Resumen
Past 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 h ...
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Past 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. [--]
Materias
Rill-bed morphology,
Emergent nonlinear spatial dynamics
Publicado en
Scientifc Reports (2022) 12:21500
Departamento
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. ISFOOD - Institute on Innovation & Sustainable Development in Food Chain
Entidades Financiadoras
R.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.