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Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%) and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5 m, 1 m, 2 m) and standard deviation (1 m, 2 m, 4 m). The bias reduced to ~0.5 m or below in all the cases: the reduction in bias varied from 11% to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40% to 49%. The reduction in RMSE ranged between 35% and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ~30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions