Sensitivity of a flood inundation model to spatially-distributed friction

Abstract

In this paper, land cover was predicted from Landsat TM imagery and used to generate spatially-distributed friction coefficients. Flood inundation was then predicted using the raster-based model LISFLOOD-FP, based on friction and three different elevation models. Sensitivity of LISFLOOD-FP to spatially-distributed friction was assessed. It was found that effect of friction on the flood wave is small when compared to the underlying elevation, but is greatest during the recession phase of the hydrograph. Hydraulic models of channel and overland flow allow river discharge to be related to flood inundation extent, by simu- lating flooding based on a scenario discharge. An important boundary condition for such models is surface friction. In particular, floodplain land cover is related to friction that affects the movement of the flood wave. However, the selection of appropriate friction coefficients for hydraulic models is difficult and it is recognised that floodplain friction coefficients have considerable uncertainty and sensitivity associated with them (1). Specific problems are (i) a lack of data sources, (ii) the often coarse spatial resolution of data and (iii) the use of stationary models (e.g., use of average friction over the whole catchment). Remote sensing can provide spatially distributed estimates of land cover, allowing a more informative and accurate representation of floodplain friction in flood inundation mod- els. However, hard classification may not provide sufficiently detailed land cover data at the sub-pixel scale. For example, where Landsat Thematic Mapper (TM) (spatial resolution of 30 m) imagery is used and the flood is only several hundred metres across, hard classification may be inappropriate. There- fore, in this paper, soft classification was used to estimate friction coefficients on a continuous scale for each cell on the floodplain. The objective was then to assess the sensitivity of a flood inundation model to uncertainty in these coefficients.