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DC Field | Value | Language |
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dc.contributor.author | Pregnolato, Maria | - |
dc.date.accessioned | 2018-10-17T10:40:43Z | - |
dc.date.available | 2018-10-17T10:40:43Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/10443/4036 | - |
dc.description | PhD Thesis | en_US |
dc.description.abstract | Cities are increasingly vulnerable to damage and disruption from adverse-weather events, due to their high concentration of people and assets. To improve engineering and planning decisions in the face of complex interactions between climate hazards, infrastructure and actors within the urban system requires novel analytical tools and methodologies. This research therefore takes a systems approach to developing an integrated framework to model the impact of surface water flooding on the transport network before using this to explore the effectiveness of potential adaptation options to increase urban resilience. The framework calculates delays in travel times by coupling a hazard model to both a hydraulic model and traffic network simulations. The hazard model was approximated for current climate by obtaining intensities for rainfall events with different return periods using the Flood Estimation Handbook (FEH methodology). These rainfall intensities were converted to flood depths over the region of interest using a dynamic flood model (CityCAT). Spatial flood footprints obtained from the model were integrated over the road network to identify affected transport corridors. To calculate the reductions in vehicle speeds due to standing water on these corridors, a new depth-disruption function (i.e. relates depth of flood water to safe vehicle speed) was developed. This was used to estimate reductions in the speeds of individual vehicles which drive a macro-transport network model that has been developed to calculate city-wide travel times and subsequently how these change due to flooding. Pre-event and post-event travel times of commuters are compared, in order to quantify the impact of flooding on network performance, and assess the effectiveness of urban interventions at managing this risk. The framework has been demonstrated in Newcastle-upon-Tyne (UK) using publicly available data and verified through available historical data. With no adaptation of the transport system, a 1 in 200 year rainfall event increases travel times by more than 50%, with an associated economic impact of over £220,000. Adaptation measures contribute significantly to flood alleviation. Application of a risk-based ‘criticality assessment’ has been shown to enable effective targeting of grey (traditional engineering) adaptation, Page ii and in this case installation of flood management measures at the top six most ‘critical’ locations can reduce net present flood risk by 41% over a 10 years timeframe. This compares to similar reduction (38%) for a green adaptation strategy. The green strategy provides a city-wide flood depth reduction, and it does not represent an economically-feasible option. Green infrastructure also provides additional co-benefits, such as enhanced biodiversity and air quality improvements, deployment of green infrastructure at a city-wide scale would require an unprecedented scale, and high cost, of intervention. Balancing grey and green interventions offers the most effective strategy to manage flood risk to transport disruption. Combining flood modelling and transport network analysis is shown to improve engineering decision-making and enable the prioritisation of adaptation investments in urban areas. The findings and the methodology are of interest to transport policy analysts, planners, economists and engineers, as well as communities affected by disruptive events. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Newcastle University | en_US |
dc.title | Risk analysis of the disruption to urban transport networks from pluvial flooding | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | School of Civil Engineering and Geosciences |
Files in This Item:
File | Description | Size | Format | |
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Pregnolato, M. 2018.pdf | Thesis | 10.61 MB | Adobe PDF | View/Open |
dspacelicence.pdf | Licence | 43.82 kB | Adobe PDF | View/Open |
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