals Disaster Risk Analysis Part 2: The Systemic Underestimation of Risk

Author(s):  
Aaida A. Mamuji ◽  
David Etkin

Abstract How risk is defined, the nature of methodologies used to assess risk, and the degree to which rare events should be included in a disaster risk analysis, are important considerations when developing policies, programs and priorities to manage risk. Each of these factors can significantly affect risk estimation. In Part 1 of this paper [Etkin, D. A., A. A. Mamuji, and L. Clarke. 2018. “Disaster Risk Analysis Part 1: The Importance of Including Rare Events.” Journal of Homeland Security and Emergency Management.] we concluded that excluding rare events has the potential to seriously underestimate the cumulative risk from all possible events,For example, of the 100 most expensive weather disasters in the US, the single most expensive event accounts for 16% of total economic impacts. Similarly, the worst explosion disaster accounts for 17% of the fatalities of the total 100 worst events. though including them can be very challenging both from a methodological and data availability perspective. Underestimating risk can result in flawed disaster risk reduction policies, resulting in insufficient attention being devoted to mitigation and/or prevention. In Part 2, we survey various governmental emergency management policies and methodologies in order to evaluate varying equations used to define risk, and to assess potential biases within disaster risk analyses that do comparative risk ranking. We find (1) that the equations used to define risk used by emergency management organizations are frequently less robust than they should or are able to be, and (2) that methodologies used to assess risk are often inadequate to properly account for the potential contribution of rare events. We conclude that there is a systemic bias within many emergency management organizations that results in underestimation of risk.

Author(s):  
David A. Etkin ◽  
Aaida A. Mamuji ◽  
Lee Clarke

Abstract Rare events or worst-case scenarios are often excluded from disaster risk analysis. Their inclusion can be very challenging, both from methodological and data availability perspectives. We argue that despite these challenges, not including worst-case scenarios in disaster risk analysis seriously underestimates total risk. It is well known that disaster data sets generally have fat tails. In this paper we analyze data for a number of disaster types in order to empirically examine the relative importance of the few most damaging events. The data show consistent fat-tail trends, which suggests that rare events are important to include in a disaster risk analysis given their percentage contributions to cumulative damage. An example of biased risk estimation is demonstrated by a case study of risk analysis of tanker spills off the western coast of Canada. Incorporating worst-case scenarios into disaster risk analysis both reduces the likelihood of developing fantasy planning documents, and has numerous benefits as evidenced by applications of foresight analysis in the public sector. A separate paper "Disaster Risk Analysis Part 2" explores how disaster risk analyses are operationalized in governmental emergency management organizations, and finds evidence of a systemic underestimation of risk.


2011 ◽  
Vol 225-226 ◽  
pp. 839-842 ◽  
Author(s):  
Lu Hao ◽  
Xiao Yu Zhang ◽  
Zhi Liang Shu

Accurate assessment to disaster risk is one of the keys to reducing disaster losses. However, due to the fact that the disaster situation data series in county unit are always relatively short, available data are often not sufficient for disaster risk analysis. In this paper, a risk analysis method based on information diffusion theory was applied to create a new disaster risk analysis model (CURAM), and the risk of disaster can be evaluated on higher spatial resolution of county unit. Visual Basic and Map Objects were used to establish CURAM applying object oriented technique and component technique. CURAM provided risk evaluate function to natural disaster in county unit, and thematic map making and output, etc. The risk assessment results calculated by CURAM indicated that information diffusion technology was highly capable of extracting useful information and therefore improved system recognition accuracy.


2018 ◽  
Vol 229 ◽  
pp. 01015
Author(s):  
Turniningtyas Ayu Rachmawati ◽  
Dwi Rahmawati ◽  
Arief Rachmansyah

Mount Bromo is one of the most active volcanoes in East Java with a 4-5 year interval of the eruption. Its last eruption was in 2015 and is expected to erupt in 2020. The mountain is characterized as having the phreatic type of eruption, which can take months, and made Sukapura district the most seriously affected. Sukapura District is inhabited by Tengger people who strongly uphold their customs. The strong spiritual relationship between Tengger people and Mount Bromo affects the efforts to reduce the disaster risk. In anticipation of the coming eruption in 2020, a disaster risk calculation is required as the basis for disaster risk reduction. This paper examines the risks of Mount Bromo eruption disaster from the aspects of its hazards, vulnerability and community capacity. The results of risk calculation indicate that the vulnerability and capacity are the most influential aspects to the magnitude of the risks suffered by the community. The high-risk areas to prioritize are Ngadisari, Sariwani, Sapikerep, Wonokerto, Ngadirejo, and part of Jetak Village. Moderate risks include part of Kedasih village, part of Pakel Village, part of Ngadas Village, part of Jetak Village and part of Wonokerto Village. The low-risk areas include part of Ngepung Village, Sukapura Village, part of Ngadas Village and part of Wonotoro Village.


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