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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 12
Geosci. Model Dev., 11, 4873–4888, 2018
https://doi.org/10.5194/gmd-11-4873-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 4873–4888, 2018
https://doi.org/10.5194/gmd-11-4873-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 06 Dec 2018

Model evaluation paper | 06 Dec 2018

Global sensitivity analysis of parameter uncertainty in landscape evolution models

Christopher J. Skinner et al.

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Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017.
Andersen, J. L., Egholm, D. L., Knudsen, M. F., Jansen, J. D., and Nielsen, S. B.: The periglacial engine of mountain erosion – Part 1: Rates of frost cracking and frost creep, Earth Surf. Dynam., 3, 447–462, https://doi.org/10.5194/esurf-3-447-2015, 2015.
Armitage, J. J., Whittaker, A. C., Zakari, M., and Campforts, B.: Numerical modelling of landscape and sediment flux response to precipitation rate change, Earth Surf. Dynam., 6, 77–99, https://doi.org/10.5194/esurf-6-77-2018, 2018.
Aronica, G., Bates, P. D., and Horritt, M. S.: Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Attal, M., Tucker, G. E., Whittaker, A. C., Cowie, P. A., and Roberts, G. P.: Modelling fluvial incision and transient landscape evolution: Influence of dynamic Channel adjustment, J. Geophys. Res.-Earth, 113, 1–16, https://doi.org/10.1029/2007JF000893, 2008.
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Short summary
Landscape evolution models are computer models used to understand how the Earth’s surface changes over time. Although designed to look at broad changes over very long time periods, they could potentially be used to predict smaller changes over shorter periods. However, to do this we need to better understand how the models respond to changes in their set-up – i.e. their behaviour. This work presents a method which can be applied to these models in order to better understand their behaviour.
Landscape evolution models are computer models used to understand how the Earth’s surface...
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