Identification and characterization of dendritic, parallel, pinnate, rectangular, and trellis networks based on deviations from planform self-similarity

Alfonso Ignacio Mejia, Jeffrey D. Niemann

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Geomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification. methods are scale dependent and have little connection to any underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks on the basis of their deviations from self-similarity. The planform geometry of dendritic networks is known to be approximately self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to 10 networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to exhibit anisotropic scaling. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test simple classification trees, which are found to perform well in classifying networks.

Original languageEnglish (US)
Article numberF02015
JournalJournal of Geophysical Research: Earth Surface
Volume113
Issue number2
DOIs
StatePublished - Jun 24 2008

Fingerprint

planforms
Planforms
Drainage
taxonomy
deviation
drainage
Geometry
drainage network
geometry
tectonics
Tectonics
Merging
methodology
testing

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

@article{0119d59f11a44cd7842b3f34b5934d64,
title = "Identification and characterization of dendritic, parallel, pinnate, rectangular, and trellis networks based on deviations from planform self-similarity",
abstract = "Geomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification. methods are scale dependent and have little connection to any underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks on the basis of their deviations from self-similarity. The planform geometry of dendritic networks is known to be approximately self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to 10 networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to exhibit anisotropic scaling. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test simple classification trees, which are found to perform well in classifying networks.",
author = "Mejia, {Alfonso Ignacio} and Niemann, {Jeffrey D.}",
year = "2008",
month = "6",
day = "24",
doi = "10.1029/2007JF000781",
language = "English (US)",
volume = "113",
journal = "Journal of Geophysical Research",
issn = "0148-0227",
publisher = "American Geophysical Union",
number = "2",

}

TY - JOUR

T1 - Identification and characterization of dendritic, parallel, pinnate, rectangular, and trellis networks based on deviations from planform self-similarity

AU - Mejia, Alfonso Ignacio

AU - Niemann, Jeffrey D.

PY - 2008/6/24

Y1 - 2008/6/24

N2 - Geomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification. methods are scale dependent and have little connection to any underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks on the basis of their deviations from self-similarity. The planform geometry of dendritic networks is known to be approximately self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to 10 networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to exhibit anisotropic scaling. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test simple classification trees, which are found to perform well in classifying networks.

AB - Geomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification. methods are scale dependent and have little connection to any underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks on the basis of their deviations from self-similarity. The planform geometry of dendritic networks is known to be approximately self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to 10 networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to exhibit anisotropic scaling. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test simple classification trees, which are found to perform well in classifying networks.

UR - http://www.scopus.com/inward/record.url?scp=50849135584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50849135584&partnerID=8YFLogxK

U2 - 10.1029/2007JF000781

DO - 10.1029/2007JF000781

M3 - Article

VL - 113

JO - Journal of Geophysical Research

JF - Journal of Geophysical Research

SN - 0148-0227

IS - 2

M1 - F02015

ER -