Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often...
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Autor*in:	Sanjib Sharma [verfasserIn] Benjamin Seiyon Lee [verfasserIn] Iman Hosseini‐Shakib [verfasserIn] Murali Haran [verfasserIn] Klaus Keller [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	hydrologic model calibration sequential Monte Carlo uncertainty characterization flood hazards flood risks

Übergeordnetes Werk:	In: Earth's Future - Wiley, 2014, 11(2023), 1, Seite n/a-n/a
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:1 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1029/2022EF003050

Katalog-ID:	DOAJ081451695

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520			\|a Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
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allfieldsGer	10.1029/2022EF003050 doi (DE-627)DOAJ081451695 (DE-599)DOAJ6d02a77e8462442aba6ab031d24a31e6 DE-627 ger DE-627 rakwb eng GE1-350 QH540-549.5 Sanjib Sharma verfasserin aut Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections. hydrologic model calibration sequential Monte Carlo uncertainty characterization flood hazards flood risks Environmental sciences Ecology Benjamin Seiyon Lee verfasserin aut Iman Hosseini‐Shakib verfasserin aut Murali Haran verfasserin aut Klaus Keller verfasserin aut In Earth's Future Wiley, 2014 11(2023), 1, Seite n/a-n/a (DE-627)774815426 (DE-600)2746403-9 23284277 nnns volume:11 year:2023 number:1 pages:n/a-n/a https://doi.org/10.1029/2022EF003050 kostenfrei https://doaj.org/article/6d02a77e8462442aba6ab031d24a31e6 kostenfrei https://doi.org/10.1029/2022EF003050 kostenfrei https://doaj.org/toc/2328-4277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 n/a-n/a
allfieldsSound	10.1029/2022EF003050 doi (DE-627)DOAJ081451695 (DE-599)DOAJ6d02a77e8462442aba6ab031d24a31e6 DE-627 ger DE-627 rakwb eng GE1-350 QH540-549.5 Sanjib Sharma verfasserin aut Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections. hydrologic model calibration sequential Monte Carlo uncertainty characterization flood hazards flood risks Environmental sciences Ecology Benjamin Seiyon Lee verfasserin aut Iman Hosseini‐Shakib verfasserin aut Murali Haran verfasserin aut Klaus Keller verfasserin aut In Earth's Future Wiley, 2014 11(2023), 1, Seite n/a-n/a (DE-627)774815426 (DE-600)2746403-9 23284277 nnns volume:11 year:2023 number:1 pages:n/a-n/a https://doi.org/10.1029/2022EF003050 kostenfrei https://doaj.org/article/6d02a77e8462442aba6ab031d24a31e6 kostenfrei https://doi.org/10.1029/2022EF003050 kostenfrei https://doaj.org/toc/2328-4277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 n/a-n/a
language	English
source	In Earth's Future 11(2023), 1, Seite n/a-n/a volume:11 year:2023 number:1 pages:n/a-n/a
sourceStr	In Earth's Future 11(2023), 1, Seite n/a-n/a volume:11 year:2023 number:1 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hydrologic model calibration sequential Monte Carlo uncertainty characterization flood hazards flood risks Environmental sciences Ecology
isfreeaccess_bool	true
container_title	Earth's Future
authorswithroles_txt_mv	Sanjib Sharma @@aut@@ Benjamin Seiyon Lee @@aut@@ Iman Hosseini‐Shakib @@aut@@ Murali Haran @@aut@@ Klaus Keller @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	774815426
id	DOAJ081451695
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Sanjib Sharma
spellingShingle	Sanjib Sharma misc GE1-350 misc QH540-549.5 misc hydrologic model calibration misc sequential Monte Carlo misc uncertainty characterization misc flood hazards misc flood risks misc Environmental sciences misc Ecology Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
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topic_title	GE1-350 QH540-549.5 Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks hydrologic model calibration sequential Monte Carlo uncertainty characterization flood hazards flood risks
topic	misc GE1-350 misc QH540-549.5 misc hydrologic model calibration misc sequential Monte Carlo misc uncertainty characterization misc flood hazards misc flood risks misc Environmental sciences misc Ecology
topic_unstemmed	misc GE1-350 misc QH540-549.5 misc hydrologic model calibration misc sequential Monte Carlo misc uncertainty characterization misc flood hazards misc flood risks misc Environmental sciences misc Ecology
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title	Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
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title_sort	neglecting model parametric uncertainty can drastically underestimate flood risks
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title_auth	Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
abstract	Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
abstractGer	Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
abstract_unstemmed	Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
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title_short	Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
url	https://doi.org/10.1029/2022EF003050 https://doaj.org/article/6d02a77e8462442aba6ab031d24a31e6 https://doaj.org/toc/2328-4277
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We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. 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Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

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