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Abstract
Relationships between sediment flux and geomorphic processes are combined with statements of mass conservation, in order to create continuum models of hillslope evolution. These models have parameters which can be calibrated using available topographical data. This contrasts the use of particle‐based models, which may be more difficult to calibrate, but are simpler, easier to implement, and have the potential to provide insight into the statistics of grain motion. The realms of individual particles and the continuum, while disparate in geomorphological modeling, can be connected using scaling techniques commonly employed in probability theory. Here, we motivate the choice of a particle‐based model of hillslope evolution, whose stationary distributions we characterize. We then provide a heuristic scaling argument, which identifies a candidate for their continuum limit. By simulating instances of the particle model, we obtain equilibrium hillslope profiles and probe their response to perturbations. These results provide a proof‐of‐concept in the unification of microscopic and macroscopic descriptions of hillslope evolution through probabilistic techniques, and simplify the study of hillslope response to external influences.
Original language | English |
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Number of pages | 29 |
Journal | Journal of Geophysical Research: Earth Surface |
Early online date | 29 Oct 2018 |
DOIs | |
Publication status | E-pub ahead of print - 29 Oct 2018 |
Keywords
- hillslope
- particle model
- landscape evolution model
- probabilistic scaling
- geomorphic transport law
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Dive into the research topics of 'Unifying particle‐based and continuum models of hillslope evolution with a probabilistic scaling technique'. Together they form a unique fingerprint.Projects
- 1 Finished
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Stochastic interacting systems: connections, fluctuations and applications
Balazs, M. (Principal Investigator)
1/06/18 → 20/05/22
Project: Research
Profiles
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Professor Marton Balazs
- School of Mathematics - Professor of Probability
- Probability, Analysis and Dynamics
- Probability
Person: Academic , Member