| abstract | Hydrodynamic modeling is a central tool for flood risk management and lies at the base for the
determination of deposition of sediment and heavy metals. In recent years, considerable effort
has been made on the development of 2D and 3D hydrodynamic models that accurately simulate
overbank flow patterns and predict extreme flood water levels in rivers and floodplains (e.g.,
Baptist et al., (2007) and Stoesser et al. (2003). In addition to surface topography (Marks and
Bates, 2000), hydrodynamic roughness of the floodplain surface is the key input parameter of
these models. Currently, no accurate, spatially distributed and quantitative method exists to parameterize hydrodynamic roughness of the floodplains as input for models, leading to
uncertainty in flood water levels as well as deposition patterns.
Vegetation roughness is dependent on vegetation structural characteristics like vegetation height
and density, rigidity of the stems and the presence of leaves (Kouwen and Li, 1980) . To provide
hydrodynamic modelers with reliable input, the spatial and temporal distribution of surface
characteristics is needed. This requires accurate and fast monitoring methods that can cover large
floodplain areas. Various remote sensing data may provide information on vegetation type,
structure and dynamics, using vegetation classification. While the spatial resolution and the level
of detail of the classification vary with the type of remote sensing data, in all cases vegetation
classes are converted to vegetation structure, which leads to undesirable loss of within-class
variation. In contrast, Airborne Laser Scanning (ALS) enables direct extraction of vegetation
structural characteristics such as vegetation height, biomass, basal area, and leaf area index
(Cobby et al., 2001; Lim et al., 2003). However, ALS was never tested for floodplain vegetation
under leaf-off conditions representative for winter floods, which has specific problems of
inundated ground surface and small herbaceous vegetation elements which cannot be detected.
Any mapping strategy requires accurate field reference data for validation of remote sensing
information products. Vegetation density is a difficult parameter to measure in the field, due to
the presence of side branches, complex stem shapes and leaves (Dudley et al., 1998; Zehm et al.,
2003). In addition, none of the current field methods generates information on the threedimensional
distribution of vegetation density. Especially for herbaceous vegetation, no accurate
method exists to determine density in the field. Therefore a large uncertainty remains in the input
to hydrodynamic models. On the other hand, the output of roughness models is mostly
calibrated in flume facilities, where high flow velocities are used, combined with steep water
surface slopes and low water depths. These circumstances are not representative for flow
conditions on lowland floodplains. Current in situ measurements of vegetation roughness using
fixed current meters and water level meters are inadequate to measure the relevant hydrodynamic
parameters such as water depth, water surface slope and the 3D flow field. This lack of
calibration data further increases the uncertainty in the hydrodynamic modeling. |
