Shoreline-Position Forecasting: Impact of Storms, Rate-Calculation Methodologies, and Temporal Scales
Despite the considerable research that has sought to describe past and predict future shoreline change, little consensus has emerged on the best methodology for forecasting future shoreline positions. While a certain degree of heterogeneity in approach is warranted given the variability in coastal g...
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Veröffentlicht in: | Journal of coastal research 2001, Vol.17 (3), p.721-730 |
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description | Despite the considerable research that has sought to describe past and predict future shoreline change, little consensus has emerged on the best methodology for forecasting future shoreline positions. While a certain degree of heterogeneity in approach is warranted given the variability in coastal geomorphology and sediment-transport processes, the prediction error associated with each method has not been evaluated in great detail. In this study, measured shoreline positions from Delaware and New York were used to calculate long-term erosion rates and make predictions to subsequent, known positions. Rates were calculated using end-point and linear-regression methods, including and excluding storm-specific shorelines. Those rate computations that included storm-specific shorelines yielded consistently poor predictions (average factor-of-three increase in error) compared with non-storm erosion rates, regardless of rate-calculation method. Linear-regression predictions, on average, performed better than end-point rate predictions, reducing error by over 70% in New York and 34% in Delaware for rates including storm shorelines, and between 4 and 31% for non-storm data (DE and NY, respectively). Predictions (hindcasts) were also made to 19thcentury shoreline positions using rates computed with modern, non-storm data. The positions predicted along relatively undeveloped stretches of the coast were within the 95% confidence interval associated with the prediction. Hindcasts made in areas characterized by heavy development and/or beach nourishment projects were poor, as would be expected given the recent alteration of the natural sediment-supply system. For all locations, inclusion of 19thcentury data reduced uncertainty in forecasts of 21stcentury shoreline positions by roughly 44%. These results show that forecasts derived from linear-regression rates using non-storm, 19thand 20thcentury data produce the lowest prediction error and uncertainty in the long-term trend. |
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Honeycutt ; Mark Crowell ; Douglas, Bruce C.</creator><creatorcontrib>Maria G. Honeycutt ; Mark Crowell ; Douglas, Bruce C.</creatorcontrib><description>Despite the considerable research that has sought to describe past and predict future shoreline change, little consensus has emerged on the best methodology for forecasting future shoreline positions. While a certain degree of heterogeneity in approach is warranted given the variability in coastal geomorphology and sediment-transport processes, the prediction error associated with each method has not been evaluated in great detail. In this study, measured shoreline positions from Delaware and New York were used to calculate long-term erosion rates and make predictions to subsequent, known positions. Rates were calculated using end-point and linear-regression methods, including and excluding storm-specific shorelines. Those rate computations that included storm-specific shorelines yielded consistently poor predictions (average factor-of-three increase in error) compared with non-storm erosion rates, regardless of rate-calculation method. Linear-regression predictions, on average, performed better than end-point rate predictions, reducing error by over 70% in New York and 34% in Delaware for rates including storm shorelines, and between 4 and 31% for non-storm data (DE and NY, respectively). Predictions (hindcasts) were also made to 19thcentury shoreline positions using rates computed with modern, non-storm data. The positions predicted along relatively undeveloped stretches of the coast were within the 95% confidence interval associated with the prediction. Hindcasts made in areas characterized by heavy development and/or beach nourishment projects were poor, as would be expected given the recent alteration of the natural sediment-supply system. For all locations, inclusion of 19thcentury data reduced uncertainty in forecasts of 21stcentury shoreline positions by roughly 44%. These results show that forecasts derived from linear-regression rates using non-storm, 19thand 20thcentury data produce the lowest prediction error and uncertainty in the long-term trend.</description><identifier>ISSN: 0749-0208</identifier><identifier>EISSN: 1551-5036</identifier><identifier>CODEN: JCRSEK</identifier><language>eng</language><publisher>Lawrence, KS: Coastal Education and Research Foundation (CERF)</publisher><subject>Arithmetic mean ; Beaches ; Coasts ; Confidence interval ; Datasets ; Earth sciences ; Earth, ocean, space ; Erosion ; Error rates ; Exact sciences and technology ; Linear regression ; Marine ; Marine and continental quaternary ; Shorelines ; Statistical forecasts ; Surficial geology ; USA, Delaware ; USA, New York</subject><ispartof>Journal of coastal research, 2001, Vol.17 (3), p.721-730</ispartof><rights>Copyright 2001 Coastal Education & Research Foundation [CERF]</rights><rights>2001 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/4300223$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/4300223$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,4024,58017,58250</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1122058$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Maria G. Honeycutt</creatorcontrib><creatorcontrib>Mark Crowell</creatorcontrib><creatorcontrib>Douglas, Bruce C.</creatorcontrib><title>Shoreline-Position Forecasting: Impact of Storms, Rate-Calculation Methodologies, and Temporal Scales</title><title>Journal of coastal research</title><description>Despite the considerable research that has sought to describe past and predict future shoreline change, little consensus has emerged on the best methodology for forecasting future shoreline positions. While a certain degree of heterogeneity in approach is warranted given the variability in coastal geomorphology and sediment-transport processes, the prediction error associated with each method has not been evaluated in great detail. In this study, measured shoreline positions from Delaware and New York were used to calculate long-term erosion rates and make predictions to subsequent, known positions. Rates were calculated using end-point and linear-regression methods, including and excluding storm-specific shorelines. Those rate computations that included storm-specific shorelines yielded consistently poor predictions (average factor-of-three increase in error) compared with non-storm erosion rates, regardless of rate-calculation method. Linear-regression predictions, on average, performed better than end-point rate predictions, reducing error by over 70% in New York and 34% in Delaware for rates including storm shorelines, and between 4 and 31% for non-storm data (DE and NY, respectively). Predictions (hindcasts) were also made to 19thcentury shoreline positions using rates computed with modern, non-storm data. The positions predicted along relatively undeveloped stretches of the coast were within the 95% confidence interval associated with the prediction. Hindcasts made in areas characterized by heavy development and/or beach nourishment projects were poor, as would be expected given the recent alteration of the natural sediment-supply system. For all locations, inclusion of 19thcentury data reduced uncertainty in forecasts of 21stcentury shoreline positions by roughly 44%. These results show that forecasts derived from linear-regression rates using non-storm, 19thand 20thcentury data produce the lowest prediction error and uncertainty in the long-term trend.</description><subject>Arithmetic mean</subject><subject>Beaches</subject><subject>Coasts</subject><subject>Confidence interval</subject><subject>Datasets</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Erosion</subject><subject>Error rates</subject><subject>Exact sciences and technology</subject><subject>Linear regression</subject><subject>Marine</subject><subject>Marine and continental quaternary</subject><subject>Shorelines</subject><subject>Statistical forecasts</subject><subject>Surficial geology</subject><subject>USA, Delaware</subject><subject>USA, New York</subject><issn>0749-0208</issn><issn>1551-5036</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNo90FFLwzAQB_AiCs7pN_AhD-KTgSRN09Q3GW4OJoqbz-WaXreMtKlN9uC3t7rh08H9fxx3d5ZMeJZxmrFUnScTlsuCMsH0ZXIVwp4xrrTMJwmud35AZzuk7z7YaH1H5mPHQIi22z6SZduDicQ3ZB390IYH8gER6QycOTj4868Yd772zm8tjjl0Ndlg2_sBHFkbcBiuk4sGXMCbU50mn_PnzeyFrt4Wy9nTiu5FKiMFDQVklagZNxxyXjWVahpVF5xhoxFrwQpZg6qE1siUElqAUJnWsuA8LyCdJvfHuf3gvw4YYtnaYNA56NAfQsm1-H2EHOHdCUIYN2wG6IwNZT_YFobvknMhWKZHdntk-zAe_x_LlDEh0vQHWwdrBQ</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Maria G. Honeycutt</creator><creator>Mark Crowell</creator><creator>Douglas, Bruce C.</creator><general>Coastal Education and Research Foundation (CERF)</general><general>Coastal Education and Research Foundation</general><scope>IQODW</scope><scope>7TN</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>2001</creationdate><title>Shoreline-Position Forecasting: Impact of Storms, Rate-Calculation Methodologies, and Temporal Scales</title><author>Maria G. Honeycutt ; Mark Crowell ; Douglas, Bruce C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j234t-a8a9a5b2d01c1a71bfb6ff6d910ef8eed2094da6b288e066282a26588491179a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Arithmetic mean</topic><topic>Beaches</topic><topic>Coasts</topic><topic>Confidence interval</topic><topic>Datasets</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Erosion</topic><topic>Error rates</topic><topic>Exact sciences and technology</topic><topic>Linear regression</topic><topic>Marine</topic><topic>Marine and continental quaternary</topic><topic>Shorelines</topic><topic>Statistical forecasts</topic><topic>Surficial geology</topic><topic>USA, Delaware</topic><topic>USA, New York</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Maria G. Honeycutt</creatorcontrib><creatorcontrib>Mark Crowell</creatorcontrib><creatorcontrib>Douglas, Bruce C.</creatorcontrib><collection>Pascal-Francis</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Journal of coastal research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maria G. Honeycutt</au><au>Mark Crowell</au><au>Douglas, Bruce C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shoreline-Position Forecasting: Impact of Storms, Rate-Calculation Methodologies, and Temporal Scales</atitle><jtitle>Journal of coastal research</jtitle><date>2001</date><risdate>2001</risdate><volume>17</volume><issue>3</issue><spage>721</spage><epage>730</epage><pages>721-730</pages><issn>0749-0208</issn><eissn>1551-5036</eissn><coden>JCRSEK</coden><abstract>Despite the considerable research that has sought to describe past and predict future shoreline change, little consensus has emerged on the best methodology for forecasting future shoreline positions. While a certain degree of heterogeneity in approach is warranted given the variability in coastal geomorphology and sediment-transport processes, the prediction error associated with each method has not been evaluated in great detail. In this study, measured shoreline positions from Delaware and New York were used to calculate long-term erosion rates and make predictions to subsequent, known positions. Rates were calculated using end-point and linear-regression methods, including and excluding storm-specific shorelines. Those rate computations that included storm-specific shorelines yielded consistently poor predictions (average factor-of-three increase in error) compared with non-storm erosion rates, regardless of rate-calculation method. Linear-regression predictions, on average, performed better than end-point rate predictions, reducing error by over 70% in New York and 34% in Delaware for rates including storm shorelines, and between 4 and 31% for non-storm data (DE and NY, respectively). Predictions (hindcasts) were also made to 19thcentury shoreline positions using rates computed with modern, non-storm data. The positions predicted along relatively undeveloped stretches of the coast were within the 95% confidence interval associated with the prediction. Hindcasts made in areas characterized by heavy development and/or beach nourishment projects were poor, as would be expected given the recent alteration of the natural sediment-supply system. For all locations, inclusion of 19thcentury data reduced uncertainty in forecasts of 21stcentury shoreline positions by roughly 44%. These results show that forecasts derived from linear-regression rates using non-storm, 19thand 20thcentury data produce the lowest prediction error and uncertainty in the long-term trend.</abstract><cop>Lawrence, KS</cop><pub>Coastal Education and Research Foundation (CERF)</pub><tpages>10</tpages></addata></record> |
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source | JSTOR Archive Collection A-Z Listing; EZB-FREE-00999 freely available EZB journals |
subjects | Arithmetic mean Beaches Coasts Confidence interval Datasets Earth sciences Earth, ocean, space Erosion Error rates Exact sciences and technology Linear regression Marine Marine and continental quaternary Shorelines Statistical forecasts Surficial geology USA, Delaware USA, New York |
title | Shoreline-Position Forecasting: Impact of Storms, Rate-Calculation Methodologies, and Temporal Scales |
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