129674 20241125101146.0 doi 10.1016/j.jhydrol.2023.130157 sideral 135920 ART-2023-135920 eng Gangrade, Sudershan Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework 2023 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 5.9 2023 ENGINEERING, CIVIL 14 / 182 = 0.077 2023 Q1 T1 WATER RESOURCES 8 / 128 = 0.062 2023 Q1 T1 GEOSCIENCES, MULTIDISCIPLINARY 19 / 254 = 0.075 2023 Q1 T1 1.764 2023 Water Science and Technology 2023 Q1 11.0 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ghimire, Ganesh R. Kao, Shih-Chieh Morales-Hernández, Mario Universidad de Zaragoza (orcid)0000-0001-6961-7250 Tavakoly, Ahmad A. Gutenson, Joseph L. Sparrow, Kent H. Darkwah, George K. Kalyanapu, Alfred J. Follum, Michael L. 5001 600 Universidad de Zaragoza Dpto. Ciencia Tecnol.Mater.Fl. Área Mecánica de Fluidos 625, Part B (2023), 130157 [16 pp.] J. hydrol. Journal of Hydrology 0022-1694 1843529 http://zaguan.unizar.es/record/129674/files/texto_completo.pdf Versión publicada 2341845 http://zaguan.unizar.es/record/129674/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:129674 articulos driver 2024-11-22-12:04:40 ARTICLE