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The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical…
The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies.
Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated.
The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures.
While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc.
This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined.
The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.
The purpose of this paper is to present a novel approach that examines the vulnerability and interdependency of critical infrastructures using the network theory in…
The purpose of this paper is to present a novel approach that examines the vulnerability and interdependency of critical infrastructures using the network theory in geographic information system (GIS) setting in combination with literature and government reports. Specifically, the objectives of this study were to generate the network models of critical infrastructure systems (CISs), particularly electricity, roads and sewerage networks; to characterize the CISs’ interdependencies; and to outline the climate adaptation (CA) and flood mitigation measures of CIS.
An integrated approach was undertaken in assessing the vulnerability and interdependency of critical infrastructures. A single system model and system-of-systems model were operationalized to examine the vulnerability and interdependency of the identified critical infrastructures in GIS environment. Existing CA and flood mitigation measures from government reports were integrated in the above-mentioned findings to better understand and gain focus in the implementation of natural disaster risk reduction (DRR) policies, particularly during the 2010/2011 floods in Queensland, Australia.
Using the results from the above-mentioned approach, the spatially explicit framework was developed with four key operational dimensions: conceiving the climate risk environment; understanding the critical infrastructures’ common cause and cascade failures; modeling individual infrastructure system and system-of-systems level within GIS setting; and integrating the above-mentioned results with the government reports to increase CA and resilience measures of flood-affected critical infrastructures.
While natural DRR measures include preparation, response and recovery, this study focused on flood mitigation. Temporal analysis and application to other natural disasters were also not considered in the analysis.
By providing this information, government-owned corporations, CISs managers and other concerned stakeholders will allow to identify infrastructure assets that are highly critical, identify vulnerable infrastructures within areas of very high flood risk, examine the interdependency of critical infrastructures and the effects of cascaded failures, identify ways of reducing flood risk and extreme climate events and prioritize DRR measures and CA strategies.
The individualist or “pigeon-hole” approach has been the common method of analyzing infrastructures’ exposure to flood hazards and tends to separately examine the risk for different types of infrastructure (e.g. electricity, water, sewerage, roads and rails and stormwater). This study introduced an integrated approach of analyzing infrastructure risk to damage and cascade failure due to flooding. Aside from introducing the integrated approach, this study operationalized GIS-based vulnerability assessment and interdependency of critical infrastructures which had been unsubstantially considered in the past analytical frameworks. The authors considered this study of high significance, considering that floodplain planning schemes often lack the consideration of critical infrastructure interdependency.