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dc.creatorMorgan, Savannahes_ES
dc.creatorHuffaker, Rayes_ES
dc.creatorGiménez Díaz, Rafaeles_ES
dc.creatorCampo-Bescós, Migueles_ES
dc.creatorMuñoz Carpena, Rafaeles_ES
dc.creatorGovers, G.es_ES
dc.date.accessioned2023-03-27T13:31:22Z
dc.date.available2023-03-27T13:31:22Z
dc.date.issued2022
dc.identifier.citationMorgan, S., Huffaker, R., Giménez, R., Campo-Bescos, M. A., Muñoz-Carpena, R., & Govers, G. (2022). Experimental evidence that rill-bed morphology is governed by emergent nonlinear spatial dynamics. Scientific Reports, 12(1), 21500. https://doi.org/10.1038/s41598-022-26114-0en
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/2454/44960
dc.description.abstractPast experimental work found that rill erosion occurs mainly during rill formation in response to feedback between rill-flow hydraulics and rill-bed roughness, and that this feedback mechanism shapes rill beds into a succession of step-pool units that self-regulates sediment transport capacity of established rills. The search for clear regularities in the spatial distribution of step-pool units has been stymied by experimental rill-bed profiles exhibiting irregular fluctuating patterns of qualitative behavior. We hypothesized that the succession of step-pool units is governed by nonlinear-deterministic dynamics, which would explain observed irregular fluctuations. We tested this hypothesis with nonlinear time series analysis to reverse-engineer (reconstruct) state-space dynamics from fifteen experimental rill-bed profiles analyzed in previous work. Our results support this hypothesis for rill-bed profiles generated both in a controlled lab (flume) setting and in an in-situ hillside setting. The results provide experimental evidence that rill morphology is shaped endogenously by internal nonlinear hydrologic and soil processes rather than stochastically forced; and set a benchmark guiding specification and testing of new theoretical framings of rill-bed roughness in soil-erosion modeling. Finally, we applied echo state neural network machine learning to simulate reconstructed rill-bed dynamics so that morphological development could be forecasted out-of-sample.en
dc.description.sponsorshipR.H. acknowledges support from USDA-NIFA (FLA-ABE-005414). R.H. and R.M-C. acknowledge support from the University of Florida Artificial Intelligence Research Catalyst Fund. R.G. and M.C-B. acknowledges funding from the Ministerio de Economía y Competitividad (Government of Spain) via the Research Project CGL2015-64284-C2-1-R.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringer Natureen
dc.relation.ispartofScientifc Reports (2022) 12:21500en
dc.rights© 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRill-bed morphologyen
dc.subjectEmergent nonlinear spatial dynamicsen
dc.titleExperimental evidence that rill-bed morphology is governed by emergent nonlinear spatial dynamicsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.date.updated2023-03-27T13:21:07Z
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1038/s41598-022-26114-0
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//CGL2015-64284-C2-1-R/ES/en
dc.relation.publisherversionhttps://doi.org/10.1038/s41598-022-26114-0
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen


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© 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as © 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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