Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-823-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-823-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
29 Feb 2016
Model description paper |
| 29 Feb 2016
CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, USA
Department of Geological Sciences, University of Colorado, Boulder, USA
Daniel E. J. Hobley
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, USA
Department of Geological Sciences, University of Colorado, Boulder, USA
Eric Hutton
Community Surface Dynamics Modeling System (CSDMS), University of Colorado, Boulder, USA
Nicole M. Gasparini
Department of Earth and Environmental Sciences, Tulane University, New Orleans, USA
Erkan Istanbulluoglu
Department of Civil and Environmental Engineering, University of Washington, Seattle, USA
Jordan M. Adams
Department of Earth and Environmental Sciences, Tulane University, New Orleans, USA
Sai Siddartha Nudurupati
Department of Civil and Environmental Engineering, University of Washington, Seattle, USA
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Cited
14 citations as recorded by crossref.
- The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds J. Adams et al. 10.5194/gmd-10-1645-2017
- A reduced-complexity model for sediment transport and step-pool morphology M. Saletti et al. 10.5194/esurf-4-549-2016
- A geomorphic-process-based cellular automata model of colluvial wedge morphology and stratigraphy H. Gray et al. 10.5194/esurf-10-329-2022
- Relative timing of uplift along the Zagros Mountain Front Flexure (Kurdistan Region of Iraq): Constrained by geomorphic indices and landscape evolution modeling M. Zebari et al. 10.5194/se-10-663-2019
- Luminescence sediment tracing reveals the complex dynamics of colluvial wedge formation H. Gray et al. 10.1126/sciadv.abo0747
- Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics D. Hobley et al. 10.5194/esurf-5-21-2017
- A lattice grain model of hillslope evolution G. Tucker et al. 10.5194/esurf-6-563-2018
- The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution C. Shobe et al. 10.5194/gmd-10-4577-2017
- Visual Analytics to Identify Temporal Patterns and Variability in Simulations from Cellular Automata P. Giabbanelli & M. Baniukiewicz 10.1145/3265748
- A hydroclimatological approach to predicting regional landslide probability using Landlab R. Strauch et al. 10.5194/esurf-6-49-2018
- Modeling the Shape and Evolution of Normal‐Fault Facets G. Tucker et al. 10.1029/2019JF005305
- Easy, fast and reproducible Stochastic Cellular Automata with chouca A. Génin et al. 10.24072/pcjournal.466
- CHONK 1.0: landscape evolution framework: cellular automata meets graph theory B. Gailleton et al. 10.5194/gmd-17-71-2024
- Open-source modular solutions for flexural isostasy: gFlex v1.0 A. Wickert 10.5194/gmd-9-997-2016
13 citations as recorded by crossref.
- The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds J. Adams et al. 10.5194/gmd-10-1645-2017
- A reduced-complexity model for sediment transport and step-pool morphology M. Saletti et al. 10.5194/esurf-4-549-2016
- A geomorphic-process-based cellular automata model of colluvial wedge morphology and stratigraphy H. Gray et al. 10.5194/esurf-10-329-2022
- Relative timing of uplift along the Zagros Mountain Front Flexure (Kurdistan Region of Iraq): Constrained by geomorphic indices and landscape evolution modeling M. Zebari et al. 10.5194/se-10-663-2019
- Luminescence sediment tracing reveals the complex dynamics of colluvial wedge formation H. Gray et al. 10.1126/sciadv.abo0747
- Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics D. Hobley et al. 10.5194/esurf-5-21-2017
- A lattice grain model of hillslope evolution G. Tucker et al. 10.5194/esurf-6-563-2018
- The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution C. Shobe et al. 10.5194/gmd-10-4577-2017
- Visual Analytics to Identify Temporal Patterns and Variability in Simulations from Cellular Automata P. Giabbanelli & M. Baniukiewicz 10.1145/3265748
- A hydroclimatological approach to predicting regional landslide probability using Landlab R. Strauch et al. 10.5194/esurf-6-49-2018
- Modeling the Shape and Evolution of Normal‐Fault Facets G. Tucker et al. 10.1029/2019JF005305
- Easy, fast and reproducible Stochastic Cellular Automata with chouca A. Génin et al. 10.24072/pcjournal.466
- CHONK 1.0: landscape evolution framework: cellular automata meets graph theory B. Gailleton et al. 10.5194/gmd-17-71-2024
1 citations as recorded by crossref.
Saved (preprint)
Latest update: 15 Nov 2024
Short summary
This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
This paper presents a new Python-language software library, called CellLab-CTS, that enables...
