Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1267-2019
https://doi.org/10.5194/gmd-12-1267-2019
Model description paper
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03 Apr 2019
Model description paper | Highlight paper |  | 03 Apr 2019

Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

Katherine R. Barnhart, Rachel C. Glade, Charles M. Shobe, and Gregory E. Tucker

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Cited articles

Ahnert, F.: Brief description of a comprehensive three-dimensional process-response model of landform development, Z. Geomorfol., Supplementband, 25, 29–49, 1976.
Andrews, D. J. and Bucknam, R. C.: Fitting degradation of shoreline scarps by a nonlinear diffusion model, J. Geophys. Res., 92, 12857–12867, https://doi.org/10.1029/JB092iB12p12857, 1987.
Andrews, D. J. and Hanks, T. C.: Scarp degraded by linear diffusion: Inverse solution for age, J. Geophys. Res., 90, 10193–10208, https://doi.org/10.1029/JB090iB12p10193, 1985.
Attal, M., Tucker, G. E., Whittaker, A. C., Cowie, P. A., and Roberts, G. P.: Modeling fluvial incision and transient landscape evolution: Influence of dynamic channel adjustment, J. Geophys. Res., 113, F03013, https://doi.org/10.1029/2007JF000893, 2008.
Attal, M., Cowie, P., Whittaker, A., Hobley, D., Tucker, G., and Roberts, G.: Testing fluvial erosion models using the transient response of bedrock rivers to tectonic forcing in the Apennines, Italy, J. Geophys. Res., 116, F02005, https://doi.org/10.1029/2010JF001875, 2011.
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Short summary
Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution.