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

Espada Jr., Rodolfo and Apan, Armando and McDougall, Kevin (2013) Using spatial modelling to develop flood risk and climate adaptation capacity metrics for vulnerability assessments of urban community and critical water supply infrastructure. In: 49th World Congress of the International Society of City and Regional Planners (ISOCARP 2013): Frontiers of Planning: Evolving and Declining Models of City Planning Practice , 1-4 Oct 2013, Brisbane, Australia.

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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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 ISOCARP. The paper may be freely used and copied for educational and other non-commercial purposes, provided that any reproduction of data be accompanied by an acknowledgement of ISOCARP as the source. This does not apply to the pages and images with explicitely reserved reproduction right: © followed by the right owner and the year of first circulation. Reproduction of the latter requires prior authorization from the author.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 12 Dec 2013 06:38
Last Modified: 15 Sep 2014 00:50
Uncontrolled Keywords: flood risk assessment; climate adaptation capacity; spatial autocorrelation; Bayesian joint conditional probability; self-organising neural network; utility network analysis
Fields of Research : 04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040604 Natural Hazards
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0806 Information Systems > 080606 Global Information Systems
12 Built Environment and Design > 1205 Urban and Regional Planning > 120507 Urban Analysis and Development
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960301 Climate Change Adaptation Measures
D Environment > 96 Environment > 9609 Land and Water Management > 960912 Urban and Industrial Water Management
D Environment > 96 Environment > 9610 Natural Hazards > 961010 Natural Hazards in Urban and Industrial Environments

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