Development of a Flood Model Based on Globally-Available Satellite Data for the Papaloapan River, Mexico
Abstract: Flood inundation modelling is highly dependent on an accurate representation of floodplain topography. These remotely sensed accurate data are often not available or expensive, especially in developing countries. As an alternative, freely available Digital Elevation Models (DEMs), such as the near-global Shuttle Radar Topography Mission (SRTM) data, have come into the focus of flood modellers. To what extent these low-resolution data can be exploited for hydraulic modelling is still an open research question. This benchmarking study investigated the potentials and limitations of the SRTM data set for flood inundation modelling on the example of the Papaloapan River, Mexico. Furthermore the effects of vegetation signal removal from the SRTM DEM as in Baugh et al. (2010) were tested. A reference model based on a light detection and ranging (LiDAR) DEM was set up with the model code LISFLOOD-FP and run for two flood events. Test models based on SRTM DEMs were run and output flood extents compared to the reference model by applying a measure of fit. This measure of fit, which was based on binary wet/dry maps of both model outputs, gave information on how well the test models simulated the flood inundation extents compared to the reference model by giving a percentage of the model performance from theoretically 0 to 100 %. SRTM-based models could not reproduce the promising results of previous studies. Flood extents were mostly underestimated and commonly flooded areas were almost exclusively made up out of the main channel surface. One of the reasons for this likely was the much steeper slope of the SRTM DEM as opposed to the LiDAR DEM where water probably was conducted much faster though the main channel. Too high bank cells as well as generally more pronounced elevation differences of the SRTM DEM throughout the whole floodplain were another problem of the SRTM DEM preventing accurate flood inundation simulations. Vegetation signal removal was successful to a certain degree improving the fit by about 10 %. However, a realistic shape of flood extent could not be simulated due to too big pixel sizes of the used canopy height data set. Also, the conditioned models overestimated flooded areas with increasing vegetation signal removal, rendering some of the models useless for comparison, as water leaving the model domain could not be accounted for in the measure of fit. This study showed the limitations of SRTM data for flood inundation modeling where an accurate approximation of the river slope as well as accurately captured bank cells and floodplain topography are crucial for the simulated outcome. Vegetation signal removal has been shown to be potentially useful but should rather be applied on more densely covered catchments.
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