Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries |
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| Authors: | Caleb A Buahin Nikhil Sangwan Cassandra Fagan David R Maidment Jeffery S Horsburgh E James Nelson Venkatesh Merwade Curtis Rae |
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| Affiliation: | 1. Department of Civil and Environmental Engineering and Utah Water Research Laboratory, Utah State University, Logan, Utah;2. Lyle School of Civil Engineering, Purdue University, West Lafayette, Indiana;3. Department of Civil, Architectural and Environmental Engineering, University of Texas, Austin, Texas;4. Department of Civil and Environmental Engineering, Brigham Young University, Provo, Utah, 84602 |
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| Abstract: | One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods. |
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| Keywords: | ensemble flood forecasting flood inundation modeling hydraulic modeling probabilistic flood inundation maps rating curves |
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