Shoreline variability from days to decades: Results of long-term video imaging
The present work characterizes the time‐space scales of variability and forcing dependencies of a unique 26 year record of daily to hourly shoreline data from a steep beach at Duck, North Carolina. Shoreline positions over a 1500 m alongshore span were estimated using a new algorithm called ASLIM ba...
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Veröffentlicht in: | Journal of geophysical research. Oceans 2015-03, Vol.120 (3), p.2159-2178 |
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description | The present work characterizes the time‐space scales of variability and forcing dependencies of a unique 26 year record of daily to hourly shoreline data from a steep beach at Duck, North Carolina. Shoreline positions over a 1500 m alongshore span were estimated using a new algorithm called ASLIM based on fitting the band of high light intensity in time exposure images to a local Gaussian fit, with a subsequent Kalman filter to reduce noise and uncertainty. Our findings revealed that the shoreline change at long times scales dominates seasonal variability, despite that wave forcing had only 2% variance at interannual frequencies. The shoreline response presented 66% of the variance at interannual scales. These results were not expected since from wave forcing it would have been expected that the shoreline response should similarly lack interannual variability, but we found it to be dominated by this scale. The alongshore‐mean shoreline time series revealed no significant annual cycle. However, there are annual oscillations in the shoreline response that are coherent with wave forcing and deserves further explanations. The pier was found to have a significant influence on shoreline behavior since restricts the seasonal longshore transport between the sides, resulting in a seasonally reversing sediment accumulation. Thus, there is a significant annual peak in shoreline variability that is coherent with the annual forcing but becomes insignificant in the longshore‐average.
Key Points:
Most of the shoreline variability is explained by interannual variations
Wave forcing is dominated by shorter periods
Alongshore‐averaged shoreline response at annual time scales is not significant |
doi_str_mv | 10.1002/2014JC010329 |
format | Article |
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Key Points:
Most of the shoreline variability is explained by interannual variations
Wave forcing is dominated by shorter periods
Alongshore‐averaged shoreline response at annual time scales is not significant</description><identifier>ISSN: 2169-9275</identifier><identifier>EISSN: 2169-9291</identifier><identifier>DOI: 10.1002/2014JC010329</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Annual variations ; Aquatic birds ; Coherence ; Ducks ; Fittings ; Geophysics ; Imaging techniques ; Interannual variability ; Interannual variations ; Kalman filters ; Light intensity ; Luminous intensity ; Mathematical models ; Meteorology ; Noise reduction ; Oceanography ; Oscillations ; Seasonal variability ; Seasonal variation ; Seasonal variations ; shoreline variability ; Shorelines ; Time ; Variability ; Variance ; video remote sensing ; Waterfowl ; wave forcing</subject><ispartof>Journal of geophysical research. Oceans, 2015-03, Vol.120 (3), p.2159-2178</ispartof><rights>2015. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a5057-f7c4931e3d5bbf267877bd54995bb21cbde886fa0733df565410104a8e04f2463</citedby><cites>FETCH-LOGICAL-a5057-f7c4931e3d5bbf267877bd54995bb21cbde886fa0733df565410104a8e04f2463</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2014JC010329$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2014JC010329$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids></links><search><creatorcontrib>Pianca, C.</creatorcontrib><creatorcontrib>Holman, R.</creatorcontrib><creatorcontrib>Siegle, E.</creatorcontrib><title>Shoreline variability from days to decades: Results of long-term video imaging</title><title>Journal of geophysical research. Oceans</title><addtitle>J. Geophys. Res. Oceans</addtitle><description>The present work characterizes the time‐space scales of variability and forcing dependencies of a unique 26 year record of daily to hourly shoreline data from a steep beach at Duck, North Carolina. Shoreline positions over a 1500 m alongshore span were estimated using a new algorithm called ASLIM based on fitting the band of high light intensity in time exposure images to a local Gaussian fit, with a subsequent Kalman filter to reduce noise and uncertainty. Our findings revealed that the shoreline change at long times scales dominates seasonal variability, despite that wave forcing had only 2% variance at interannual frequencies. The shoreline response presented 66% of the variance at interannual scales. These results were not expected since from wave forcing it would have been expected that the shoreline response should similarly lack interannual variability, but we found it to be dominated by this scale. The alongshore‐mean shoreline time series revealed no significant annual cycle. However, there are annual oscillations in the shoreline response that are coherent with wave forcing and deserves further explanations. The pier was found to have a significant influence on shoreline behavior since restricts the seasonal longshore transport between the sides, resulting in a seasonally reversing sediment accumulation. Thus, there is a significant annual peak in shoreline variability that is coherent with the annual forcing but becomes insignificant in the longshore‐average.
Key Points:
Most of the shoreline variability is explained by interannual variations
Wave forcing is dominated by shorter periods
Alongshore‐averaged shoreline response at annual time scales is not significant</description><subject>Algorithms</subject><subject>Annual variations</subject><subject>Aquatic birds</subject><subject>Coherence</subject><subject>Ducks</subject><subject>Fittings</subject><subject>Geophysics</subject><subject>Imaging techniques</subject><subject>Interannual variability</subject><subject>Interannual variations</subject><subject>Kalman filters</subject><subject>Light intensity</subject><subject>Luminous intensity</subject><subject>Mathematical models</subject><subject>Meteorology</subject><subject>Noise reduction</subject><subject>Oceanography</subject><subject>Oscillations</subject><subject>Seasonal variability</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>shoreline variability</subject><subject>Shorelines</subject><subject>Time</subject><subject>Variability</subject><subject>Variance</subject><subject>video remote sensing</subject><subject>Waterfowl</subject><subject>wave forcing</subject><issn>2169-9275</issn><issn>2169-9291</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkU1PGzEQhi1UJFDg1h9giQuHLvW31721EaQgCmpoy9Hy7o5T080a7A2Qf1-3QajigDoaaWak5x3NB0JvKTmihLD3jFBxNiWUcGa20C6jylSGGfrmOddyB-3nfEOK1bQWwuyii6ufMUEfBsD3LgXXhD6Ma-xTXOLOrTMeI-6gdR3kD3gOedWPGUeP-zgsqhHSEt-HDiIOS7cIw2IPbXvXZ9h_ihP0_eT42_RzdX45O51-PK-cJFJXXrfCcAq8k03jmdK11k0nhTGlZrRtOqhr5R3RnHdeKiloWUy4GojwTCg-QYebvrcp3q0gj3YZcgt97waIq2ypqqU2TBLzH6hWXCrCeUEPXqA3cZWGsoilhtWcGqnJq5QqA5O_PkHvNlSbYs4JvL1N5UhpbSmxfx5m_31YwfkGfwg9rF9l7dlsPmWUSl1U1UYV8giPzyqXftkyiZb2-mJmv1zrk0_zr1f2B_8NsLGiaw</recordid><startdate>201503</startdate><enddate>201503</enddate><creator>Pianca, C.</creator><creator>Holman, R.</creator><creator>Siegle, E.</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>7TV</scope><scope>C1K</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>201503</creationdate><title>Shoreline variability from days to decades: Results of long-term video imaging</title><author>Pianca, C. ; Holman, R. ; Siegle, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a5057-f7c4931e3d5bbf267877bd54995bb21cbde886fa0733df565410104a8e04f2463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Annual variations</topic><topic>Aquatic birds</topic><topic>Coherence</topic><topic>Ducks</topic><topic>Fittings</topic><topic>Geophysics</topic><topic>Imaging techniques</topic><topic>Interannual variability</topic><topic>Interannual variations</topic><topic>Kalman filters</topic><topic>Light intensity</topic><topic>Luminous intensity</topic><topic>Mathematical models</topic><topic>Meteorology</topic><topic>Noise reduction</topic><topic>Oceanography</topic><topic>Oscillations</topic><topic>Seasonal variability</topic><topic>Seasonal variation</topic><topic>Seasonal variations</topic><topic>shoreline variability</topic><topic>Shorelines</topic><topic>Time</topic><topic>Variability</topic><topic>Variance</topic><topic>video remote sensing</topic><topic>Waterfowl</topic><topic>wave forcing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pianca, C.</creatorcontrib><creatorcontrib>Holman, R.</creatorcontrib><creatorcontrib>Siegle, E.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Pollution Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of geophysical research. Oceans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pianca, C.</au><au>Holman, R.</au><au>Siegle, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shoreline variability from days to decades: Results of long-term video imaging</atitle><jtitle>Journal of geophysical research. Oceans</jtitle><addtitle>J. Geophys. Res. Oceans</addtitle><date>2015-03</date><risdate>2015</risdate><volume>120</volume><issue>3</issue><spage>2159</spage><epage>2178</epage><pages>2159-2178</pages><issn>2169-9275</issn><eissn>2169-9291</eissn><abstract>The present work characterizes the time‐space scales of variability and forcing dependencies of a unique 26 year record of daily to hourly shoreline data from a steep beach at Duck, North Carolina. Shoreline positions over a 1500 m alongshore span were estimated using a new algorithm called ASLIM based on fitting the band of high light intensity in time exposure images to a local Gaussian fit, with a subsequent Kalman filter to reduce noise and uncertainty. Our findings revealed that the shoreline change at long times scales dominates seasonal variability, despite that wave forcing had only 2% variance at interannual frequencies. The shoreline response presented 66% of the variance at interannual scales. These results were not expected since from wave forcing it would have been expected that the shoreline response should similarly lack interannual variability, but we found it to be dominated by this scale. The alongshore‐mean shoreline time series revealed no significant annual cycle. However, there are annual oscillations in the shoreline response that are coherent with wave forcing and deserves further explanations. The pier was found to have a significant influence on shoreline behavior since restricts the seasonal longshore transport between the sides, resulting in a seasonally reversing sediment accumulation. Thus, there is a significant annual peak in shoreline variability that is coherent with the annual forcing but becomes insignificant in the longshore‐average.
Key Points:
Most of the shoreline variability is explained by interannual variations
Wave forcing is dominated by shorter periods
Alongshore‐averaged shoreline response at annual time scales is not significant</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2014JC010329</doi><tpages>20</tpages></addata></record> |
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subjects | Algorithms Annual variations Aquatic birds Coherence Ducks Fittings Geophysics Imaging techniques Interannual variability Interannual variations Kalman filters Light intensity Luminous intensity Mathematical models Meteorology Noise reduction Oceanography Oscillations Seasonal variability Seasonal variation Seasonal variations shoreline variability Shorelines Time Variability Variance video remote sensing Waterfowl wave forcing |
title | Shoreline variability from days to decades: Results of long-term video imaging |
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