@article{7c28f107b04e45689056e45519fe0693,
title = "Spectral Signature of Landscape Channelization",
abstract = "Channel networks increase in complexity as the importance of erosion grows compared to diffusion by soil creep, giving rise to a channelization cascade. Simulations, laboratory experiments, and data from a natural landscape are used to uncover the signature of such a cascade in the wavenumber spectrum of elevation fluctuations. Power spectra at intermediate distances from the boundaries are characterized by a peak wavenumber that is related to the quasi-cyclic valleys superimposed on a power-law scaling with exponent (α) at smaller scales. Dimensional analysis and self-similarity arguments show that α is uniquely linked to the power-law relation (with exponent m) between erosion potential and the specific drainage area via α = 2m − 3. This finding connects the spectral behavior of erosional surfaces to the exponent m that distinguishes between the steep landscapes with debris-flow-dominated channels and relatively flat fluvial landscapes and directly controls the shape of the channel profile.",
author = "Milad Hooshyar and Gabriel Katul and Amilcare Porporato",
note = "Funding Information: Amilcare Porporato acknowledges support from the US National Science Foundation (NSF) grants EAR-1331846 and EAR-1338694, and BP through the Carbon Mitigation Initiative (CMI) at Princeton University. Milad Hooshyar acknowledges support from the Princeton Institute for International and Regional Studies (PIIRS) and the Princeton Environmental Institute (PEI). The physical experiments are originally performed by Singh et?al.?(2015). The link to the simulation code used in this work is provided in Anand et?al.?(2019). The numerical simulations in this article were performed on computational resources provided by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology?s High-Performance Computing Center and Visualization Laboratory at Princeton University. We thank Stefano Orlandini and an anonymous reviewer for useful comments. Funding Information: Amilcare Porporato acknowledges support from the US National Science Foundation (NSF) grants EAR‐1331846 and EAR‐1338694, and BP through the Carbon Mitigation Initiative (CMI) at Princeton University. Milad Hooshyar acknowledges support from the Princeton Institute for International and Regional Studies (PIIRS) and the Princeton Environmental Institute (PEI). The physical experiments are originally performed by Singh et al. ( 2015 ). The link to the simulation code used in this work is provided in Anand et al. ( 2019 ). The numerical simulations in this article were performed on computational resources provided by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology{\textquoteright}s High‐Performance Computing Center and Visualization Laboratory at Princeton University. We thank Stefano Orlandini and an anonymous reviewer for useful comments. Publisher Copyright: {\textcopyright} 2021. American Geophysical Union. All Rights Reserved.",
year = "2021",
month = apr,
day = "28",
doi = "10.1029/2020GL091015",
language = "English (US)",
volume = "48",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "8",
}