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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | McClean, Fergus Clarke | - |
| dc.date.accessioned | 2020-01-22T15:00:34Z | - |
| dc.date.available | 2020-01-22T15:00:34Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.uri | http://theses.ncl.ac.uk/jspui/handle/10443/4625 | - |
| dc.description | Broad-scale flood modelling is a growing research area with applications in insurance, adaption and response. This has been fuelled by increasing availability of continental-global datasets providing inputs to a mounting array of models. However, outputs vary greatly and validation is challenging. This research developed a novel, consistent methodology for assigning performance scores to models using a range of gridded datasets and an accurate numerical 2D hydrodynamic modelling system. Validation using both extent and discharge was conducted for Storm Desmond in Northern England and the global applicability of the methodology demonstrated across Europe and in Indonesia. To meet computational demands, a cloud computing framework was implemented using a PostgreSQL database. Visualisation of results was achieved using a newly designed web interface. Finally OpenStreetMap data was overlaid to demonstrate the sensitivity of impacts to flood model inputs. The main findings are that relative importance of precipitation and topographic data changes depending on the metrics used for validation. More variability in peak discharge error was found between models using different rainfall inputs (22-70%) than different DEMs (9-37%). Conversely, flood extent critical success index (CSI) was more sensitive to the choice of topography (25-32%) than rainfall (27-30%), though overall variability in CSI was low. This was echoed in the impacts analysis with higher sensitivity of feature inundation to topography than rainfall. Importantly, there was far more overall variability in discharge accuracy than extent which indicates that reproduction of peak discharge is a more powerful measure for assessing model performance. Models driven by globalcontinental precipitation products underestimated peaks more than those using Met Office rain gauge data, though better performance was demonstrated by replacing ERA-Interim with the updated ERA5 dataset. The research highlights a growing need for more robust validation of broad scale flood simulations, and the difficulties this presents. Strong influence of dataset choice on infrastructure inundation has consequences for insurance premiums, development planning and adaptation to climate change risks which should not be ignored. | en_US |
| dc.description.sponsorship | NERC for funding the research through the Data, Risk and Environmental Analytical Methods (DREAM) training centre. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Newcastle University | en_US |
| dc.title | Broad-scale flood modelling in the cloud : validation and sensitivities from hazard to impact | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| McClean F C 2019.pdf | Thesis | 8.38 MB | Adobe PDF | View/Open |
| dspacelicence.pdf | Licence | 43.82 kB | Adobe PDF | View/Open |
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