Mengqi Ye, MSc

Critical infrastructure (CI) is exposed to natural hazards that may lead to the devastation of these infrastructures and burden society with the indirect consequences that stem from this. Fragility and vulnerability curves, which quantify the likelihood of a certain damage state and the level of damage of an element under varying hazard intensities, play a crucial role in comprehending, evaluating, and mitigating the damage posed by natural hazards to these infrastructures. To date, however, these curves for CI have been distributed across the literature instead of being accessible through a centralized database. This study, through a systematic literature review, synthesizes the state of the art of fragility and vulnerability curves for the CI assets of energy, transport, water, waste, telecommunication, health, and education in context of natural hazards and offers a unique physical vulnerability database. The publicly available centralized database that contains over 1510 curves can directly be used as input for risk assessment studies that evaluate the potential physical damage to assets due to flooding, earthquakes, windstorms, and landslides. The literature review highlights that vulnerability development has mainly focused on earthquake curves for a wide range of infrastructure types. The curves for windstorms have the second largest share in the database, but they are especially limited to energy curves. While all CI systems require more vulnerability research, additional efforts are needed for telecommunication, which is largely underrepresented in our database.

Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

Tropical cyclones (TCs) have devastating impacts and are responsible for significant damage. Consequently, for TC-induced direct economic loss (DEL) attribution all factors associated with risk (i.e. hazard, exposure and vulnerability) must be examined. This research quantifies the relationship between TC-induced DELs and maximum wind speed, asset value and Gross Domestic Product (GDP) per capita using a regression model with TC records from 2000 to 2015 for China’s mainland area. The coefficient of the maximum wind speed term indicates that a doubling of the maximum wind speed increases DELs by 225% [97%, 435%] when the other two variables are held constant. The coefficient of the asset value term indicates that a doubling of asset value exposed to TCs increases DELs by 79% [58%, 103%]; thus, if hazard and vulnerability are assumed to be constant in the future, then a dramatic escalation in TC-induced DELs will occur given the increase in asset value, suggesting that TC-prone areas with rapid urbanization and wealth accumulation will inevitably be subject to higher risk. Reducing the asset value exposure via land-use planning, for example, is important for decreasing TC risk. The coefficient of GDP per capita term indicates that a doubling in GDP per capita could decrease DELs by 54% [39%, 66%]. Because accumulated assets constantly increase people’s demand for improved security, stakeholders must invest in risk identification, early warning systems, emergency management and other effective prevention measures with increasing income to reduce vulnerability. This research aims to quantitatively connect TC risk (expected DELs, specifically) to physical and socioeconomic drivers and emphasizes how human dimensions could contribute to TC risk. Moreover, the model can be used to estimate TC risk under climate change and future socioeconomic development in the context of China.

Tropical cyclones (TCs) can wreak havoc on the landscape and overwhelm communities. Since economic exposure is an important factor in damage function, an evaluation of economic exposure is essential because the characteristics of TC-related hazards are changing under accelerating economic development patterns. Here, we first reconstructed the wind and rainfall fields of historical TCs through an extensive database to extract the economic exposure to TC-prone areas on the mainland of China. We found that rainfall is an important factor in determining the affected extent of a TC event and that economic exposure will be misestimated when considering only the wind field. The results reveal that economic exposure to TCs has increased considerably from 1990 to 2015 and will continue to increase until the year 2100 under shared socioeconomic pathways (SSPs). We found that 66.7% of China’s gross domestic product [GDP; CNY 48.6 trillion (7.8 trillion U.S. dollars)] and 63.9% of China’s asset value [CNY 139.5 trillion (22.4 trillion U.S. dollars)] were concentrated in TC-prone areas in 2015 and increased at an average annual rate of 10.6% and 13.9%, respectively. Projections of GDP scenarios under SSPs revealed continued growth in the early twenty-first century, and the range of GDP and asset value in TC-prone areas by 2100 varied. Further detailed studies are needed to provide a detailed damage function for TC loss assessments under climate change and to consider how TC hazards will interact under changes in exposure and vulnerability related to economic development and social change.

Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census level). To bridge the gap between census-level BFA data and geo-coded building asset value data, this article introduces a method for building asset value mapping, using Shanghai as an example. This method consists of a census-level BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data, combined with LandScan population density grid data and a financial appraisal of building asset values. Validation with statistical data and field survey data confirms that the method can produce good results, but largely constrained by the resolution of the population density grid used. However, compared with other models with no disaggregation in flood exposure assessment that involves Shanghai, the building asset value mapping method used in this study has a comparative advantage, and it will provide a quick way to produce a building asset value map for regional flood risk assessments. We argue that a sound flood risk assessment should be based on a high-resolution—individual building-based—building asset value map because of the high spatial heterogeneity of flood hazards.