Modeling downward counterfactual events : unrealized disasters and why they matter
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 o...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/145497 |
| _version_ | 1824454968361353216 |
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| author | Lin, Yolanda C. Jenkins, Susanna F. Chow, Jun Rui Biass, Sébastien Woo, Gordon Lallemant, David |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Lin, Yolanda C. Jenkins, Susanna F. Chow, Jun Rui Biass, Sébastien Woo, Gordon Lallemant, David |
| author_sort | Lin, Yolanda C. |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:30:44Z |
| format | Journal Article |
| id | ntu-10356/145497 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:30:44Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1454972023-02-28T16:42:16Z Modeling downward counterfactual events : unrealized disasters and why they matter Lin, Yolanda C. Jenkins, Susanna F. Chow, Jun Rui Biass, Sébastien Woo, Gordon Lallemant, David Asian School of the Environment Earth Observatory of Singapore Science::Geology Risk Framework Sensitivity Analysis 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. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) Published version YL, DL, and SJ received funding support from the National Research Foundation, Prime Minister’s Office, Singapore (NRF) under award number NRF2018-SR2001-007, and YL and DL were additionally supported by NRF through award number NRF-NRFF2018-06. JC was supported by the CN Yang Scholars’ Program of Nanyang Technological University. This research is partly supported by the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centers of Excellence initiative through the Earth Observatory of Singapore. This work comprises Earth Observatory of Singapore contribution number 307. 2020-12-23T02:20:12Z 2020-12-23T02:20:12Z 2020 Journal Article Lin, Y. C., Jenkins, S. F., Chow, J. R., Biass, S., Woo, G., & Lallemant, D. (2020). Modeling downward counterfactual events : unrealized disasters and why they matter. Frontiers in Earth Science, 8, 575048-. doi:10.3389/feart.2020.575048 2296-6463 https://hdl.handle.net/10356/145497 10.3389/feart.2020.575048 8 en NRF2018-SR2001-007 NRF-NRFF2018-06 Frontiers in Earth Science © 2020 Lin, Jenkins, Chow, Biass, Woo and Lallemant. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. application/pdf |
| spellingShingle | Science::Geology Risk Framework Sensitivity Analysis Lin, Yolanda C. Jenkins, Susanna F. Chow, Jun Rui Biass, Sébastien Woo, Gordon Lallemant, David Modeling downward counterfactual events : unrealized disasters and why they matter |
| title | Modeling downward counterfactual events : unrealized disasters and why they matter |
| title_full | Modeling downward counterfactual events : unrealized disasters and why they matter |
| title_fullStr | Modeling downward counterfactual events : unrealized disasters and why they matter |
| title_full_unstemmed | Modeling downward counterfactual events : unrealized disasters and why they matter |
| title_short | Modeling downward counterfactual events : unrealized disasters and why they matter |
| title_sort | modeling downward counterfactual events unrealized disasters and why they matter |
| topic | Science::Geology Risk Framework Sensitivity Analysis |
| url | https://hdl.handle.net/10356/145497 |
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