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Models and scales for cross‐shore sandbar migration
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| Zeitschriftentitel: | Journal of Geophysical Research: Earth Surface |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Journal of Geophysical Research: Earth Surface, 115, 2010, F3 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Geophysical Union (AGU)
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| Schlagwörter: |
| author_facet |
Pape, L. Kuriyama, Y. Ruessink, B. G. Pape, L. Kuriyama, Y. Ruessink, B. G. |
|---|---|
| author |
Pape, L. Kuriyama, Y. Ruessink, B. G. |
| spellingShingle |
Pape, L. Kuriyama, Y. Ruessink, B. G. Journal of Geophysical Research: Earth Surface Models and scales for cross‐shore sandbar migration Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics |
| author_sort |
pape, l. |
| spelling |
Pape, L. Kuriyama, Y. Ruessink, B. G. 0148-0227 American Geophysical Union (AGU) Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics http://dx.doi.org/10.1029/2009jf001644 <jats:p>We investigate the long‐term (months to years) predictability of cross‐shore sandbar migration with two models that operate on different abstraction levels: (1) a coupled, cross‐shore waves‐currents‐bathymetric evolution model and (2) two data‐driven neural network models, based on simplified cross‐shore profile representations and daily averaged wave properties. For model calibration, training, and validation, we use a high‐resolution 15 yearlong profile data set collected at the Hasaki Oceanographic Research Station in Japan. Sandbar behavior at this field site is characterized by cycles of net‐offshore migration with a duration of 1–4 years. We find that all models can produce several general features of sandbar behavior at the studied field site, such as rapid offshore migration, slower onshore return, and net‐offshore migration. However, it is difficult to quantitatively predict the offshore‐directed trends in sandbar location over time scales of months to years. While simple linear models outperform more detailed nonlinear models, for all models it is difficult to predict long‐term sandbar behavior, because of error accumulation in the model's processes over time. Representing processes on a more abstract level (scale aggregation) alleviates error accumulation but does not completely overcome this problem.</jats:p> Models and scales for cross‐shore sandbar migration Journal of Geophysical Research: Earth Surface |
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Chemie und Pharmazie Land- und Forstwirtschaft, Gartenbau, Fischereiwirtschaft, Hauswirtschaft Biologie Allgemeine Naturwissenschaft Physik Technik Geologie und Paläontologie Geographie |
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Models and scales for cross‐shore sandbar migration |
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Models and scales for cross‐shore sandbar migration |
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Models and scales for cross‐shore sandbar migration |
| title_fullStr |
Models and scales for cross‐shore sandbar migration |
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Models and scales for cross‐shore sandbar migration |
| title_short |
Models and scales for cross‐shore sandbar migration |
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models and scales for cross‐shore sandbar migration |
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Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics |
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http://dx.doi.org/10.1029/2009jf001644 |
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2010 |
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<jats:p>We investigate the long‐term (months to years) predictability of cross‐shore sandbar migration with two models that operate on different abstraction levels: (1) a coupled, cross‐shore waves‐currents‐bathymetric evolution model and (2) two data‐driven neural network models, based on simplified cross‐shore profile representations and daily averaged wave properties. For model calibration, training, and validation, we use a high‐resolution 15 yearlong profile data set collected at the Hasaki Oceanographic Research Station in Japan. Sandbar behavior at this field site is characterized by cycles of net‐offshore migration with a duration of 1–4 years. We find that all models can produce several general features of sandbar behavior at the studied field site, such as rapid offshore migration, slower onshore return, and net‐offshore migration. However, it is difficult to quantitatively predict the offshore‐directed trends in sandbar location over time scales of months to years. While simple linear models outperform more detailed nonlinear models, for all models it is difficult to predict long‐term sandbar behavior, because of error accumulation in the model's processes over time. Representing processes on a more abstract level (scale aggregation) alleviates error accumulation but does not completely overcome this problem.</jats:p> |
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| author | Pape, L., Kuriyama, Y., Ruessink, B. G. |
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| description | <jats:p>We investigate the long‐term (months to years) predictability of cross‐shore sandbar migration with two models that operate on different abstraction levels: (1) a coupled, cross‐shore waves‐currents‐bathymetric evolution model and (2) two data‐driven neural network models, based on simplified cross‐shore profile representations and daily averaged wave properties. For model calibration, training, and validation, we use a high‐resolution 15 yearlong profile data set collected at the Hasaki Oceanographic Research Station in Japan. Sandbar behavior at this field site is characterized by cycles of net‐offshore migration with a duration of 1–4 years. We find that all models can produce several general features of sandbar behavior at the studied field site, such as rapid offshore migration, slower onshore return, and net‐offshore migration. However, it is difficult to quantitatively predict the offshore‐directed trends in sandbar location over time scales of months to years. While simple linear models outperform more detailed nonlinear models, for all models it is difficult to predict long‐term sandbar behavior, because of error accumulation in the model's processes over time. Representing processes on a more abstract level (scale aggregation) alleviates error accumulation but does not completely overcome this problem.</jats:p> |
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| imprint | American Geophysical Union (AGU), 2010 |
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| spelling | Pape, L. Kuriyama, Y. Ruessink, B. G. 0148-0227 American Geophysical Union (AGU) Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics http://dx.doi.org/10.1029/2009jf001644 <jats:p>We investigate the long‐term (months to years) predictability of cross‐shore sandbar migration with two models that operate on different abstraction levels: (1) a coupled, cross‐shore waves‐currents‐bathymetric evolution model and (2) two data‐driven neural network models, based on simplified cross‐shore profile representations and daily averaged wave properties. For model calibration, training, and validation, we use a high‐resolution 15 yearlong profile data set collected at the Hasaki Oceanographic Research Station in Japan. Sandbar behavior at this field site is characterized by cycles of net‐offshore migration with a duration of 1–4 years. We find that all models can produce several general features of sandbar behavior at the studied field site, such as rapid offshore migration, slower onshore return, and net‐offshore migration. However, it is difficult to quantitatively predict the offshore‐directed trends in sandbar location over time scales of months to years. While simple linear models outperform more detailed nonlinear models, for all models it is difficult to predict long‐term sandbar behavior, because of error accumulation in the model's processes over time. Representing processes on a more abstract level (scale aggregation) alleviates error accumulation but does not completely overcome this problem.</jats:p> Models and scales for cross‐shore sandbar migration Journal of Geophysical Research: Earth Surface |
| spellingShingle | Pape, L., Kuriyama, Y., Ruessink, B. G., Journal of Geophysical Research: Earth Surface, Models and scales for cross‐shore sandbar migration, Paleontology, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), Atmospheric Science, Earth-Surface Processes, Geochemistry and Petrology, Soil Science, Water Science and Technology, Ecology, Aquatic Science, Forestry, Oceanography, Geophysics |
| title | Models and scales for cross‐shore sandbar migration |
| title_full | Models and scales for cross‐shore sandbar migration |
| title_fullStr | Models and scales for cross‐shore sandbar migration |
| title_full_unstemmed | Models and scales for cross‐shore sandbar migration |
| title_short | Models and scales for cross‐shore sandbar migration |
| title_sort | models and scales for cross‐shore sandbar migration |
| title_unstemmed | Models and scales for cross‐shore sandbar migration |
| topic | Paleontology, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), Atmospheric Science, Earth-Surface Processes, Geochemistry and Petrology, Soil Science, Water Science and Technology, Ecology, Aquatic Science, Forestry, Oceanography, Geophysics |
| url | http://dx.doi.org/10.1029/2009jf001644 |