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A neutral model as a null hypothesis test for river network sinuosity

Authors :
L. Salomon
C. Gaucherel
BotAnique et BioinforMatique de l'Architecture des Plantes
Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut de Recherche pour le Développement (IRD [France-Ouest])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Institut Français de Pondichéry (IFP)
Ministère de l'Europe et des Affaires étrangères (MEAE)-Centre National de la Recherche Scientifique (CNRS)
Agencia Espanola de Cooperacion Internacional para el Desarrollo (AECID)
Source :
Geomorphology, Geomorphology, Elsevier, 2014, 214, pp.416-422. ⟨10.1016/j.geomorph.2014.02.022⟩
Publication Year :
2014
Publisher :
Elsevier BV, 2014.

Abstract

International audience; Neutral models (NMs) are built to test null hypotheses and to detect properties at work in an object or a system. While several studies in geomorphology have used NMs without explicitly mentioning them or describing how they were built, it must be recognized that neutral models more often concerned theoretical explorations that drove such use. In this paper, we propose a panel of NMs of river (channel) networks based on a well-established relationship between observed and simulated sinuosity properties. We first simulated new instances of river networks with a (one-parameter) neutral model based on optimal channel networks (OCN) and leading to homogeneous sinuosity watersheds. We then proposed a "less neutral" model able to generate a variety of river networks accounting for the spatial heterogeneity of observed properties such as elevation. These models, providing confidence levels, allowed us to certify that some properties played a role in the generation of the observed network. Finally, we demonstrated and illustrated both models on the Bidasoa watershed (Spain-France frontier), with a new dedicated software (called SSM). NMs in geomorphology ensure to progressively help to identify the process operating in an observed object, and to ultimately improve our understanding of it (i.e. intrinsic need). But they also provide simulated samples statistically "similar" to an observed one, thus offering new alternatives to every process carried by the observed object (i.e. extrinsic need). Artificial river networks studied here would be of great value to environmental sciences studying geomorphology and freshwater-related processes.

Details

ISSN :
0169555X
Volume :
214
Database :
OpenAIRE
Journal :
Geomorphology
Accession number :
edsair.doi.dedup.....a51a04d329bd4d5c541baa841031fd2e
Full Text :
https://doi.org/10.1016/j.geomorph.2014.02.022