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Bayesian calibration of a flood inundation model using spatial data
Author(s) -
Hall Jim W.,
Manning Lucy J.,
Hankin Robin K. S.
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2009wr008541
Subject(s) - calibration , bayesian probability , flood myth , flooding (psychology) , computer science , bayesian inference , posterior probability , gaussian network model , data mining , probabilistic logic , gaussian , statistics , environmental science , remote sensing , mathematics , artificial intelligence , geography , psychology , physics , archaeology , quantum mechanics , psychotherapist
Bayesian theory of model calibration provides a coherent framework for distinguishing and encoding multiple sources of uncertainty in probabilistic predictions of flooding. This paper demonstrates the use of a Bayesian approach to computer model calibration, where the calibration data are in the form of spatial observations of flood extent. The Bayesian procedure involves generating posterior distributions of the flood model calibration parameters and observation error, as well as a Gaussian model inadequacy function, which represents the discrepancy between the best model predictions and reality. The approach is first illustrated with a simple didactic example and is then applied to a flood model of a reach of the river Thames in the UK. A predictive spatial distribution of flooding is generated for a flood of given severity.