Spatial analysis and modelling of flood risk and climate adaptation capacity for assessing urban community and critical infrastructure interdependency

Flood hazards are the most common and destructive of all natural hazards in the world. A series of floods that hit the south east region of Queensland in Australia from December 2010 to January 2011 caused a massive devastation to the State, people, and its critical infrastructures. GIS-based risk mapping is considered a vital component in land use planning to reduce the adverse impacts of flooding. However, the integrated mapping of climate adaptation strategies, analysing interdependencies of critical infrastructures, and finding optimum decisions for natural disaster risk reduction in floodplain areas remain some of the challenging tasks. In this study, I examined the vulnerability of an urban community and its critical infrastructures to help alleviate these problem areas. The aim was to investigate the vulnerability and interdependency of urban community’s critical infrastructures using an integrated approach of flood risk and climate adaptation capacity assessments in conjunction with newly developed spatially-explicit analytical tools. As to the research area, I explored Brisbane City and identified the flood-affected critical infrastructures such as electricity, road and rail, sewerage, stormwater, water supply networks, and building properties. I developed a new spatially-explicit analytical approach to analyse the problem in four components: 1) transformation and standardisation of flood risk and climate adaptation capacity indicating variables using a) high resolution digital elevation modelling and urban morphological characterisation with 3D analysis, b) spatial analysis with fuzzy logic, c) geospatial autocorrelation, among others; 2) fuzzy gamma weighted overlay and topological cluster analyses using Bayesian joint conditional probability theory and self-organising neural network (SONN); 3) examination of critical infrastructure interdependency using utility network theory; and 4) analysis of optimum natural disaster risk reduction policies with Markov Decision Processes (MDP). The flood risk metrics and climate adaptation capacity metrics revealed a geographically inverse relationship (e.g. areas with very high flood risk index occupy a low climate adaptation capacity index). Interestingly, majority of the study area (93%) exhibited negative climate adaptation capacity metrics (-22.84 to < 0) which indicate that the resources (e.g. socio-economic) are not sufficient to increase the climate resiliency of the urban community and its critical infrastructures. I utilised these sets of information in the vulnerability assessment of critical infrastructures at single system level. The January 2011 flood instigated service disruptions on the following infrastructures: 1) electricity supplies along 627km (75%) and 212km (25%) transmission lines in two separate areas; 2) road and rail services along 170km (47%) and 2.5km (38%) networks, respectively; 3) potable water supply along 246km (56%) distribution lines; and 4) stormwater and sewerage services along 33km (91%) and 32km (78%) networks, respectively. From the critical infrastructure interdependency analysis, the failure of sewerage system due to the failure of electricity supply during the January 2011 flood exemplified the first order interdependency of critical infrastructures. The ripple effects of electricity failure down to road inaccessibility for emergency evacuation demonstrated the higher order interdependency. Moreover, an inverted pyramid structure demonstrated that the hierarchy of climate adaptation strategies of the infrastructures was graded from long-term measures (e.g. elimination) down to short-term measures (e.g. protection). The analysis with Markov Decision Processes (MDP) elucidated that the Australian Commonwealth government utilised the natural disaster risk reduction expenditure to focus on recovery while the State government focused on mitigation. There was a clear indication that the results of the MDP analysis for the State government established an agreement with the previous economic analysis (i.e. mitigation could reduce the cost of recovery by 50% by 2050 with benefit-cost ratio of 1.25). The newly developed spatially-explicit analytical technique, formulated in this thesis as the flood risk-adaptation capacity index-adaptation strategies (FRACIAS) linkage model, integrates the flood risk and climate adaptation capacity assessments for floodplain areas. Exacerbated by the absence of critical infrastructure interdependency assessment in various geographic analyses, this study enhanced the usual compartmentalised methods of assessing the flood risk and climate adaptation capacity of flood plain areas. Using the different drivers and factors that exposed an urban community and critical interdependent infrastructures to extreme climatic event, this work developed GIS-enabled systematic analysis which established the nexus between the descriptive and prescriptive modelling to climate risk assessment

Spatial modelling of adaptation strategies for urban built infrastructures exposed to flood hazards

The recent 2010/2011 floods in the central and southern Queensland (Australia) prompted this research to investigate the application of geographical information system (GIS) and remote sensing in modelling the current flood risk, adaptation/coping capacity, and adaptation strategies. Identified Brisbane City as the study area, the study aimed to develop a new approach of formulating adaptation/coping strategies that will aid in addressing flood risk management issues of an urban area with intensive residential and commercial uses. Fuzzy logic was the spatial analytical tool used in the integration of flood risk components (hazard, vulnerability, and exposure) and in the generation of flood risk and adaptation capacity indices. The research shows that 875 ha, 566 ha, and 828 ha were described as areas with relatively low, relatively moderate, and relatively high risk to flooding, respectively. Identified adaptation strategies for areas classified as having relatively low (RL), relatively moderate (RM), relatively high (RH), and likely very high (LVH) adaptation/coping capacity were mitigation to recovery phases, mitigation to response phases, mitigation to preparedness phases, and mitigation phase, respectively. Integrating the results from the flood risk assessment, quantitative description of adaptation capacity, and identification of adaptation strategies, a new analytical technique identified as flood risk-adaptation capacity index-adaptation strategies (FRACIAS) linkage model was developed for this study

Using spatial modelling to develop flood risk and climate adaptation capacity metrics for vulnerability assessments of urban community and critical water supply infrastructure

The aim of this study was to develop a new spatially-explicit analytical approach for urban flood risk assessment and generation of climate adaptation capacity metrics for assessing urban community and critical water supply network vulnerability. Using the January 2011 flood in Queensland (Australia) with the core suburbs of Brisbane City as the study area, the research issues with regards to the sufficiency of indicating variables and suitability of climate risk modelling were addressed in this study. A range of geographical variables were analysed using high resolution digital elevation modelling and urban morphological characterisation with 3D analysis, spatial analysis with fuzzy logic, and geospatial autocorrelation techniques with global Moran's I and Anselin Local Moran's I. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a 2-dimension self-organising 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 joint conditional probability weights based on Bayesian theory. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. The analyses showed that 186 ha (8%) and 221ha (10%) of the study area were exposed to very high flood risk and very low adaptation capacity, respectively. Ninety percent (90%) of the study area revealed negative adaptation capacity metrics (-31 to < 0) which implies that the resources are not enough to increase climate resiliency of the urban community and critical infrastructure (i.e. water supply network). This scenario was further exacerbated by the findings that government infrastructures in Queensland were uncovered by flood insurance. In the water supply network vulnerability assessment, eight (8) out of 107 critical trunk-reticulation main connection points were assessed as highly vulnerable critical water supply assets. Furthermore, utility network analysis showed that turbid water may flow along 246km of pressure main lines (i.e. trunk and reticulation mains) covering the north east to north west sides of the study area. In the absence of immediate mitigation measures, increased risk of fluvial flooding to water supply may significantly impacted the health conditions of urban residents. The newly developed spatially-explicit analytical technique, identified in this study as the flood risk-adaptation capacity index/metrics-adaptation strategies (FRACIAS) linkage model, will allow the integration of flood risk and climate adaptation assessments which have been treated separately in the past. This study provides a tool of high level analyses (e.g. building floor space, water supply connections, etc.) and identifies adaptation strategies to enable urban communities and the water supply industry to better prepare and mitigate future flood events. Furthermore, the results generated from the model can be used to improve insurance and land-use planning policies. These include the deliberation of risk-based premium pricing of flood insurance that should not heavily based on the geographic location of risk but should also take into consideration the adaptation capacity (e.g. income, severe disability, poor access to emergency services, etc.) of the community at risk. Through this approach, the governments (i.e. local, state, and federal) may provide financial and development support to areas of very high flood risk and very low adaptation capacity; thereby strengthening public-private partnership. As precaution, insurance policies may not be used solely as a decision tool for urban development on areas of very high flood risk but also consider the poor land-use planning inherited from the past. Further disaster risk reduction measures identified in this study include the 'flood proofing' of residential houses and commercial buildings, implementation of 'property buy-back' scheme and 'land swap' program, and amendment of Queensland Development Code to regulate the construction of buildings on areas identified with very high flood risk and very low adaptation capacity

Vulnerability assessment and interdependency analysis of critical infrastructures for climate adaptation and flood mitigation

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Practical implications – 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. Originality/value – 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

Understanding the January 2011 Queensland flood: the role of geographic interdependency in flood risk assessment for urban community

The aim of this study was to develop a new analytical approach of flood risk assessment in an urban area based on the concepts of geographic interdependency and spatial autocorrelation. This study focused on the January 2011 flood in Queensland (Australia) with core suburbs of Brisbane City as the study area. Urban morphological characterisation, point distance analysis, and collective events analysis were implemented to transform or standardise geographic features representing infrastructures such as building properties, emergency facilities, and flood-damaged electricity assets within buildings during the January 2011 flood. The global Moran's I and the Anselin Local Moran's I were the spatial autocorrelation techniques operationalised to simulate and analyse the geographic interdependency of the identified geographic features. The modified fuzzy gamma overlay operation was then used to integrate the generated risk components indicators (i.e. hazard, vulnerability, and exposure) to model the flood risk to urban community. Results of the analysis showed that the global Moran's I and Anselin Local Moran's I can simulate geographic interdependency of infrastructures for flood risk assessment in an urban community. Thirty three per cent (33%) (about 750 ha) and 25% (about 570 ha) of the study area were highly and very highly at risk and/or impacted, respectively, by the January 2011 flood at 95% level of confidence. This study produced spatially explicit analytical techniques that will enhance the spatial context of and benefit disaster risk management as tools for improving flood risk mapping and improving information for flood risk awareness. Furthermore, the tool can also help in identifying areas for detention basins for flood control, locating additional emergency and disaster assistance centres in priority flood risk areas, establishing early warning system in areas with high and very high flood risks, and improving preparedness, mitigation, response, and recovery plans

Spatial modelling of natural disaster risk reduction policies with Markov decision processes

The 2010/2011 floods in Queensland, Australia inflicted significant damages to government's critical infrastructures, private properties and businesses reaching an estimated amount of AU$16 billion. Mitigating the devastating effects of floods to the community and critical infrastructures entails competing financial requirements at the different levels of government. Hence, the main objective of this study was to examine the financial optimality of disaster risk reduction measures by integrating Markov decision processes (MDP for short) with geographic information system (GIS). Conducted in the core suburbs of Brisbane City, we organised the MDP variables using the following: 1) flood risk levels as the states of the urban system; 2) Queensland's disaster risk reduction measures as the action variables; 3) percentage of government expenditures by disaster risk reduction category as the state transition probabilities; 4) total lost earnings to businesses affected by the flood events as the reward variables; and 5) the weighted average riskless rate of return, the weighted average rate of return, and rate of return for a riskier asset as discounting factors. We analysed 36 MDP scenarios at four-level iteration and then calculated the expectimax values to find the optimal policy. The results from the analyses revealed that the Commonwealth government optimised the use of its natural disaster risk reduction expenditures to recovery while the State government focused on mitigation. When both government expenditures combined, the mitigation measure was identified as the optimum natural disaster risk reduction policy. The methodology presented in this study allowed a spatial representation and computationally feasible integration of complex flood disaster risk model with government expenditures and business earnings. The insights from this integrated approach emphasise the viability of finding optimum expenditures, and re-examine if necessary, in implementing natural disaster risk reduction policies and climate adaptation strategies