Author(s): Lucy Manning; Jim Hall

Linked Author(s):

Keywords: No Keywords

Abstract: Analysis of uncertainties in hydraulic models of flooding processes is a perennial area of investigation. Here our concern is with (i) combining prior knowledge with information from calibration exercises to generate well justified posterior distributions on model parameters, whilst (ii) at the same time also using observations to understand the structural uncertainties that separate model predictions from hydraulic processes in reality. This paper reports on the latest results in a programme of work on Bayesian calibration of flood inundation models. The recent research has moved on from the steady state models reported at previous IAHR conferences to a preliminary version of Bayesian calibration for an unsteady flow model. The approach entails construction of a dynamic emulator of a 1-D river model, in the form of a transfer function model with a non-linear state-dependent transformation of the inputs. A similar approach is adopted for the observed data, so that the structural uncertainty is reflected in the discrepancy between the non-linear state-dependent functions. The approach is shown to enhance the accuracy of models with knowndeficiencies at the same time as providing well justified uncertainty estimates.

DOI:

Year: 2010