Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Sudershan Gangrade; Ganesh R. Ghimire; Shih Chieh Kao; Mario Morales-Hernández; Ahmad A. Tavakoly; Joseph L. Gutenson; Kent H. Sparrow; George K. Darkwah; Alfred J. Kalyanapu; Michael L. Follum

doi:10.1016/j.jhydrol.2023.130157

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Sudershan Gangrade, Ganesh R. Ghimire, Shih Chieh Kao, Mario Morales-Hernández, Ahmad A. Tavakoly, Joseph L. Gutenson, Kent H. Sparrow, George K. Darkwah, Alfred J. Kalyanapu, Michael L. Follum

Water Resources Science & Engineering

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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 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.

Original language	English
Article number	130157
Journal	Journal of Hydrology
Volume	625
DOIs	https://doi.org/10.1016/j.jhydrol.2023.130157
State	Published - Oct 2023

Funding

This study was supported by the US Air Force Numerical Weather Modeling Program. The research used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory (ORNL), which is a Department of Energy (DOE) Office of Science User Facility. The High-Water Marks data was collected and compiled by US Army Corps of Engineers Nashville District. We thank Araz Baranji (USACE) for sharing the high water marks data with us. ORNL is managed by UT-Battelle, LLC for DOE under Contract DE-AC05-00OR22725. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://energy.gov/downloads/doe-public-access-plan ). This study was supported by the US Air Force Numerical Weather Modeling Program. The research used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory (ORNL), which is a Department of Energy (DOE) Office of Science User Facility. The High-Water Marks data was collected and compiled by US Army Corps of Engineers Nashville District. We thank Araz Baranji (USACE) for sharing the high water marks data with us. ORNL is managed by UT-Battelle, LLC for DOE under Contract DE-AC05-00OR22725. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes.

Keywords

AutoRoute
Central Tennessee floods
Error propagation
Flood inundation
High-performance computing
TRITON

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10.1016/j.jhydrol.2023.130157

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Gangrade, S., Ghimire, G. R., Kao, S. C., Morales-Hernández, M., Tavakoly, A. A., Gutenson, J. L., Sparrow, K. H., Darkwah, G. K., Kalyanapu, A. J., & Follum, M. L. (2023). Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework. Journal of Hydrology, 625, Article 130157. https://doi.org/10.1016/j.jhydrol.2023.130157

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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 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.",

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author = "Sudershan Gangrade and Ghimire, \{Ganesh R.\} and Kao, \{Shih Chieh\} and Mario Morales-Hern{\'a}ndez and Tavakoly, \{Ahmad A.\} and Gutenson, \{Joseph L.\} and Sparrow, \{Kent H.\} and Darkwah, \{George K.\} and Kalyanapu, \{Alfred J.\} and Follum, \{Michael L.\}",

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AU - Ghimire, Ganesh R.

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AU - Morales-Hernández, Mario

AU - Tavakoly, Ahmad A.

AU - Gutenson, Joseph L.

AU - Sparrow, Kent H.

AU - Darkwah, George K.

AU - Kalyanapu, Alfred J.

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AB - 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.

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ER -

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

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