Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework
Abstract
Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundationsmore »
- Authors:
-
[1];
[1];
[1];
[2];
[3];
[4];
[5];
[6]
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Univ. of Zaragoza (Spain)
- US Army Engineer Research and Development Center (ERDC), Vicksburg, MS (United States); Univ. of Maryland, College Park, MD (United States)
- US Army Engineer Research and Development Center (ERDC), Vicksburg, MS (United States)
- Tennessee Technological Univ., Cookeville, TN (United States)
- Follum Hydrologic Solutions, LLC., Casper, WY (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
- OSTI Identifier:
- 2205446
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Hydrology
- Additional Journal Information:
- Journal Volume: 625; Journal Issue: B; Journal ID: ISSN 0022-1694
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; flood inundation; TRITON; AutoRoute; central Tennessee floods; error propagation; high-performance computing
Citation Formats
Gangrade, Sudershan, Ghimire, Ganesh R., Kao, Shih-Chieh, Morales-Hernández, Mario, Tavakoly, Ahmad A., Gutenson, Joseph L., Sparrow, Kent H., Darkwah, George K., Kalyanapu, Alfred J., and Follum, Michael L. Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework. United States: N. p., 2023.
Web. doi:10.1016/j.jhydrol.2023.130157.
Gangrade, Sudershan, Ghimire, Ganesh R., Kao, Shih-Chieh, Morales-Hernández, Mario, Tavakoly, Ahmad A., Gutenson, Joseph L., Sparrow, Kent H., Darkwah, George K., Kalyanapu, Alfred J., & Follum, Michael L. Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework. United States. https://doi.org/10.1016/j.jhydrol.2023.130157
Gangrade, Sudershan, Ghimire, Ganesh R., Kao, Shih-Chieh, Morales-Hernández, Mario, Tavakoly, Ahmad A., Gutenson, Joseph L., Sparrow, Kent H., Darkwah, George K., Kalyanapu, Alfred J., and Follum, Michael L. Thu .
"Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework". United States. https://doi.org/10.1016/j.jhydrol.2023.130157. https://www.osti.gov/servlets/purl/2205446.
@article{osti_2205446,
title = {Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework},
author = {Gangrade, Sudershan and Ghimire, Ganesh R. and Kao, Shih-Chieh and Morales-Hernández, Mario and Tavakoly, Ahmad A. and Gutenson, Joseph L. and Sparrow, Kent H. and Darkwah, George K. and Kalyanapu, Alfred J. and Follum, Michael L.},
abstractNote = {Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.},
doi = {10.1016/j.jhydrol.2023.130157},
journal = {Journal of Hydrology},
number = B,
volume = 625,
place = {United States},
year = {Thu Sep 14 00:00:00 EDT 2023},
month = {Thu Sep 14 00:00:00 EDT 2023}
}
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