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Chaos in a simple model of a delta network
The flux partitioning in delta networks controls how deltas build land and generate stratigraphy. Here, we study flux-partitioning dynamics in a delta network using a simple numerical model consisting of two orders of bifurcations. Previous work on single bifurcations has shown periodic behavior ari...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959556/ https://www.ncbi.nlm.nih.gov/pubmed/33077587 http://dx.doi.org/10.1073/pnas.2010416117 |
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author | Salter, Gerard Voller, Vaughan R. Paola, Chris |
author_facet | Salter, Gerard Voller, Vaughan R. Paola, Chris |
author_sort | Salter, Gerard |
collection | PubMed |
description | The flux partitioning in delta networks controls how deltas build land and generate stratigraphy. Here, we study flux-partitioning dynamics in a delta network using a simple numerical model consisting of two orders of bifurcations. Previous work on single bifurcations has shown periodic behavior arising due to the interplay between channel deepening and downstream deposition. We find that coupling between upstream and downstream bifurcations can lead to chaos; despite its simplicity, our model generates surprisingly complex aperiodic yet bounded dynamics. Our model exhibits sensitive dependence on initial conditions, the hallmark signature of chaos, implying long-term unpredictability of delta networks. However, estimates of the predictability horizon suggest substantial room for improvement in delta-network modeling before fundamental limits on predictability are encountered. We also observe periodic windows, implying that a change in forcing (e.g., due to climate change) could cause a delta to switch from predictable to unpredictable or vice versa. We test our model by using it to generate stratigraphy; converting the temporal Lyapunov exponent to vertical distance using the mean sedimentation rate, we observe qualitatively realistic patterns such as upwards fining and scale-dependent compensation statistics, consistent with ancient and experimental systems. We suggest that chaotic behavior may be common in geomorphic systems and that it implies fundamental bounds on their predictability. We conclude that while delta “weather” (precise configuration) is unpredictable in the long-term, delta “climate” (statistical behavior) is predictable. |
format | Online Article Text |
id | pubmed-7959556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-79595562021-03-23 Chaos in a simple model of a delta network Salter, Gerard Voller, Vaughan R. Paola, Chris Proc Natl Acad Sci U S A Physical Sciences The flux partitioning in delta networks controls how deltas build land and generate stratigraphy. Here, we study flux-partitioning dynamics in a delta network using a simple numerical model consisting of two orders of bifurcations. Previous work on single bifurcations has shown periodic behavior arising due to the interplay between channel deepening and downstream deposition. We find that coupling between upstream and downstream bifurcations can lead to chaos; despite its simplicity, our model generates surprisingly complex aperiodic yet bounded dynamics. Our model exhibits sensitive dependence on initial conditions, the hallmark signature of chaos, implying long-term unpredictability of delta networks. However, estimates of the predictability horizon suggest substantial room for improvement in delta-network modeling before fundamental limits on predictability are encountered. We also observe periodic windows, implying that a change in forcing (e.g., due to climate change) could cause a delta to switch from predictable to unpredictable or vice versa. We test our model by using it to generate stratigraphy; converting the temporal Lyapunov exponent to vertical distance using the mean sedimentation rate, we observe qualitatively realistic patterns such as upwards fining and scale-dependent compensation statistics, consistent with ancient and experimental systems. We suggest that chaotic behavior may be common in geomorphic systems and that it implies fundamental bounds on their predictability. We conclude that while delta “weather” (precise configuration) is unpredictable in the long-term, delta “climate” (statistical behavior) is predictable. National Academy of Sciences 2020-11-03 2020-10-19 /pmc/articles/PMC7959556/ /pubmed/33077587 http://dx.doi.org/10.1073/pnas.2010416117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Salter, Gerard Voller, Vaughan R. Paola, Chris Chaos in a simple model of a delta network |
title | Chaos in a simple model of a delta network |
title_full | Chaos in a simple model of a delta network |
title_fullStr | Chaos in a simple model of a delta network |
title_full_unstemmed | Chaos in a simple model of a delta network |
title_short | Chaos in a simple model of a delta network |
title_sort | chaos in a simple model of a delta network |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959556/ https://www.ncbi.nlm.nih.gov/pubmed/33077587 http://dx.doi.org/10.1073/pnas.2010416117 |
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