Uncertainty Quantification in Flood Inundation Mapping Using Generalized Likelihood Uncertainty Estimate and Sensitivity Analysis

Abstract

The process of creating flood inundation maps is affected by uncertainties in data, modeling approaches, parameters, and geoprocessing tools. Generalized likelihood uncertainty estimation (GLUE) is one of the popular techniques used to represent uncertainty in model predictions through Monte Carlo analysis coupled with Bayesian estimation. The objectives of this study are to (1) compare the uncertainty arising from multiple variables in flood inundation mapping using Monte Carlo simulations and GLUE and (2) investigate the role of subjective selection of the GLUE likelihood measure in quantifying uncertainty in flood inundation mapping. The role of the flow, topography, and roughness coefficient is investigated on the output of a one-dimensional Hydrologic Engineering Center–River Analysis System (HEC–RAS) model and flood inundation map for an observed flood event on East Fork White River near Seymour, Indiana (Seymour reach) and Strouds Creek in Orange County, North Carolina. Performance of GLUE is assessed by selecting three likelihood functions including the sum of absolute error (SAE) in water surface elevation and inundation width, sum of squared error (SSE) in water surface elevation and inundation width, and a statistic (