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Influence of uncertain boundary conditions and model structure on flood inundation predictions.

Research output: Contribution to journalJournal articlepeer-review

  • Florian Pappenberger
  • Patrick Matgen
  • Keith J. Beven
  • Jean-Baptiste Henry
  • Laurent Pfister
  • Paul de Fraipont
<mark>Journal publication date</mark>10/2006
<mark>Journal</mark>Advances in Water Resources
Issue number10
Number of pages20
Pages (from-to)1430-1449
Publication StatusPublished
<mark>Original language</mark>English


In this study, the GLUE methodology is applied to establish the sensitivity of flood inundation predictions to uncertainty of the upstream boundary condition and bridges within the modelled region. An understanding of such uncertainties is essential to improve flood forecasting and floodplain mapping. The model has been evaluated on a large data set. This paper shows uncertainty of the upstream boundary can have significant impact on the model results, exceeding the importance of model parameter uncertainty in some areas. However, this depends on the hydraulic conditions in the reach e.g. internal boundary conditions and, for example, the amount of backwater within the modelled region. The type of bridge implementation can have local effects, which is strongly influenced by the bridge geometry (in this case the area of the culvert). However, the type of bridge will not merely influence the model performance within the region of the structure, but also other evaluation criteria such as the travel time. This also highlights the difficulties in establishing which parameters have to be more closely examined in order to achieve better fits. In this study no parameter set or model implementation that fulfils all evaluation criteria could be established. We propose four different approaches to this problem: closer investigation of anomalies; introduction of local parameters; increasing the size of acceptable error bounds; and resorting to local model evaluation. Moreover, we show that it can be advantageous to decouple the classification into behavioural and non-behavioural model data/parameter sets from the calculation of uncertainty bounds