Flood-inundation modeling in an operational context: sensitivity to topographic resolution and Manning’s n Sarah Praskievicz, Shawn Carter, Juzer Dhondia and Michael Follum ABSTRACT Streamflow forecasts from operational hydrologic models can be converted into forecasts of flood- inundation extent using either physically based hydraulic models or simpler terrain-based approaches. Two factors that influence simulated flood-inundation extent are spatial resolution of topographic data and in-channel and overland-flow roughness characterized by the Manning’s n parameter. Here, AutoRoute, a raster-based flood-inundation model, was used to simulate two recent flood events in Florida (a forested floodplain) and Texas (an urban floodplain) using two different topographic resolutions and a range of Manning’s n values. The AutoRoute-simulated flood- inundation extents were evaluated using observed extents from remotely sensed imagery. For comparison, the same flood events were also simulated using a one-dimensional Hydrologic Engineering Center River Analysis System (HEC-RAS) model. Results indicated that model performance was much improved with higher topographic resolution for the forested floodplain site and that the urban site was more sensitive to Manning’s n. For the three different rivers analyzed, the fit for HEC-RAS was 5–10% higher than that for AutoRoute. Despite being only slightly less accurate than HEC-RAS in its simulation of flood extent, AutoRoute was much simpler to set up and required less computational time to run. Key words | AutoRoute, emergency response, flooding, HEC-RAS, inundation modeling, Landsat HIGHLIGHTS • Applies a simple terrain-based model for use in operational flood forecasting. • Compares the performance of a simple terrain-based model with that of a standard physically based hydraulic model. • Uses the observed inundation extent from remote sensing to quantitatively validate model performance. • Assesses model sensitivity to topographic resolution and Manning’s n. • Provides a framework that can potentially be adapted for near real-time operational flood forecasting for large spatial domains. Sarah Praskievicz (corresponding author) Department of Geography, Environment, and Sustainability, The University of North Carolina at Greensboro, 237 Graham Bldg., 1009 Spring Garden St., Greensboro, NC 27412, USA E-mail: sjpraski@uncg.edu Shawn Carter National Oceanic and Atmospheric Administration, National Weather Service, Office of Water Prediction, Water Prediction Operations Division, 205 Hackberry Lane, Tuscaloosa, AL 35401, USA Juzer Dhondia University Corporation for Atmospheric Research, National Water Center, Office of Water Prediction, National Oceanic and Atmospheric Administration, 205 Hackberry Ln., Tuscaloosa, AL 35401, USA Michael Follum Coastal and Hydraulics Laboratory, Engineer Research and Development Center, United States Army Corps of Engineers, 3909 Halls Ferry Rd., Vicksburg, MS 39180-6199, USA Current address: Water and Civil Works Branch, Wyoming Area Office, Bureau of Reclamation, Mills, WY 82644, USA INTRODUCTION Floods are among the deadliest and most destructive natural disasters globally. To save lives and protect property, emer- gency responders must have accurate, real-time, high- resolution forecasts of flood-inundation extent. Such fore- casts allow emergency responders to make the best possible decisions about evacuations and staging of resources (Fagan ). Because flood events range from local to watershed-scale, efforts within the United States are ongoing to develop operational hydrologic models that can forecast streamflow for a continental domain, at high 1338 © IWA Publishing 2020 Journal of Hydroinformatics | 22.5 | 2020 doi: 10.2166/hydro.2020.005 Downloaded from http://iwaponline.com/jh/article-pdf/22/5/1338/763301/jh0221338.pdf by guest on 23 November 2021