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A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling

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A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling. / Ahmadisharaf, Ebrahim; Kalyanapu, Alfred; Bates, Paul.

In: Hydrological Sciences Journal, 22.10.2018.

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Ahmadisharaf, Ebrahim ; Kalyanapu, Alfred ; Bates, Paul. / A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling. In: Hydrological Sciences Journal. 2018.

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@article{4d11521b0da4496ab7ed120073268838,
title = "A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling",
abstract = "Prediction of design hydrographs is key in floodplain mapping using hydraulic models, which are either steady state or unsteady. The former, which require only an input peak, substantially overestimate the volume of water entering the floodplain compared to the more realistic dynamic case simulated by the unsteady models that require the full hydrograph. Past efforts to account for the uncertainty of boundary conditions using unsteady hydraulic modeling have been based largely on a joint flood frequency–shape analysis, with only a very limited number of studies using hydrological modeling to produce the design hydrographs. This study therefore presents a generic probabilistic framework that couples a hydrological model with an unsteady hydraulic model to estimate the uncertainty of flood characteristics. The framework is demonstrated on the Swannanoa River watershed in North Carolina, USA. Given its flexibility, the framework can be applied to study other sources of uncertainty in other hydrological models and watersheds.",
keywords = "floodplain mapping, hydrologic modeling, unsteady hydraulic modeling, uncertainty analysis",
author = "Ebrahim Ahmadisharaf and Alfred Kalyanapu and Paul Bates",
year = "2018",
month = "10",
day = "22",
doi = "10.1080/02626667.2018.1525615",
language = "English",
journal = "Hydrological Sciences Journal",
issn = "0262-6667",
publisher = "Taylor & Francis Group",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling

AU - Ahmadisharaf, Ebrahim

AU - Kalyanapu, Alfred

AU - Bates, Paul

PY - 2018/10/22

Y1 - 2018/10/22

N2 - Prediction of design hydrographs is key in floodplain mapping using hydraulic models, which are either steady state or unsteady. The former, which require only an input peak, substantially overestimate the volume of water entering the floodplain compared to the more realistic dynamic case simulated by the unsteady models that require the full hydrograph. Past efforts to account for the uncertainty of boundary conditions using unsteady hydraulic modeling have been based largely on a joint flood frequency–shape analysis, with only a very limited number of studies using hydrological modeling to produce the design hydrographs. This study therefore presents a generic probabilistic framework that couples a hydrological model with an unsteady hydraulic model to estimate the uncertainty of flood characteristics. The framework is demonstrated on the Swannanoa River watershed in North Carolina, USA. Given its flexibility, the framework can be applied to study other sources of uncertainty in other hydrological models and watersheds.

AB - Prediction of design hydrographs is key in floodplain mapping using hydraulic models, which are either steady state or unsteady. The former, which require only an input peak, substantially overestimate the volume of water entering the floodplain compared to the more realistic dynamic case simulated by the unsteady models that require the full hydrograph. Past efforts to account for the uncertainty of boundary conditions using unsteady hydraulic modeling have been based largely on a joint flood frequency–shape analysis, with only a very limited number of studies using hydrological modeling to produce the design hydrographs. This study therefore presents a generic probabilistic framework that couples a hydrological model with an unsteady hydraulic model to estimate the uncertainty of flood characteristics. The framework is demonstrated on the Swannanoa River watershed in North Carolina, USA. Given its flexibility, the framework can be applied to study other sources of uncertainty in other hydrological models and watersheds.

KW - floodplain mapping

KW - hydrologic modeling

KW - unsteady hydraulic modeling

KW - uncertainty analysis

U2 - 10.1080/02626667.2018.1525615

DO - 10.1080/02626667.2018.1525615

M3 - Article

JO - Hydrological Sciences Journal

JF - Hydrological Sciences Journal

SN - 0262-6667

ER -