Risk-Resilience-Sustainability Nexus: A Natural Hazards Perspective

風險-彈性-可持續性之間的相互關係: 從自然災害的角度

Student thesis: Doctoral Thesis

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Awarding Institution
  • Chung Leung Johnny CHAN (Supervisor)
Award date3 Aug 2020


Risk-informed planning and management are integral to the sustainability of hazard-prone areas at all spatial scales. In connection to this, the current thesis advances the in-practice traditional risk frameworks for inclusiveness, reliability, and future vigilance in the wake of natural hazards and climate change via fostering inter-disciplinary approaches, recommends the integration of natural systems in risk assessment process for co-benefits, proposes aligning risk and sustainability frameworks for multi-objective planning and management, and explains how geospatial information models/technology could assist in risk management (profiling, decision-making, policy development, and resource allocation). The thesis consists of four main parts: (I) risk assessment perspectives, (II) influence of natural systems on risk level and its distribution, (III) aligning risk and sustainability frameworks, and (IV) integrating spatial models for effective risk management.

Each part addresses a specific objective of this study. These include risk perspectives, realizing natural habitats role in risk frameworks, aligning risk management and sustainability intentions, and integrating geospatial technology to inform risk management for effective planning (decision-making and resource allocation). Among all the natural hazards, tropical cyclones and coastal storms are chosen for this study due to their significant impacts.

Part I comprehensively details different risk assessment perspectives using hurricane flood risk along the United States Atlantic and Gulf coasts as a case study—under current and future scenarios. Most of the high-risk hotspots are found in the Gulf coast region, particularly along the west coast of Florida. While the resultant risk is sensitive to the consideration of evaluation factors (i.e., hazard, vulnerability, and resilience), two out of three risk evaluation approaches indicate New York City as a risk hotspot under the future climate. Additionally, a machine-learning algorithm-based approach to map the spatially distinct groups shows that the counties in the highest risk group (15% of total counties, including New York City) in the future lack specifically in the community capital and the social components of community resilience. 

In Part II, a coupled human-nature system-based framework is used to provide evidence on the influence of coastal natural habitats (CNHs) on coastal storm-risk level and spatial distribution. To do so, a spatially relative risk index for each coastal county along the U.S. Atlantic coast is computed incorporating several bio-geo-physical variables (e.g., geomorphology, natural habitats, coastal relief, and historical data on sea level trends, wind, and wave) and data on socio-ecological systems. The index is calculated under two CNH scenarios (i.e., without- and with-habitat) and is further used for mapping the at-risk population. The without-habitat scenario is found to overestimate the population in the highest risk category by 10 % and the number of counties by as much as 40 % as compared to the with-habitat scenario—mostly in the Gulf region. Also, the without-habitat scenario miscalculates the spatial distribution of the risk. While the results highlight the role of CNHs in influencing the risk level and its distribution, the findings support the emphasis by conservationists on policies relevant to the protection and restoration of coastal natural systems owing to their multiple services.

In Part III, a risk-resilience-sustainability nexus-based approach is proposed to align risk and sustainability frameworks. In contrast to traditional approaches, the framework employs an integrative approach and simultaneously provides useful input for resilience management in parallel to achieving certain Sustainable Development Goals (SDGs). The proposed framework is applied for risk assessment (represented by a Typhoon Risk Index—TRI) of coastal counties in Mainland China. A large spatial heterogeneity in typhoon risk is found with an increase in the risk from north to south in the study area. Further, the overall performance of coastal provinces in Mainland China is higher to achieve SDGs 3 and 15 followed by 13 and 8. The study shows that while Guangdong Province in southern China is in the highest risk category, its achievement status for SDG-13 (climate actions, strengthening resilience) is the lowest relative to other provinces. 

In Part IV, a geographic-information-system-based framework integrating spatial distributional models is proposed to evaluate the spatial heterogeneities of risk, its spatial patterns, and statistically significant hotspots of the highest risk. Further, the level of contribution of each risk parameter (i.e., hazard, vulnerability, and community resilience) towards overall risk is evaluated. It is found that among 70% exposed counties, ~ 30% are in the highest risk category (value ≥ 3rd quartile). The areas under the highest risk harbour > 50 million people (~43%)—more than 7 million non-adults (0–14 years, ~42%), and approximately 2.5 million elderly people (above 65 years, ~31%). The Pearl-River-Delta region of Guangdong Province in southern China is identified as the hotspot of the highest typhoon risk, followed by Fujian and Zhejiang provinces—95% confidence.

    Research areas

  • Risk-Resilience-Sustainability Nexus, Climate Change, Natural Hazards, Tropical Cyclone, Risk Assessment and Management, Spatial Analysis, Statistical Analysis, Geographical Distributional Modeling, Decision Making, Policy, Risk Planning