Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Abstract Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabin...

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Autores principales:	Víctor M. Santos, Thomas Wahl, Robert Jane, Shubhra K. Misra, Kathleen D. White
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	compound flooding coastal flood risk copulas extreme value analysis multivariate statistical modelling regression River protective works. Regulation. Flood control TC530-537 Disasters and engineering TA495
Acceso en línea:	https://doaj.org/article/6b8b5539b67a4870b36beb57f6f55754
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spelling	oai:doaj.org-article:6b8b5539b67a4870b36beb57f6f557542021-11-11T05:32:09ZAssessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints1753-318X10.1111/jfr3.12749https://doaj.org/article/6b8b5539b67a4870b36beb57f6f557542021-12-01T00:00:00Zhttps://doi.org/10.1111/jfr3.12749https://doaj.org/toc/1753-318XAbstract Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine‐copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre‐processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.Víctor M. SantosThomas WahlRobert JaneShubhra K. MisraKathleen D. WhiteWileyarticlecompound floodingcoastal flood riskcopulasextreme value analysismultivariate statistical modellingregressionRiver protective works. Regulation. Flood controlTC530-537Disasters and engineeringTA495ENJournal of Flood Risk Management, Vol 14, Iss 4, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	compound flooding coastal flood risk copulas extreme value analysis multivariate statistical modelling regression River protective works. Regulation. Flood control TC530-537 Disasters and engineering TA495
spellingShingle	compound flooding coastal flood risk copulas extreme value analysis multivariate statistical modelling regression River protective works. Regulation. Flood control TC530-537 Disasters and engineering TA495 Víctor M. Santos Thomas Wahl Robert Jane Shubhra K. Misra Kathleen D. White Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
description	Abstract Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine‐copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre‐processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.
format	article
author	Víctor M. Santos Thomas Wahl Robert Jane Shubhra K. Misra Kathleen D. White
author_facet	Víctor M. Santos Thomas Wahl Robert Jane Shubhra K. Misra Kathleen D. White
author_sort	Víctor M. Santos
title	Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
title_short	Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
title_full	Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
title_fullStr	Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
title_full_unstemmed	Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
title_sort	assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/6b8b5539b67a4870b36beb57f6f55754
work_keys_str_mv	AT victormsantos assessingcompoundfloodingpotentialwithmultivariatestatisticalmodelsinacomplexestuarinesystemunderdataconstraints AT thomaswahl assessingcompoundfloodingpotentialwithmultivariatestatisticalmodelsinacomplexestuarinesystemunderdataconstraints AT robertjane assessingcompoundfloodingpotentialwithmultivariatestatisticalmodelsinacomplexestuarinesystemunderdataconstraints AT shubhrakmisra assessingcompoundfloodingpotentialwithmultivariatestatisticalmodelsinacomplexestuarinesystemunderdataconstraints AT kathleendwhite assessingcompoundfloodingpotentialwithmultivariatestatisticalmodelsinacomplexestuarinesystemunderdataconstraints
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Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

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