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River landscapes and optimal channel networks

We study tree structures termed optimal channel networks (OCNs) that minimize the total gravitational energy loss in the system, an exact property of steady-state landscape configurations that prove dynamically accessible and strikingly similar to natural forms. Here, we show that every OCN is a so-...

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Autores principales: Balister, Paul, Balogh, József, Bertuzzo, Enrico, Bollobás, Béla, Caldarelli, Guido, Maritan, Amos, Mastrandrea, Rossana, Morris, Robert, Rinaldo, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042144/
https://www.ncbi.nlm.nih.gov/pubmed/29891709
http://dx.doi.org/10.1073/pnas.1804484115
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author Balister, Paul
Balogh, József
Bertuzzo, Enrico
Bollobás, Béla
Caldarelli, Guido
Maritan, Amos
Mastrandrea, Rossana
Morris, Robert
Rinaldo, Andrea
author_facet Balister, Paul
Balogh, József
Bertuzzo, Enrico
Bollobás, Béla
Caldarelli, Guido
Maritan, Amos
Mastrandrea, Rossana
Morris, Robert
Rinaldo, Andrea
author_sort Balister, Paul
collection PubMed
description We study tree structures termed optimal channel networks (OCNs) that minimize the total gravitational energy loss in the system, an exact property of steady-state landscape configurations that prove dynamically accessible and strikingly similar to natural forms. Here, we show that every OCN is a so-called natural river tree, in the sense that there exists a height function such that the flow directions are always directed along steepest descent. We also study the natural river trees in an arbitrary graph in terms of forbidden substructures, which we call [Formula: see text]-path obstacles, and OCNs on a [Formula: see text]-dimensional lattice, improving earlier results by determining the minimum energy up to a constant factor for every [Formula: see text]. Results extend our capabilities in environmental statistical mechanics.
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spelling pubmed-60421442018-07-13 River landscapes and optimal channel networks Balister, Paul Balogh, József Bertuzzo, Enrico Bollobás, Béla Caldarelli, Guido Maritan, Amos Mastrandrea, Rossana Morris, Robert Rinaldo, Andrea Proc Natl Acad Sci U S A Physical Sciences We study tree structures termed optimal channel networks (OCNs) that minimize the total gravitational energy loss in the system, an exact property of steady-state landscape configurations that prove dynamically accessible and strikingly similar to natural forms. Here, we show that every OCN is a so-called natural river tree, in the sense that there exists a height function such that the flow directions are always directed along steepest descent. We also study the natural river trees in an arbitrary graph in terms of forbidden substructures, which we call [Formula: see text]-path obstacles, and OCNs on a [Formula: see text]-dimensional lattice, improving earlier results by determining the minimum energy up to a constant factor for every [Formula: see text]. Results extend our capabilities in environmental statistical mechanics. National Academy of Sciences 2018-06-26 2018-06-11 /pmc/articles/PMC6042144/ /pubmed/29891709 http://dx.doi.org/10.1073/pnas.1804484115 Text en Copyright © 2018 the Author(s). Published by PNAS. 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
Balister, Paul
Balogh, József
Bertuzzo, Enrico
Bollobás, Béla
Caldarelli, Guido
Maritan, Amos
Mastrandrea, Rossana
Morris, Robert
Rinaldo, Andrea
River landscapes and optimal channel networks
title River landscapes and optimal channel networks
title_full River landscapes and optimal channel networks
title_fullStr River landscapes and optimal channel networks
title_full_unstemmed River landscapes and optimal channel networks
title_short River landscapes and optimal channel networks
title_sort river landscapes and optimal channel networks
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042144/
https://www.ncbi.nlm.nih.gov/pubmed/29891709
http://dx.doi.org/10.1073/pnas.1804484115
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