Modeling Downward Counterfactual Events: Unrealized Disasters and why they Matter

Yolanda C. Lin,Susanna F. Jenkins,David Lallemant,Jun Rui Chow,Sébastien Biass,Gordon Woo

doi:10.3389/feart.2020.575048

Abstract

Disaster risk research's reliance on past events has proved inadequate when it comes to extreme events. This shortcoming stems from limited records (for example, due to the vast differences in timescales between geological processes and human records) and the dynamic nature of all three components of risk -- drivers of change in hazard (e.g., climate change), exposure (e.g., urban growth), and vulnerability (e.g., aging infrastructure). This paper provides a framework for modeling key unrealized events through downward counterfactual changes for consideration in future risk modeling and assessment. Past disasters are typically viewed as fixed events, and the resulting lessons-learned are inherently limited by this definition; downward counterfactual thought provides a means to harvest additional insights and capture a larger consequence space. As such, we have identified a need for a guiding framework in order to incorporate downward counterfactual thought in the context of disaster risk modeling applied to both natural and anthropogenic hazards. The downward counterfactual framework relies first on identifying a past event, which may or may not have been considered a major disaster. After an event has been identified, historical parameters from the past event are relaxed in order to identify small changes that result in downward, worsening consequences. This can be continued for multiple changes until an end-of-search criteria is reached. The framework is especially relevant for regions that may have a sparse past catalog, either due to limited data or limited occurrences of natural hazards. As such, the framework is demonstrated in the context of Singapore, a city-nation that has historically had limited recorded natural hazards events, through five case studies on past anthropogenic, environmental, seismic, volcanic, and storm hazard events. This framework can help harness lessons-learned from unrealized disasters to support a more resilient future through informed policies and plans.

Highlights

Current policies, risk management practices, emergency preparedness, and scientific research interests are all shaped by past events–in many cases, past disasters [e.g., following an earthquake (Wilkinson et al, 2014) or after a major storm (Jha et al, 2016)]
We have identified a need for a guiding framework in order to incorporate downward counterfactual thought in the context of disaster risk modeling applied to both natural and technological or anthropogenic hazards
Current disaster risk modeling methods are insufficient in capturing surprising, low-probability, and/or emergent events

Summary

INTRODUCTION

Risk management practices, emergency preparedness, and scientific research interests are all shaped by past events–in many cases, past disasters [e.g., following an earthquake (Wilkinson et al, 2014) or after a major storm (Jha et al, 2016)]. As we compare results in Step 5, we accept that the downward counterfactual consequences both exceed the historic consequence and meet the end-of-search criteria for Step 6 This comparison suggests that a shift of a matter of days–in the span of months that the volcano had shown signs of unrest–could have drastically changed consequences for the island-nation of Singapore and many other countries. This methodology can be further extended to explore downstream effects, such as damages, losses, recovery, and other stages of pre- and post-disaster risk analysis Analysis of this dimension presents its own challenges–computationally, to search over multiple variables with potentially large data sets and large uncertainties; from a discipline perspective, to cross through the hazard science and reach through to the impacts and consequences that build the fabric of a city or community. Compared to presenting a hypothetical scenario based in the future, the downward counterfactual provides a more complete context for stakeholders to understand potentially relevant downward risks, and connect to their own experience or experiences in their community

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT