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A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin
Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology...
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Published in: | Water resources research 2018-11, Vol.54 (11), p.9321-9341 |
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creator | Schlef, Katherine E. François, Baptiste Robertson, Andrew W. Brown, Casey |
description | Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology consisting of four steps: (1) selection of predictand representing extreme events, (2) identification of credible large‐scale predictors that mechanistically control the occurrence and magnitude of the predictand, (3) development of a statistical model relating the predictors to the predictand, and (4) projection of the predictand by forcing the model with predictor projections. These four steps, developed from a review of the current literature, are demonstrated for multiple gages in the northwest Ohio River Basin in the United States Midwest as a case study. Floods are defined as annual maximum series events in January through April and are linked to geopotential height and soil moisture predictors in a Bayesian linear regression model. The projections generally show a slight decrease in future flood magnitude and demonstrate the transparency of the climate‐informed approach. An initial step for more general application across the United States and remaining challenges associated with climate‐informed flood projection are discussed.
Key Points
A formal four step methodology is proposed for climate‐informed approaches to long‐term flood projection
The methodology is applied to the Ohio River Basin as a case study, and projected change is attributed to specific predictands
An initial step to general applicability across the United States and remaining challenges are discussed |
doi_str_mv | 10.1029/2018WR023209 |
format | article |
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Key Points
A formal four step methodology is proposed for climate‐informed approaches to long‐term flood projection
The methodology is applied to the Ohio River Basin as a case study, and projected change is attributed to specific predictands
An initial step to general applicability across the United States and remaining challenges are discussed</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2018WR023209</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Bayesian analysis ; Case studies ; Climate ; climate change ; Climate models ; climate‐informed ; Dynamic height ; flood ; Flood control ; Flood magnitude ; Flood management ; Floods ; Geopotential ; Geopotential height ; Hydrology ; Literature reviews ; Mathematical models ; Methods ; Modelling ; nonstationary ; Ohio River Basin ; Probability theory ; Projection ; Regression models ; River basins ; Rivers ; Soil ; Soil moisture ; Statistical analysis ; Statistical models ; Transparency (optical)</subject><ispartof>Water resources research, 2018-11, Vol.54 (11), p.9321-9341</ispartof><rights>2018. American Geophysical Union. All Rights Reserved.</rights><rights>2018. American Geophysical Union. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a2833-6f2c3b9591ed4120ad12edb7bca48a7b4ccc3d033372932954564e1a06bee3823</citedby><cites>FETCH-LOGICAL-a2833-6f2c3b9591ed4120ad12edb7bca48a7b4ccc3d033372932954564e1a06bee3823</cites><orcidid>0000-0002-0625-4357 ; 0000-0001-7585-5589 ; 0000-0002-8248-041X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2018WR023209$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2018WR023209$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,11514,27924,27925,46468,46892</link.rule.ids></links><search><creatorcontrib>Schlef, Katherine E.</creatorcontrib><creatorcontrib>François, Baptiste</creatorcontrib><creatorcontrib>Robertson, Andrew W.</creatorcontrib><creatorcontrib>Brown, Casey</creatorcontrib><title>A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin</title><title>Water resources research</title><description>Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology consisting of four steps: (1) selection of predictand representing extreme events, (2) identification of credible large‐scale predictors that mechanistically control the occurrence and magnitude of the predictand, (3) development of a statistical model relating the predictors to the predictand, and (4) projection of the predictand by forcing the model with predictor projections. These four steps, developed from a review of the current literature, are demonstrated for multiple gages in the northwest Ohio River Basin in the United States Midwest as a case study. Floods are defined as annual maximum series events in January through April and are linked to geopotential height and soil moisture predictors in a Bayesian linear regression model. The projections generally show a slight decrease in future flood magnitude and demonstrate the transparency of the climate‐informed approach. An initial step for more general application across the United States and remaining challenges associated with climate‐informed flood projection are discussed.
Key Points
A formal four step methodology is proposed for climate‐informed approaches to long‐term flood projection
The methodology is applied to the Ohio River Basin as a case study, and projected change is attributed to specific predictands
An initial step to general applicability across the United States and remaining challenges are discussed</description><subject>Bayesian analysis</subject><subject>Case studies</subject><subject>Climate</subject><subject>climate change</subject><subject>Climate models</subject><subject>climate‐informed</subject><subject>Dynamic height</subject><subject>flood</subject><subject>Flood control</subject><subject>Flood magnitude</subject><subject>Flood management</subject><subject>Floods</subject><subject>Geopotential</subject><subject>Geopotential height</subject><subject>Hydrology</subject><subject>Literature reviews</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Modelling</subject><subject>nonstationary</subject><subject>Ohio River Basin</subject><subject>Probability theory</subject><subject>Projection</subject><subject>Regression models</subject><subject>River basins</subject><subject>Rivers</subject><subject>Soil</subject><subject>Soil moisture</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Transparency (optical)</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kM1Kw0AURgdRsFZ3PsCAW6Pzk2Qyy1psLVSUUOkyTJKbJiXN1Jmp0pV9BBc-YZ_Ekbpw5epyuYfv8h2ELim5oYTJW0ZoMk8J44zII9SjMgwDIQU_Rj1CQh5QLsUpOrN2SQgNo1j00McAj6EDo1r8CK7WpW71YosrbfCwbVbKwX73Oen8voISD9Zro1VRg8VO46nuFv46A7PCo1brEj8bvYTCNbrb774mbbuxzviIEs8bV2NXA36qG43T5g0MvlO26c7RSaVaCxe_s49eRvez4UMwfRpPhoNpoFjCeRBXrOC5jCSFMqSMqJIyKHORFypMlMjDoih4STjngknOZOTbhUAViXMAnjDeR1eHXF_gdQPWZUu9MZ1_mTEaCeH9iMRT1weqMNpaA1W2Nl6C2WaUZD-Ks7-KPc4P-HvTwvZfNpunw5Tx2Jf5BtCYgKI</recordid><startdate>201811</startdate><enddate>201811</enddate><creator>Schlef, Katherine E.</creator><creator>François, Baptiste</creator><creator>Robertson, Andrew W.</creator><creator>Brown, Casey</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-0625-4357</orcidid><orcidid>https://orcid.org/0000-0001-7585-5589</orcidid><orcidid>https://orcid.org/0000-0002-8248-041X</orcidid></search><sort><creationdate>201811</creationdate><title>A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin</title><author>Schlef, Katherine E. ; François, Baptiste ; Robertson, Andrew W. ; Brown, Casey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a2833-6f2c3b9591ed4120ad12edb7bca48a7b4ccc3d033372932954564e1a06bee3823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bayesian analysis</topic><topic>Case studies</topic><topic>Climate</topic><topic>climate change</topic><topic>Climate models</topic><topic>climate‐informed</topic><topic>Dynamic height</topic><topic>flood</topic><topic>Flood control</topic><topic>Flood magnitude</topic><topic>Flood management</topic><topic>Floods</topic><topic>Geopotential</topic><topic>Geopotential height</topic><topic>Hydrology</topic><topic>Literature reviews</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Modelling</topic><topic>nonstationary</topic><topic>Ohio River Basin</topic><topic>Probability theory</topic><topic>Projection</topic><topic>Regression models</topic><topic>River basins</topic><topic>Rivers</topic><topic>Soil</topic><topic>Soil moisture</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Transparency (optical)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schlef, Katherine E.</creatorcontrib><creatorcontrib>François, Baptiste</creatorcontrib><creatorcontrib>Robertson, Andrew W.</creatorcontrib><creatorcontrib>Brown, Casey</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schlef, Katherine E.</au><au>François, Baptiste</au><au>Robertson, Andrew W.</au><au>Brown, Casey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin</atitle><jtitle>Water resources research</jtitle><date>2018-11</date><risdate>2018</risdate><volume>54</volume><issue>11</issue><spage>9321</spage><epage>9341</epage><pages>9321-9341</pages><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology consisting of four steps: (1) selection of predictand representing extreme events, (2) identification of credible large‐scale predictors that mechanistically control the occurrence and magnitude of the predictand, (3) development of a statistical model relating the predictors to the predictand, and (4) projection of the predictand by forcing the model with predictor projections. These four steps, developed from a review of the current literature, are demonstrated for multiple gages in the northwest Ohio River Basin in the United States Midwest as a case study. Floods are defined as annual maximum series events in January through April and are linked to geopotential height and soil moisture predictors in a Bayesian linear regression model. The projections generally show a slight decrease in future flood magnitude and demonstrate the transparency of the climate‐informed approach. An initial step for more general application across the United States and remaining challenges associated with climate‐informed flood projection are discussed.
Key Points
A formal four step methodology is proposed for climate‐informed approaches to long‐term flood projection
The methodology is applied to the Ohio River Basin as a case study, and projected change is attributed to specific predictands
An initial step to general applicability across the United States and remaining challenges are discussed</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2018WR023209</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-0625-4357</orcidid><orcidid>https://orcid.org/0000-0001-7585-5589</orcidid><orcidid>https://orcid.org/0000-0002-8248-041X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bayesian analysis Case studies Climate climate change Climate models climate‐informed Dynamic height flood Flood control Flood magnitude Flood management Floods Geopotential Geopotential height Hydrology Literature reviews Mathematical models Methods Modelling nonstationary Ohio River Basin Probability theory Projection Regression models River basins Rivers Soil Soil moisture Statistical analysis Statistical models Transparency (optical) |
title | A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin |
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