Evaluation of the Usgs National Elevation Dataset and the Kansas Biological Survey's FLDPLN ("Floodplain") Model for Inundation Extent Estimation

Abstract

The Kansas Biological Survey has developed a library of modeled flood inundation extents, using the FLDPLN model, for major streams across Kansas that can be accessed in near real-time to provide valuable information to disaster responders. This research 1) examines the USGS National Elevation Dataset (NED) and evaluates the affects of errors in the elevation data on flood inundation extent estimation and 2) evaluates the capabilities and limitations of the FLDPLN model for inundation extent estimation. Results showed that, although the accuracy of pre-LiDAR NED is better than published figures, modeled flood extents vary significantly when using LiDAR-derived vs. pre-LiDAR NED elevation data inputs. Comparison of modeled flood extents for HEC-RAS, HAZUS, and FLDPLN models for both hypothetical and empirical floods events showed greater correspondence at high flood stages. Improved elevation data and empirical low flood data would offer improved flood extent estimates and more robust model evaluation.