The Usability of Remote Sensing Data for Flood Inundation Modelling: a Case Study of the Mississippi River

Abstract: The probability and impact of flooding is projected to increase in the future. This is due to climate and land-use changes (e.g. urbanization) in addition to the ongoing socioeconomic development of many floodplain areas. Exploiting the increasing availability of satellite data for flood inundation modelling will allow mapping floods in remote, data-poor areas to lower costs, and thereby make it possible to estimate flood risks in areas that today lack the economic resources needed for supporting risk assessment. In this context, this study has investigated the potentials and limitations of using low-cost, global remote sensing data (i.e. SRTM) to support flood inundation modelling. To this end, a case study of a river reach along the Mississippi was exploited. In particular, two flood inundation models were built by using the same 2D hydraulic model code (LISFLOOD-FP), but with two different topographical inputs, i.e. high quality/accuracy LiDAR topography data and the freely available SRTM topography data. The LiDAR data was lowered to the same resolution as the SRTM data and the two models were run with the resolution of 83x83 m2 . Thereafter, the models were compared by simulating two historical flood events of different magnitude. The comparison of the two models showed that flood inundation modelling with satellite data is more accurate (closer to the reference model, i.e. LiDAR-based model) for the higher magnitude flood event than for the lower magnitude flood event. This was attributed to the relatively reduced importance of micro topography during bigger flood events. An area-based performance measure gave a value of the correspondence (i.e. the fit) between the predicted flood extents for the two models. The areas/pixels were reclassified in ARC GIS to flooded or dry. Thereafter, areas flooded in both the LiDAR and the SRTM simulations were divided by the sum of the areas flooded in both or in one of the simulations (LiDAR or SRTM). From this procedure the fit could be determined, where a fit of 100 % would mean that the simulations had predicted the same flood extents. For the high magnitude flood event simulated in this study, the fit in terms of flood extent between the LiDAR-based and the SRTM-based model was 72 %, while the fit for the smaller flood was only 38 %. In this study, model calibration was preformed manually because of limited availability of time and computational power. However, this is not considered a major limitation as the work does not aim to make a faultless model of this river reach of the Mississippi, but rather to determine the potentials and limitations of SRTM topography data in supporting flood inundation modelling. Additional studies of rivers systems with different properties, flood magnitudes, vegetation covers and river scales should be conducted, to further validate the usability of remote sensing data for flood inundation modelling.