Coastline evolution based on statistical analysis and modeling
<p>Wind, waves, tides, sediment supply, changes in relative sea level and human activities strongly affect shorelines, which constantly move in response to these processes, over a variety of timescales. Thus, the implementation of sound coastal zone management strategies needs reliable informa...
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2019-09-01
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doaj-32191510c4e34203993a2a686983b6692020-11-25T00:50:12ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812019-09-01191937195310.5194/nhess-19-1937-2019Coastline evolution based on statistical analysis and modelingE. ArmenioF. De SerioM. MossaA. F. Petrillo<p>Wind, waves, tides, sediment supply, changes in relative sea level and human activities strongly affect shorelines, which constantly move in response to these processes, over a variety of timescales. Thus, the implementation of sound coastal zone management strategies needs reliable information on erosion and/or deposition processes. To suggest a feasible way to provide this information is the main reason for this work. A chain approach is proposed here, tested on a vulnerable coastal site located along southern Italy, and based on the joint analysis of field data, statistical tools and numerical modeling. Firstly, the coastline morphology has been examined through interannual field data, such as aerial photographs, plane-bathymetric surveys and seabed characterization. After this, rates of shoreline changes have been quantified with a specific GIS tool. The correlations among the historical positions of the shoreline have been detected by statistical analysis and have been satisfactorily confirmed by numerical modeling, in terms of recurrent erosion–accretion area and beach rotation trends. Finally, based on field topographic, sediment, wave and wind data, the response of the beach through numerical simulation has been investigated in a forecasting perspective. The purpose of this study is to provide a feasible, general and replicable chain approach, which could help to thoroughly understand the dynamics of a coastal system, identify typical and recurrent erosion–accretion processes, and predict possible future trends, useful for planning of coastal activities.</p>https://www.nat-hazards-earth-syst-sci.net/19/1937/2019/nhess-19-1937-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
E. Armenio F. De Serio M. Mossa A. F. Petrillo |
spellingShingle |
E. Armenio F. De Serio M. Mossa A. F. Petrillo Coastline evolution based on statistical analysis and modeling Natural Hazards and Earth System Sciences |
author_facet |
E. Armenio F. De Serio M. Mossa A. F. Petrillo |
author_sort |
E. Armenio |
title |
Coastline evolution based on statistical analysis and modeling |
title_short |
Coastline evolution based on statistical analysis and modeling |
title_full |
Coastline evolution based on statistical analysis and modeling |
title_fullStr |
Coastline evolution based on statistical analysis and modeling |
title_full_unstemmed |
Coastline evolution based on statistical analysis and modeling |
title_sort |
coastline evolution based on statistical analysis and modeling |
publisher |
Copernicus Publications |
series |
Natural Hazards and Earth System Sciences |
issn |
1561-8633 1684-9981 |
publishDate |
2019-09-01 |
description |
<p>Wind, waves, tides, sediment supply, changes in relative
sea level and human activities strongly affect shorelines, which constantly
move in response to these processes, over a variety of timescales. Thus,
the implementation of sound coastal zone management strategies needs
reliable information on erosion and/or deposition processes. To suggest a
feasible way to provide this information is the main reason for this work. A
chain approach is proposed here, tested on a vulnerable coastal site located
along southern Italy, and based on the joint analysis of field data,
statistical tools and numerical modeling. Firstly, the coastline morphology
has been examined through interannual field data, such as aerial
photographs, plane-bathymetric surveys and seabed characterization. After this,
rates of shoreline changes have been quantified with a specific GIS tool.
The correlations among the historical positions of the shoreline have been
detected by statistical analysis and have been satisfactorily confirmed by
numerical modeling, in terms of recurrent erosion–accretion area and beach
rotation trends. Finally, based on field topographic, sediment, wave and
wind data, the response of the beach through numerical simulation has been
investigated in a forecasting perspective. The purpose of this study is to
provide a feasible, general and replicable chain approach, which could help
to thoroughly understand the dynamics of a coastal system, identify typical
and recurrent erosion–accretion processes, and predict possible future
trends, useful for planning of coastal activities.</p> |
url |
https://www.nat-hazards-earth-syst-sci.net/19/1937/2019/nhess-19-1937-2019.pdf |
work_keys_str_mv |
AT earmenio coastlineevolutionbasedonstatisticalanalysisandmodeling AT fdeserio coastlineevolutionbasedonstatisticalanalysisandmodeling AT mmossa coastlineevolutionbasedonstatisticalanalysisandmodeling AT afpetrillo coastlineevolutionbasedonstatisticalanalysisandmodeling |
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1725248703772491776 |