Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

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SRA 2013 Annual Meeting Abstracts P.38 Song, JW*; Small, MJ; Carnegie Mellon University; jsong31@gmail.com Determining detection rates of environmental DNA sampling for monitoring the risk of invasive fish species Early detection of invasive species is critical to effective aquatic ecosystem risk management. A newly developed detection method is eDNA sampling, which is the analysis of water samples for the presence of species-specific environmental DNA (eDNA), DNA fragments that are released in the water, to infer the presence of the species. This technique promises improved detection sensitivity and specificity and reduced monitoring costs compared to traditional techniques. However, the use of eDNA sampling in decision-making frameworks is challenging due to the many uncertainties associated with the DNA technology and sampling methodology. These uncertainties have received particular attention in the use of eDNA sampling for detection of invasive Asian carp species in the Great Lakes region, where many costly and ineffective risk management efforts have been performed due to eDNA evidence. In this paper, the uncertainties in the relationship between fish presence and eDNA presence in a river system is explored. A one-dimensional advective-reactive-dispersive transport model is integrated with a fish dispersal model to determine the concentration profile of eDNA, spatially and temporally, in a specified river system. The model can then evaluate the relationship between fish density and eDNA concentration and the potential detection rates at each section of the river. The results suggest that under high flow conditions, such as in major river channels, there is a high likelihood of false negatives due to the washout of eDNA. The potential of false positives is higher under low flow conditions, such as in slower-moving backwater areas, because the persistence of eDNA can now influence the results. A stronger understanding of the detection rates of eDNA sampling will help inform improved sampling methodologies and better integration with decision-making frameworks. P.131 Song, H*; Underhill, JC; Schuldt, JP; Song and Schuldt: Cornell University, Underhill: Johns Hopkins University; hs672@cornell.edu Communicating conservation with labels: Experiment on the effectiveness of using IUCN categories for advocacy The Red List published by the International Union for Conservation of Nature and Natural Resources (IUCN) uses a categorical system with labels such as “Critically Endangered” or “Vulnerable” to communicate the level of threat faced by each species. This study examined whether messages using such categorization information would be as effective as messages using statistical information in communicating risk. In an online experiment, 169 participants were randomly assigned to read four descriptions about threatened species written with either categorization information (verbal group) or statistical information (statistical group). Readability measured by the Flesch-Kincaid Grade Level score was controlled for across conditions (e.g., “According to the IUCN, the Bigeye Tuna is classified as a Vulnerable (VU) species” vs. “According to the IUCN, the Bigeye Tuna population declined by 42% around the globe over the past 15 years”). Although there were no significant differences in perceived message clarity or behavioral intention, perceived risk of extinction was higher among the statistical group than the verbal group. Thus, professionals communicating with lay audiences about threatened species may wish to cite relevant statistics instead of, or along with, the Red List categories. A follow-up study featuring a more diverse participant sample and varying levels of statistical complexity is currently underway. T4-A.1 Spada, M*; Burgherr, P; Laboratory for Energy Systems Analysis, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland; matteo.spada@psi.ch Quantitative Risk Analysis of Severe Accidents in Fossil Energy Chains Using Bayesian Hierarchical Models Risk assessment of severe accidents in the energy sector is an important aspect that contributes to improve safety performance of technologies, but is also essential in the broader context of energy security and policy formulation by decision makers. A comprehensive approach is needed because accidents can occur at all stages of an energy chain. The classical approach to assess the risk of severe accidents in fossil energy chains is the use of aggregated risk indicators focusing on human health impacts, i.e., fatality rates, and/or frequency-consequence curves. However, the analysis of extreme accidents contributing disproportionally to the total number of fatalities in an energy chain is often impeded due to the scarcity of historical observations. Furthermore, the common high uncertainties, in particular for the risk of extreme events, cannot be fully addressed using this standard approach. In order to assess the risk, including the one of extreme accidents, we apply a Bayesian hierarchical model. This allows yielding analytical functions for frequency and severity distributions as well as frequency trends. Bayesian data analysis permits the pooling of information from different data sets, and inherently delivers a measure of uncertainty. The current analysis covers severe (≥5 fatalities) accidents in the coal, oil and natural gas chains for the years 1970-2008, which are contained in PSI’s Energy-related Severe Accident Database (ENSAD). First, analytical functions for frequency and severity distributions common to all energy fossil chains were established. Second, these distributions served as an input to the Bayesian Hierarchical Model. The risks are quantified separately for OECD, EU 27 and non-OECD countries. The proposed approach provides a unified framework that comprehensively covers accident risks in fossil energy chains, and allows calculating specific risk indicators to be used in a comprehensive sustainability and energy security evaluation of energy technologies. T3-I.1 Staid, A*; Guikema, SD; Nateghi, R; Quiring, SM; Gao, MZ; Yang, Z; Johns Hopkins University; staid@jhu.edu Long-term hurricane impact on U.S. power systems Hurricanes have been the cause of extensive damage to infrastructure, massive financial losses, and displaced communities in many regions of the United States throughout history. From an infrastructure standpoint, the electric power distribution system is particularly vulnerable; power outages and related damages have to be repaired quickly, forcing utility companies to spend significant amounts of time and resources during and after each storm. It is expected that climate change will have significant impacts on weather patterns, but there is much uncertainty regarding the nature of those impacts. In order to characterize some of this uncertainty, we simulate the long-term impacts of hurricanes on U.S. power systems and evaluate this impact looking out into the future under different scenarios. We evaluate future power system impacts while varying hurricane intensity, annual hurricane frequency, and landfall location. This allows us to understand the sensitivity of hurricane impacts to some of the possible scenarios expected under climate change. Using the historical data record as a baseline, hurricane intensity and annual frequency are independently varied both positively and negatively. Changes in landfall location are achieved by adjusting the probability distribution for landfall. The results of these simulations show the areas along the Atlantic Coast that will be hit hardest by hurricanes under possible changes in hurricane hazard scenarios. Areas that are heavily impacted under multiple scenarios, or under those scenarios expected to be the most likely to occur, can use this information to make more informed decisions about possible investments in power grid resilience or robustness. Adaptations to climate change need to be considered as soon as possible in order to maximize the benefits of these investments, even though considerable uncertainty remains about the potential impacts of climate change on hurricane hazards to the power system. December 8-11, 2013 - Baltimore, MD
SRA 2013 Annual Meeting Abstracts T1-I.3 Stavrou, DI*; Ventikos, NP; School of Naval Architecture and Marine Engineer in Technical University of Athens; dstaurou@gmail.com Submarine Power Cables (SPCs): The laying procedure, the fleet and reliability analysis of Medium Voltage Network. The use of SPCs over the last decades plays a significant role in the transfer of energy in worldwide scale. The aim of this study is to determine the aspects of the issue that concerns the laying procedure and develop a model of reliability analysis so to evaluate potential cable routes. Vessels with special characteristics are used to accomplish the laying procedure. A preliminary analysis is necessary so as to determine the factors that affect both the laying procedure. A reliability analysis of effectiveness is applied and presented using the raw data of the Hellenic SPCs Mediun Voltage Network; the model is based on the respective model that has been represented in the study “Reliability Analysis Of Submarine Power Cables And Determination Of External Mechanical Protection” by M. Nakamura at al. in 1992. The parameters that have been considered in the context of the presented model comprise: The depth of the cable route/depth at the point of failure of the cable; The length of the cable route/minimum distance of the point of failure from the coast; The seabed characteristics of the cable route at the area of failure; The level of protection of the cable along the cable route at the area of failure. The application of this reliability model can be used in a two-fold manner: To determine critical and safe areas along a certain cable route. During the phase of preliminary design to choose the optimum route for laying the cable. In particular the failure rate at any (Ei, Lj, Dk) of the cable is: Ri=F(Ei)·F(Lj)·P(Dk|E1,L0) For the entire length of the cable the reliability is: Rtotal =ΣRi The mean time between failures refers to the safe operating time for a cable route. MTBF=1/Rtotal P.111 Stedge, J*; Brad, F; Abt Associates; gerald_stedge@abtassoc.com SafeWater CBX: Incorporating Uncertainty and Variability in Benefits Analysis Incorporating variability and uncertainty into public health regulation benefits assessments is critical to fully understanding the potential impacts; however, doing so can be data intensive and computationally complex. To support the development of national primary drinking water standards, we developed the SafeWater CBX model which is designed to estimate the health benefits associated with alternative maximum contaminant levels (MCLs) in drinking water. SafeWater CBX is the first model ever developed to fully incorporate both variability and uncertainty in drinking water benefits assessment. The model first estimates the exposed populations at each public water system (PWS) entry point to the distribution system (EP). The exposed population is categorized by age and gender. Based on EP-level distributions of contaminant occurrence (which vary by source water type and region and are uncertain), drinking water consumption (which varies by age), and dose-response functions (which vary by age and gender and are also uncertain), the model then estimates the expected cases of illness each year (over a 50-year period of analysis) at any number of alternative MCLs. SafeWater CBX then values both the reduced expected illnesses and deaths avoided using cost of illness estimates and the value of statistical life (both of which are uncertain). The health benefits can be displayed by source water type (ground or surface water), age group, sex, PWS system size, and region. In addition to its ability to incorporate variability and uncertainty into the benefits analysis, SafeWater CBX also provides users with the option to run in “mean mode” where all inputs are treated as certain (variability is still modeled). This capability allows users to conduct sensitivity analyses in real time (15 minutes per run), making it possible to incorporate information on benefits into the regulatory option selection process. W4-H.2 Steinhardt, JS*; Shapiro, MA; Cornell University; jsteinh@gmail.com The impact of narrative messages on prospect theory framing effects. Several previous studies found that gain/loss framing can influence the effectiveness of non-narrative health and risk communication messages. However, narrative messages are increasingly popular in health campaigns but the effect of narrative messages on prospect theory framing effects is not fully understood. Three experiments examined the role of narrative messages on framing effects. Experiment 1 found that framing did not influence judgments about characters and decisions in a story derived from a prospect theory context. Experiment 2 found a shift in preferences when a decision about gambling was presented in the form of a narrative compared with traditional wording from prospect theory studies (Tversky & Kahneman, 1986). This shift was present regardless of whether or not the zero was deleted from the wording, as suggested might remove framing effects in previous research (Reyna, 2012). Using a different story/decision context based on a choice between radiation therapy and surgery (McNeil et al., 1982), Experiment 3 found a narrative presentation amplified a preference for surgery compared to a non-narrative presentation. The results of all three experiments suggest that health campaigns cannot assume that framing effects will be the same in narrative messages and non-narrative messages. Potential reasons for these differences and suggestions for future research are discussed. P.98 Steinhardt, JS*; Niederdeppe, J; Lee, T; Cornell University; jsteinh@gmail.com Numeracy and Beliefs About the Preventability of Cancer Fatalistic beliefs about cancer and uncertainty about information in news stories about cancer are barriers to cancer preventing behaviors. This research explores the relationship between numeracy, the ability to reason mathematically and interpret basic statistical and probabilistic information, and both fatalistic beliefs and uncertainty about the information present in news stories about cancer. Numeracy is measured using a 7-item subjective numeracy scale. A sample of 601 adults aged 18 and older were asked to read news stories about cancer in one of 15 randomized conditions and then asked questions related to cancer prevention and subjective numeracy. Higher levels of numeracy were associated with less fatalistic beliefs about cancer and less uncertainty about cancer risk and prevention. Interactions between numeracy and cancer news story exposure on fatalistic and uncertain beliefs about cancer risks and preventions, however, were not statistically significant. We conclude with a discussion of implications of these findings for future research on numeracy and health communication about complex issues like cancer. December 8-11, 2013 - Baltimore, MD
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