Building climate change into risk assessments

Abstract

Community managers and planners have an increasing need for assessing system failure risks as they relate to fact-based information on weather extremes and climate change. We illustrate a model that defines the services a software system can provide to facilitate the discovery of useful information by stakeholders with different technical background wanting to reach a fact-based consensus on risks, hazards, and vulnerabilities. Decision support systems succeed in facilitating the analysis of past severe weather events but provide limited support for the analysis of hazards related to climate change. Severe weather data enable estimates of the ability of an exposed system to withstand environmental extreme values, but the estimates of their impact on communities remain largely undetermined and prone to divergent interpretations. This study proposes a model that is built on the experience of a decision support system (DSS) that is dedicated to guide users through a stakeholder-based vulnerability assessment of community water systems. The DSS integrated data sources into an online environment so that perceived risks—defined and prioritized qualitatively by users—could be compared and discussed against the impacts that past events have had on the community. To make DSS useful for practical decision making related to the complex issues, such as those encountered in the case of climate change, we propose a model with a prototype design suitable for semantic web applications where the various entities are connected by an ontology that defines relative concepts and relationships.

Appendices

Appendix 1

See Table 4.

Table 4 List of risks recorded during the VASS experiment with their associated vulnerable system and hazards

Appendix 2

Results obtained by Howe et al. (2013) with the application of the participatory vulnerability scoping diagram (P-VSD) while engaging county A and B experts in a Participatory Risk Mapping (PRM) exercise (after Figs. 2, 3).

The approach provided an ordinal ranking of both the severity and probability of specific risks and allowed the calculation of an additional frequency variable based on the number of participants who mention the risk. Ordinal ranking assigned the value five for high and one for low. The risks in the P-VSD diagrams short-hand the risk descriptions elaborated by the experts during the focus groups to make the complex diagram readable.

In the figures, color hues signify the vulnerability dimension of a risk ranking result (e.g., purple for exposure, orange for sensitivity, and green for adaptive capacity). Transparency corresponds to the group’s perceived probability of each risk, with solid colors signifying high probability. Within each dimension, risks are sorted clockwise according to the order they were discussed in the ranking exercise. The relative importance of the risk (attribute one) is approximated by the risk’s numerical order in the sequence, whereas the other attributes are defined through the application of the deliberative process. The method used in the collection of the data makes it possible to describe the level of agreement between individuals and the group, as well as to compare results between groups

Appendix 3

The table lists the mini-stories extracted from the risk descriptions elaborated by the experts and listed in “Appendix 1”. Each mini-story includes an active verb along with subject and predicate to define an ontology element. The VUM generalization accommodates all the mini-stories extracted from the two focus groups (Table 5).

Table 5 Mini-stories extracted from the vulnerabilities in “Appendix 1”