Evaluating Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCIFRIM)-based Flood Inundation against multi-satellite observation

Floods are the costliest and most frequently occurring natural disasters. One of the key factors in preventing and reducing losses is providing a reliable flood map. However, the uncertainty associated with either flood inundation model or data, specifically the Digital Elevation Model (DEM), may have adverse effects on the reliability of flood stage and inundation maps. Therefore, a systematic understanding of the uncertainty is necessary. In this study, an attempt is made to assess whether models are more susceptible to the uncertainties or the data itself. In order to do this, a SCIFRIM (Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model) is employed, utilizing a list of DEM datasets to reconstruct the October 2024 Valencia flood event. The modelled flood extents were validated against those derived from multi-sensor remote sensing data. The Critical Success Index (CSI) was calculated to assess the agreement between observed and modelled flood extents, yielding values of 0.49 and 0.59 for October 30th and 31st, respectively, when combining SCIFRIM and Lidar-DEM. Additionally, a multi-model comparison has been performed between SCIFRIM and CaMa-Flood (Catchment-based Macro-scale Floodplain), HEC-RAS (Hydrologic Engineering Center's River Analysis System), and TUFLOW (Two-dimensional Unsteady FLOW), demonstrating its relevance in terms of outputs (flood extent and stage) and model runtime. The findings demonstrate that the proposed modeling framework offers a reliable approach for flood assessment. It has great potential to support rapid assessment and decision-making in data-scarce regions.