000129674 001__ 129674
000129674 005__ 20240104102233.0
000129674 0247_ $$2doi$$a10.1016/j.jhydrol.2023.130157
000129674 0248_ $$2sideral$$a135920
000129674 037__ $$aART-2023-135920
000129674 041__ $$aeng
000129674 100__ $$aGangrade, Sudershan
000129674 245__ $$aUnraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework
000129674 260__ $$c2023
000129674 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129674 5203_ $$aFlood 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.
000129674 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000129674 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000129674 700__ $$aGhimire, Ganesh R.
000129674 700__ $$aKao, Shih-Chieh
000129674 700__ $$0(orcid)0000-0001-6961-7250$$aMorales-Hernández, Mario$$uUniversidad de Zaragoza
000129674 700__ $$aTavakoly, Ahmad A.
000129674 700__ $$aGutenson, Joseph L.
000129674 700__ $$aSparrow, Kent H.
000129674 700__ $$aDarkwah, George K.
000129674 700__ $$aKalyanapu, Alfred J.
000129674 700__ $$aFollum, Michael L.
000129674 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000129674 773__ $$g625, Part B (2023), 130157 [16 pp.]$$pJ. hydrol.$$tJournal of Hydrology$$x0022-1694
000129674 8564_ $$s1843529$$uhttps://zaguan.unizar.es/record/129674/files/texto_completo.pdf$$yVersión publicada
000129674 8564_ $$s2341845$$uhttps://zaguan.unizar.es/record/129674/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000129674 909CO $$ooai:zaguan.unizar.es:129674$$particulos$$pdriver
000129674 951__ $$a2024-01-04-09:09:00
000129674 980__ $$aARTICLE