Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncer...
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Autor*in:	Zahra Zahmatkesh [verfasserIn] Shasha Han [verfasserIn] Paulin Coulibaly [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	probabilistic floodplain mapping uncertainty quantification model calibration climate change

Übergeordnetes Werk:	In: Water - MDPI AG, 2010, 13(2021), 9, p 1248
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:9, p 1248

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/w13091248

Katalog-ID:	DOAJ051708558

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allfieldsSound	10.3390/w13091248 doi (DE-627)DOAJ051708558 (DE-599)DOAJfa01ac9848594a418f147af6bf72b8ff DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Zahra Zahmatkesh verfasserin aut Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning. probabilistic floodplain mapping uncertainty quantification model calibration climate change Hydraulic engineering Water supply for domestic and industrial purposes Shasha Han verfasserin aut Paulin Coulibaly verfasserin aut In Water MDPI AG, 2010 13(2021), 9, p 1248 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:13 year:2021 number:9, p 1248 https://doi.org/10.3390/w13091248 kostenfrei https://doaj.org/article/fa01ac9848594a418f147af6bf72b8ff kostenfrei https://www.mdpi.com/2073-4441/13/9/1248 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 9, p 1248
language	English
source	In Water 13(2021), 9, p 1248 volume:13 year:2021 number:9, p 1248
sourceStr	In Water 13(2021), 9, p 1248 volume:13 year:2021 number:9, p 1248
format_phy_str_mv	Article
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topic_facet	probabilistic floodplain mapping uncertainty quantification model calibration climate change Hydraulic engineering Water supply for domestic and industrial purposes
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authorswithroles_txt_mv	Zahra Zahmatkesh @@aut@@ Shasha Han @@aut@@ Paulin Coulibaly @@aut@@
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callnumber-first	T - Technology
author	Zahra Zahmatkesh
spellingShingle	Zahra Zahmatkesh misc TC1-978 misc TD201-500 misc probabilistic floodplain mapping misc uncertainty quantification misc model calibration misc climate change misc Hydraulic engineering misc Water supply for domestic and industrial purposes Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change
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topic_title	TC1-978 TD201-500 Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change probabilistic floodplain mapping uncertainty quantification model calibration climate change
topic	misc TC1-978 misc TD201-500 misc probabilistic floodplain mapping misc uncertainty quantification misc model calibration misc climate change misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_unstemmed	misc TC1-978 misc TD201-500 misc probabilistic floodplain mapping misc uncertainty quantification misc model calibration misc climate change misc Hydraulic engineering misc Water supply for domestic and industrial purposes
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title	Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change
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title_full	Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change
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title_sort	understanding uncertainty in probabilistic floodplain mapping in the time of climate change
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title_auth	Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change
abstract	An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.
abstractGer	An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.
abstract_unstemmed	An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.
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title_short	Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change
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Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

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