Society For Risk Analysis Annual Meeting 2013

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

M4-I
Risk and Rewards of Natural Resources and Natural Disasters

Room: Latrobe   3:30 PM - 5:10 PM

Chair(s): Royce Francis   seed@gwu.edu

Sponsored by EISG & RDSG



M4-I.1  15:30  Confronting risks and benefits of energy system improvements in developing communities: The case of Canada’s Northwest Territories. Kenney L*, Arvai J; University of Calgary   lmkenney@ucalgary.ca

Abstract: Decisions about energy development and delivery are inherently complex: they must address the technical challenges of energy production and transmission, and because they involve and affect people; as such, these decisions must address a range of often conflicting values. Decision making processes cannot proceed without input from multiple stakeholders. Ultimately, untangling this complexity means making tradeoffs across economic, social, and environmental concerns, while accounting for risk and uncertainty related to dynamic coupled natural-human systems. In developing communities these kinds of decisions are made more challenging as a result of the unique characteristics that typify these locations: e.g., poorly developed infrastructure; limited government budgets; political systems lacking transparency; economic vulnerability; and low education levels. Moreover, the social, economic, political, and environmental systems in these areas are tightly linked; changes in one system can easily affect the others. Decisions about energy have significant consequences in terms of quality of life and, therefore, are often part of larger development agendas. A case in point is Canada’s Northwest Territories (NWT), which is home to significant variations in geography, infrastructure development, economic activity, cultural traditions, and governance arrangements. Under these challenging conditions the NWT government is attempting to reform the region’s energy systems. This presentation reports the results of case study research conducted in the NWT between 2012 and 2013. We discuss the risks, challenges and benefits related to improving energy systems in developing communities. We then provide recommendations about how to structure decisions related to the energy development and delivery in the NWT so as to effectively meet a range of stakeholders’ objectives in a transparent and inclusive manner.

M4-I.2  15:50  Estimating the probability of extreme low-wind periods in the central United States. Rose SM*, Handschy MA, Apt J; CARNEGIE MELLON UNIVERSITY, ENDURING ENERGY LLC   srose@cmu.edu

Abstract: We estimate the probabilities of extreme low-wind-power events across regions ranging in size from tens to a few thousand kilometers. We first estimate the distributions of aggregate wind power for a range of aggregation area sizes using historical wind speed data and semi-empirical wind speed data from meteorological reanalysis projects. We then derive similar distributions theoretically by aggregating the output of individual wind farms modeled by correlated probability distributions. In both approaches, we attempt to characterize how the tail of the distribution of aggregate power changes as a function of the number of wind farms and the separation between them. These estimates will aid electrical grid operators in determining the quantity of conventional power that must be available over the long term as the penetration of wind power increases, and will aid in understanding debt-financing risks for wind project development.

M4-I.3  16:10  An assessment of the risks of building collapse for the City of Nairobi based on an investigation into East Africa’s construction quality control processes. . Figueroa RH*, Morgan MG, Fischbeck PS; Carnegie Mellon University   raulf@cmu.edu

Abstract: In developing countries, poor quality control in construction has led to spontaneous building collapse and, in the event of even moderate seismic activity, to major disaster. While earthquake-resistant designs have greatly improved international building codes that are accessible to designers everywhere, builders in developing countries often fail to meet acceptable standards. This paper examines the state of the industry with respect to compliance with standards for concrete used in structures, and assesses the risks of building collapse under different scenarios for the city of Nairobi. The state of the industry is assessed in two ways: 1) with a comparison of test results reported by established laboratories in Nairobi from a sample of new construction projects, to non-destructive-test data collected at twenty-four construction sites; and 2) through the elicitation of experts in construction familiar with the Kenyan industry. The findings suggest that there is widespread fraud and that the current quality control practices are not effective in ensuring structural reliability. Therefore, regulators routinely certify buildings as safe for occupation based, in part, on inaccurate of false laboratory reports. These findings highlight an example of laxity in quality control in the construction industry that could be pervasive in many developing countries, as the recent tragedy in Bangladesh and the disaster in Haiti in 2010 suggest. The risks of collapse is assessed by combining building inventory data, seismic performance models of common types of building in Nairobi, and estimates obtained by expert elicitation into a probabilistic risk model. Thousands of dangerously weak buildings will be built, and unless better policies are implemented, millions of people would likely be exposed to unnecessarily higher risks for generations. The methodology presented can be implemented in many other regions with minimal adjustments.

M4-I.4  16:30  Forensic disaster investigations (FORIN), a new approach to learn lessons from disasters: A case study of the 2001 Algiers (Algeria) Flood and Debris flow. Benouar D*, Rovins J; University of Science and Technology Houari Boumediene (USTHB)   dbenouar@gmail.com

Abstract: Disasters are increasingly being understood as ‘processes’ and not discreet ‘events’. Moreover, the causes of disasters are driven by complex engineering, socio-economic, socio-cultural, and various geophysical factors. Such interacting driving factors, occurring across a range of temporal and spatial scales, combine in numerous ways to configure disaster risks. Using some selected disasters in Africa, the dynamics of such risks and their configurations will be explored using a new approach and methodology, namely Forensic Disaster Investigations (also called FORIN studies). Forensic task is perhaps similar to solving a picture of a disaster puzzle. Initially, there are dozens or even hundreds of apparently disorganized pieces piled when examined individually, each piece may not provide much information. Methodically, the various pieces are sorted and patiently fitted together in a logical context taking into account all the parameters. Slowly, an overall picture of the disaster emerges. When a significant portion of the disaster puzzle has been solved, it then becomes easier to see where the remaining pieces fit. The Integrated Research on Disaster Risk programme is proposing new methodologies to examine the root issues surrounding the increase in disaster cost both human and economic.This paper attempts, as a case study, to investigate the Algiers (Algeria) floods and debris flows of 10 November 2001 which caused the loss of more than 714 human lives, injured more than 312, made missing 116 and about 10 000 were homeless, damaging more than 1500 housing units and scores of schools, bridges and public works. The objective is to dig more deeply into the causes of disasters in an integrated, comprehensive, transparent, and investigative or forensic style. To establish a sound basis for analysis, FORIN relies upon the actual evidence found and applies accepted scientific methodologies and principles to interpret the disaster in all its facets. Often, the analysis requires the simultaneous application of several scientific disciplines.

M4-I.5  16:50  Bayesian Multiscale Modeling of Spatial Infrastructure Performance Predictions. Reilly AC*, Guikema SD; Johns Hopkins University   acr@jhu.edu

Abstract: A number of models have been developed to estimate the spatial distribution of the likelihood of infrastructure impact during a natural hazard event. For example, statistical approaches have been developed to estimate the percentage of customers without power due to a hurricane, with the estimates made at the census tract level. However, such statistical models for predicting outage rates do not fully account for the spatial structure of outage patterns, leading to predictions where adjacent regions are dissimilar. In this paper, we develop a tree-based statistical mass-balance multiscale model to smooth the outage predictions at granular levels by allowing spatially similar areas to inform one another. Granular observations are then aggregated based upon their intrinsic hierarchical spatial structure leading to courser, region-wide predictions. We use a generalized density-based clustering algorithm to extract the hierarchical spatial structure. The “noise” regions (i.e., those regions located in sparse areas) are then aggregated using a distance-based clustering approach. We demonstrate this approach using outage predictions from Hurricanes Irene and develop outage prediction maps at various levels of granularity.



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