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Hierarchically nested river landform sequences. Part 1: Theory
Author(s) -
Pasternack Gregory B.,
Baig Dastagir,
Weber Matthew D.,
Brown Rocko A.
Publication year - 2018
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.4411
Subject(s) - beach morphodynamics , landform , geology , scale (ratio) , fluvial , routing (electronic design automation) , flow routing , convergence (economics) , flow (mathematics) , erosion , geomorphology , hydrology (agriculture) , computer science , sediment transport , cartography , geography , sediment , geotechnical engineering , mathematics , geometry , structural basin , economic growth , economics , computer network
Past river classifications use incommensurate typologies at each spatial scale and do not capture the pivotal role of topographic variability at each scale in driving the morphodynamics responsible for evolving hierarchically nested fluvial landforms. This study developed a new way to create geomorphic classifications using metrics diagnostic of individual processes the same way at every spatial scale and spanning a wide range of scales. We tested the approach on flow convergence routing, a geomorphically and ecologically important process with different morphodynamic states of erosion, routing, and deposition depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality represent this process at any flow; they are nozzle, wide bar, normal channel, constricted pool, and oversized. These landforms are then nested within themselves by considering their longitudinal sequencing at key flows representing geomorphically important stages. A data analysis framework was developed to answer questions about the stage‐dependent spatial structure of topographic variability. Nesting permutations constrain and reveal how flow convergence routing morphodynamics functions in any river the framework is applied to. The methodology may also be used with other physical and biological datasets to evaluate the extent to which the patterning in that data is influenced by flow convergence routing. Copyright © 2018 John Wiley & Sons, Ltd.