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

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.

[1]  Sharon L. Lohr,et al.  Sampling: Design and Analysis , 1999 .

[2]  V. T. Chow Open-channel hydraulics , 1959 .

[3]  Martin J. Baptist,et al.  Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing , 2008 .

[4]  L. Mertes,et al.  Remote sensing of riverine landscapes , 2002 .

[5]  Paul D. Bates,et al.  Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling , 2007 .

[6]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[7]  C. V. D. Sande,et al.  A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment , 2003 .

[8]  M. Horritt A linearized approach to flow resistance uncertainty in a 2-D finite volume model of flood flow , 2006 .

[9]  Stephen J. Walsh,et al.  Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA , 2001, Plant Ecology.

[10]  Keith Beven,et al.  Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data , 1998 .

[11]  K. Beven,et al.  Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS) , 2005 .

[12]  Ling Qian,et al.  5th International Conference on Hydroinformatics , 2002 .

[13]  D. Klopstra,et al.  Analytical model for hydraulic roughness of submerged vegetation , 1997 .

[14]  P. Bates,et al.  Evaluation of 1D and 2D numerical models for predicting river flood inundation , 2002 .

[15]  M. Straatsma Quantitative Mapping of Hydrodynamic Vegetation Density of Floodplain Forests Under Leaf-off Conditions Using Airborne Laser Scanning , 2008 .

[16]  P. Bates,et al.  A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. , 2010 .

[17]  Menno Straatsma,et al.  Error propagation in hydrodynamics of lowland rivers due to uncertainty in vegetation roughness parameterization , 2010 .

[18]  B. Jansen,et al.  Water ecotope classification for integrated water management in the Netherlands , 2003 .

[19]  Norbert Pfeifer,et al.  Optimisation of LiDAR derived terrain models for river flow modelling , 2008 .

[20]  D. Mason,et al.  Image processing of airborne scanning laser altimetry data for improved river flood modelling , 2001 .

[21]  J. Brasington,et al.  Object-based land cover classification using airborne LiDAR , 2008 .

[22]  Menno Straatsma,et al.  Extracting structural characteristics of herbaceous floodplain vegetation under leaf‐off conditions using airborne laser scanner data , 2007 .

[23]  Paul D. Bates,et al.  The Utility of Spaceborne Radar to Render Flood Inundation Maps Based on Multialgorithm Ensembles , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Matthew D. Wilson,et al.  Simple spatially-distributed models for predicting flood inundation: A review , 2007 .

[25]  P. Bates,et al.  Identifiability of distributed floodplain roughness values in flood extent estimation , 2005 .

[26]  J. Clevers,et al.  Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data , 2007 .

[27]  G.B.M. Heuvelink,et al.  Proceedings of the 4th international symposium on spatial accuracy. Assessment in natural resources and environmental sciences , 2000 .

[28]  A unified formulation for the three-dimensional shallow water equations using orthogonal co-ordinates: theory and application , 2005 .

[29]  Paul D. Bates,et al.  Floodplain friction parameterization in two‐dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry , 2003 .

[30]  W. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .