Improving flood hazard prediction models

Inaccurate components of hydrodynamic models can lead to inaccurate flood hazard simulations, particularly when models are applied to floods larger than the model calibration conditions, yet hazard information is usually sought for events which are more extreme than those documented by accurate hist...

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Bibliographic Details
Published inInternational journal of river basin management Vol. 16; no. 4; pp. 449 - 456
Main Author Smart, G. M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.10.2018
Taylor & Francis Ltd
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ISSN1571-5124
1814-2060
1814-2060
DOI10.1080/15715124.2017.1411923

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Summary:Inaccurate components of hydrodynamic models can lead to inaccurate flood hazard simulations, particularly when models are applied to floods larger than the model calibration conditions, yet hazard information is usually sought for events which are more extreme than those documented by accurate historical measurements. Model components subject to error include the input hydrographs (and their exceedance probabilities), input roughness maps, representations of topography, bathymetry, the numerical solver and the model flow resistance equation. There is particular uncertainty surrounding the treatment of roughness and flow resistance in 2D flood models. Such models typically have low-resolution mapping of roughness compared to the mapping of topography. Significant areas with high roughness and low flow depth can occur with flood rise and fall, particularly with 'direct rainfall' models. Conventional flow resistance equations break down under these high relative roughness conditions. The formulation of friction within a hydrodynamic model code and derivation of depth-averaged flow resistance equations for 2D models are investigated. This study gives recommendations for improved mapping of roughness, new equations for better representing flow resistance in the modelling code, a nomograph for converting the more common 'n' roughness values to 'Z o ' roughness values and makes suggestions for better communication of model results.
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ISSN:1571-5124
1814-2060
1814-2060
DOI:10.1080/15715124.2017.1411923