Uncertainty in 2D hydrodynamic models from errors in roughness parameterization based on aerial images

Menno Straatsma*, Freek Huthoff

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

34 Citations (Scopus)
34 Downloads (Pure)

Abstract

In The Netherlands, 2D-hydrodynamic simulations are used to evaluate the effect of potential safety measures against river floods. In the investigated scenarios, the floodplains are completely inundated, thus requiring realistic representations of hydraulic roughness of floodplain vegetation. The current study aims at providing better insight into the uncertainty of flood water levels due to uncertain floodplain roughness parameterization. The study focuses on three key elements in the uncertainty of floodplain roughness: (1) classification error of the landcover map, (2), within class variation of vegetation structural characteristics, and (3) mapping scale. To assess the effect of the first error source, new realizations of ecotope maps were made based on the current floodplain ecotope map and an error matrix of the classification. For the second error source, field measurements of vegetation structure were used to obtain uncertainty ranges for each vegetation structural type. The scale error was investigated by reassigning roughness codes on a smaller spatial scale. It is shown that classification accuracy of 69% leads to an uncertainty range of predicted water levels in the order of decimeters. The other error sources are less relevant. The quantification of the uncertainty in water levels can help to make better decisions on suitable flood protection measures. Moreover, the relation between uncertain floodplain roughness and the error bands in water levels may serve as a guideline for the desired accuracy of floodplain characteristics in hydrodynamic models.
Original languageEnglish
Pages (from-to)324-334
JournalPhysics and Chemistry of the Earth
Volume36
Issue number7-8
DOIs
Publication statusPublished - 2011

Keywords

  • Classification error
  • Floodplain vegetation
  • Rhine distributaries
  • Within class variation
  • Scale error
  • Stochastic roughness
  • 2024 OA procedure

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