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

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