Seismic loss dynamics in three Asian megacities using a macro-level approach based on socioeconomic exposure indicators

In 2015, the United Nations defined one of the main targets of the Sendai Framework as reducing the direct disaster economic loss in relation to GDP by 2030. We propose a framework to model the dynamics of seismic losses from present to future by using a macro-level loss estimation approach.
Published in Earth & Environment
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A society resilient to natural hazards is the one that can adapt to evolving exposure, absorb the impacts of disastrous events, and distribute risk among different stakeholders. Megacities are vulnerable hotspots of natural hazards due to high concentration of people, properties, infrastructure, and economic activities. Currently, majority of global megacities are in Asia, and many of them are often exposed to extreme earthquakes being located on top of or near seismic faults. In fact, Asia has been the region with the largest built-up area subjected to earthquakes, with a threefold increase from 1975 to 2015.

Exposure has been considered as the most dynamic component of risk when compared with other components, namely, hazard and vulnerability. It is often modelled by following purely temporal approaches that overlook its complexity; however, it varies across temporal and spatial scales. In 2015, the United Nations reported one of the main targets of the Sendai Framework as reducing the direct disaster economic loss in relation to Gross Domestic Product (GDP) by 2030. While historical exposure and risk assessments can explain past, they provide limited information for the future. Most of the studies in the literature focus on prediction of weather-related hazard losses, particularly scrutinizing the effects of climate change. Since the change in built environment generally means expansion in impervious surface, the impacts of urban growth can be seen in both hazard and exposure components for some of the weather-related perils. On the other hand, urbanization affects only the exposure component for seismic risk, and the hazard component cannot be predicted and controlled. This unique feature highlights the importance of modelling, mapping, and quantifying time-dependent exposure to understand and manage future seismic risk.

In this study, we aim to develop a multi-step framework to model the dynamics of seismic losses from present to future leveraging on the spatio-temporal changes in exposure, and by using a macro-level loss estimation approach. For this purpose, we selected three earthquake-prone Asian megacities from developing countries, namely Jakarta, Metro Manila, and Istanbul, as case studies. Our framework involves integration of different methodologies in the literature which have previously been used for other purposes. The macro-level approach that we employed in this study was first proposed by the USGS (United States Geological Survey) through PAGER (Prompt Assessment of Global Earthquakes for Response) system. It utilizes socioeconomic indicators, assuming that there is a direct relation between economic condition of a region and the potential loss resulting from an event in that region. Unlike conventional seismic loss estimation methodologies, it does not require a detailed inventory of exposed structures that is usually not publicly available, especially in developing countries. Although macro-level approaches can be effectively used for the evaluation of spatio-temporal loss trends and thus estimation of future seismic loss, we have observed that they have not been utilized for such an objective before. In addition to this, they have not been integrated into natural catastrophe modelling schemes to estimate primary probabilistic risk metrics such as average annual loss (AAL) and probable maximum loss (PML), which are widely used by the insurance industry and the decision-makers.

With these motivations in mind, we first calculated present probabilistic loss metrics, AAL and PML, by combining PAGER’s macro-level methodology with probabilistic seismic hazard analysis (PSHA). The results reveal that our approach can produce present loss estimates that are in the same order of magnitude as the conventional approaches. Therefore, it can be considered as a promising complementary tool to the conventional risk assessment framework. Afterwards, we estimated future AAL and PML leveraging on our exposure growth model. We have reported the present and future loss estimates in grid-level, Admin Level 2 and megacity-level. Our predictions suggest that present average annual loss could increase almost twofold in Jakarta and in Metro Manila, and by almost 57% in Istanbul by 2030.

The findings of our research shed light on future exposure and loss assessments by making comparisons among present and future. We observed that in regions where high levels of exposure and hazard are coupled, high potential of losses is predicted, while there are different trends in each megacity due to their varying historical urbanization patterns. Urban planners can utilize our grid-level present and future exposure and loss maps for risk mitigation and zoning purposes. In addition, AAL maps for Admin Level 2 can aid local authorities in allocation of resources, and megacity-level AAL and PML can provide information to governments and decision-makers about disaster risk financing options to reduce forthcoming risks. Overall, our proposed framework can be used to trigger discussions between scientific community and decision-makers for better long-term risk reduction and risk awareness strategies.

This research was conducted during the Ph.D. study of Dr. Gizem Mestav Sarica (now at Aon, Singapore as a senior analyst) in collaboration with her Ph.D. advisor Prof. Pan Tso-Chien at the Institute of Catastrophe Risk Management (ICRM), Nanyang Technological University (NTU), Singapore. The authors would like to acknowledge the partial financial support for this work provided by the ICRM, NTU, Singapore and the Monetary Authority of Singapore (MAS). More details can be found in our article “Seismic loss dynamics in three Asian megacities using a macro-level approach based on socioeconomic exposure indicators”.  Link: https://www.nature.com/articles/s43247-022-00430-9

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