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

Espada Jr., Rodolfo (2014) Spatial analysis and modelling of flood risk and climate adaptation capacity for assessing urban community and critical infrastructure interdependency. [Thesis (PhD/Research)]


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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.

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Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - No Department (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - No Department (1 Jul 2013 -)
Supervisors: Apan, Armando
Date Deposited: 14 Jun 2016 06:05
Last Modified: 05 Sep 2016 02:49
Uncontrolled Keywords: spatial; interdependency; compartmentalised; flood; autocorrelation
Fields of Research (2008): 05 Environmental Sciences > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4199 Other environmental sciences > 419999 Other environmental sciences not elsewhere classified

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