Spatio-temporal dynamics in seismic exposure of Asian megacities: past, present and future

Abstract

The estimation of urban growth in megacities is a critical and intricate task for researchers and decision-makers owing to the complexity of these urban systems. Currently, the majority of megacities are located in Asia which is one of the most disaster-prone regions in the world. The high concentrations of people, infrastructure and assets in megacities create high loss potentials for natural hazards; therefore, the forecasting of exposure metrics such as built-up area is crucial for disaster risk assessment. This study aims to identify and project the dynamics of built-up area at risk using a spatio-temporal approach considering seismic hazard in three Asian megacities, namely Jakarta, Metro Manila and Istanbul. First, Landsat Thematic Mapper images were processed to obtain the built-up areas of 1995 and 2016 for Metro Manila, and of 1995 and 2018 for Jakarta and Istanbul. The SLEUTH urban growth model, a cellular automaton (CA)-based spatial model that simulates urban growth using historical geospatial data, was then employed to predict the urban growth of these megacities by 2030. Finally, seismic hazard maps obtained for 10% and 2% probabilities of exceedance were overlaid with built-up area maps. For a seismic hazard of 10% probability of exceedance in 50 years, the total urban area subjected to Modified Mercalli intensities (MMI) VIII and IX has increased nearly 65% over 35 years in Metro Manila. For Jakarta and Istanbul, the total urban area at the MMI VIII level has increased nearly 79% and 54% over 35 years, respectively. For a seismic hazard of 2% probability of exceedance in 50 years, the total urban area subjected to MMI IX has increased nearly 75%, 65% and 49% over 35 years in Jakarta, Metro Manila and Istanbul, respectively. The results show that urban growth modelling can be utilized to assess the built-up area exposed to high risk as well as to plan urban growth considering natural hazards in megacities.

Using the T‐EFA method in a cellular automata‐based urban growth simulation's calibration step

“…In line with the study purposes, Istanbul province, whose population increased by more than 50% (TSI, 2020) after the great Marmara earthquake on August 17, 1999, was selected as the project area. Although there are many studies in the literature conducted using the SLEUTH model specific to Istanbul (Ayazli et al., 2015, 2019; Mestav Sarica et al., 2020; Nigussie & Altunkaynak, 2017), a particular part of Istanbul or only one or a few of the 12 spatial metrics used in the SLEUTH model calibration were used in these studies. Our study aims to eliminate these deficiencies as follows: Istanbul and its surroundings constitute the study area in order to fully determine the urban sprawl impact on all forest areas, wetlands, and agricultural lands within the city boundaries. Furthermore, we aim to use the 12 spatial metrics' R 2 scores at the model calibration stage, based on the assumption that the SLEUTH model calibration parameters contain all the systematic effects affecting the city's growth in the historical process that might or might not be observed.…”

Spatio-temporal dynamics of flood exposure in Shenzhen from present to future

“…Recent developments in technology have brought about profound advancements in disaster risk management. There have been transitions from static quantitative risk assessments to dynamic ones to reflect the pace of change in urbanization, and thus exposure (Mestav Sarica et al., 2020a; Wenzel et al., 2014). In addition to representing exposure variation in terms of built-up areas, change in impervious surfaces due to urbanization also alters the natural capacity for rainwater infiltration and runoff during rainfall events.…”

“…Shenzhen megacity was selected as a case study due to its rapid urban growth and high exposure to floods. We believe that by taking spatial change into account along with the temporal change, a more accurate projection for future exposure can be achieved (Mestav Sarica et al., 2020b) as compared to purely statistical approaches that consider only temporal change.…”

Modeling Downward Counterfactual Events: Unrealized Disasters and why they Matter

“…With climate change, hazards themselves-extreme temperatures, unprecedented storm frequencies, and more-are creating emergent, unfamiliar threats that communities have never experienced before (e.g., Shepherd et al, 2018). Simultaneously, some of the most vulnerable populations are subject to increasing risk from rapid urban growth in hazardprone regions (e.g., Mestav Sarica et al, 2020).…”