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Assessing the reliability of probabilistic flood inundation model predictions

Stephens, E. and Bates, P. (2015) Assessing the reliability of probabilistic flood inundation model predictions. Hydrological Processes, 29 (19). pp. 4264-4283. ISSN 08856087

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To link to this item DOI: 10.1002/hyp.10451

Abstract/Summary

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD-FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post-peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Archaeology, Geography and Environmental Science > Earth Systems Science
Faculty of Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
ID Code:46597
Publisher:Wiley Online Library

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