The use of remotely sensed land cover to derive floodplain friction coefficients for flood inundation modelling

Abstract

Abstract:Remotely sensed land cover was used to generate spatially-distributed friction coefficients for use in a two-dimensional model of flood inundation. Such models are at the forefront of research into the prediction of river flooding. Standard practice, however, is to use single (static) friction coefficients on both the channel and floodplain, which are varied in a calibration procedure to provide a "best fit" to a known inundation extent. Spatially-distributed friction provides a physically grounded estimate of friction that does not require fitting to a known inundation extent, but which can be fitted if desired. Remote sensing offers the opportunity to map these friction coefficients relatively straightforwardly and for low cost. Inundation was predicted using the LISFLOOD-FP model for a reach on the River Nene, UK. Friction coefficients were produced from land cover predicted from Landsat TM imagery using both ML and fuzzy c-means classifiction. The elevetion data used were from combined contour and differential global positioning system (GPS) elevation data. Predicted inundation using spatiallydistributed and static friction were compared. Spatially-distributed friction had the greatest effect on the timing of flood inundation, but a small effect on predicted inundation extent. The results indicate that spatially-distributed friction should be considered where the timing of initial flooding (e.g. for early warning) is important.

Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data

“…They noted that the computational cost is considerably reduced in the inertial formulation compared to a diffusive wave approximation in high resolution model simulations. In terms of friction parameters, whilst many flood inundation models have commonly used a uniform roughness coefficient for the floodplain (Bates and De Roo, 2000;Horritt and Bates, 2002), some researchers have used spatially-distributed friction in both rural and urban areas Wilson and Atkinson, 2007;Schubert at al., 2008;Gallegos et al, 2009). For the urban case, Gallegos et al (2009) noted that a spatially uniform resistance parameter lead to poor stream flow accuracy compared to a spatially distributed parameter.…”

Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood Modelling

“…A map showing the Manning's n roughness coefficients for the different channel and floodplain elements was produced from the orthoimage of the area and field survey establishing the values presented in Table 2 [11,13,[20][21].…”

“…The roughness coefficients are a parameter of the hydraulic model, which, when correctly defined, allows one to obtain a distribution of the velocity and the shear stress very close to the reality, and a good prediction of free surface flow. Because they result from the combination of runoff surface characteristics that are influenced by several flow characteristics, such as water depth and velocity, roughness coefficients are a very important input data for the flood hydraulic modelling [10][11][12]. A land use map produced with high resolution aerial images can be used to represent the spatial distribution of the roughness coefficients in the flooded areas [11,13].…”

Modeling and visualizing uncertainties of flood boundary delineation: algorithm for slope and DEM resolution dependencies of 1D hydraulic models

“…Although modelers have acknowledged the implications of roughness for a long time, it is not until recent years that any efforts have been made to see how much this type of uncertainty affects the results (see e.g. Pappenberger et al 2005;Werner et al 2005;Casas et al 2006;Schumann et al 2007;Wilson and Atkinson 2007;Brandt 2009;Warmink et al 2013;Wu in press). This is probably due to the type of uncertainty which earlier has been considered the main constraint for successful modeling, viz.…”