Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.815263
Title: Sensitivity analysis, uncertainty quantification and parameter estimation for a numerical tide and storm surge model
Author: Warder, Simon Charles
ISNI:       0000 0004 9357 204X
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2020
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Storm surges pose a significant hazard to coastal communities worldwide. Accurate and reliable storm surge numerical models, combined with an understanding of model uncertainties, are therefore vital. In this work, an adjoint-capable numerical coastal ocean model, Thetis, is extended and used to perform a sensitivity analysis for a hindcast case study in the North Sea, revealing the spatial patterns of the sensitivity of modelled surges to three model inputs. These sensitivities are used to gain physical insight, and to perform uncertainty quantification for each of the model inputs. The results indicate that, while the greatest contribution to uncertainty is made by meteorological inputs, uncertain bottom friction is nevertheless significant. This motivates a comparison of parameter estimation methods for a spatially varying bottom friction coefficient. This is performed for a tide-only case study consisting of the Bristol Channel and Severn Estuary. Here, a gradient-based optimisation method via the adjoint model is compared with Bayesian inference via a Markov Chain Monte Carlo algorithm, utilising a Gaussian process emulator as a surrogate for the full numerical model. Three friction parameters, based on the distribution of sediment types within the model domain, are estimated, with the results from each calibration method consistent within the estimated parameter uncertainties. Furthermore, the estimated parameters are found to reduce model-observation misfit for a second numerical model, TELEMAC-2D, suggesting that the calibration process has identified physically meaningful parameters. Since such a model calibration relies on the availability of observation data, this work considers a framework for the identification of observation locations which will perform best in a parameter estimation exercise. In addition to an application within the Bristol Channel case study, this work takes steps towards guiding new observations in the Maldives, where present datasets are severely limited and the development of well-calibrated numerical models would be highly valuable.
Supervisor: Piggott, Matthew ; Cotter, Colin ; Ham, David Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.815263  DOI:
Share: