Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-4873-2018
https://doi.org/10.5194/gmd-11-4873-2018
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, Tom J. Coulthard, Wolfgang Schwanghart, Marco J. Van De Wiel, and Greg Hancock

Related authors

Localised geomorphic response to channel-spanning leaky wooden dams
Joshua M. Wolstenholme, Christopher J. Skinner, David J. Milan, Robert E. Thomas, and Daniel R. Parsons
EGUsphere, https://doi.org/10.5194/egusphere-2024-3001,https://doi.org/10.5194/egusphere-2024-3001, 2024
This preprint is open for discussion and under review for Earth Surface Dynamics (ESurf).
Short summary
Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j
Joshua M. Wolstenholme, Christopher J. Skinner, David J. Milan, Robert E. Thomas, and Daniel R. Parsons
EGUsphere, https://doi.org/10.5194/egusphere-2024-2132,https://doi.org/10.5194/egusphere-2024-2132, 2024
Short summary
Testing the sensitivity of the CAESAR-Lisflood landscape evolution model to grid cell size
Christopher J. Skinner and Thomas J. Coulthard
Earth Surf. Dynam., 11, 695–711, https://doi.org/10.5194/esurf-11-695-2023,https://doi.org/10.5194/esurf-11-695-2023, 2023
Short summary
Flash Flood!: a SeriousGeoGames activity combining science festivals, video games, and virtual reality with research data for communicating flood risk and geomorphology
Chris Skinner
Geosci. Commun., 3, 1–17, https://doi.org/10.5194/gc-3-1-2020,https://doi.org/10.5194/gc-3-1-2020, 2020
Short summary
Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response
Nadav Peleg, Chris Skinner, Simone Fatichi, and Peter Molnar
Earth Surf. Dynam., 8, 17–36, https://doi.org/10.5194/esurf-8-17-2020,https://doi.org/10.5194/esurf-8-17-2020, 2020
Short summary

Related subject area

Numerical methods
A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 17, 8853–8872, https://doi.org/10.5194/gmd-17-8853-2024,https://doi.org/10.5194/gmd-17-8853-2024, 2024
Short summary
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev., 17, 8799–8816, https://doi.org/10.5194/gmd-17-8799-2024,https://doi.org/10.5194/gmd-17-8799-2024, 2024
Short summary
The Paleochrono-1.1 probabilistic model to derive a common age model for several paleoclimatic sites using absolute and relative dating constraints
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Olander Rasmussen
Geosci. Model Dev., 17, 8735–8750, https://doi.org/10.5194/gmd-17-8735-2024,https://doi.org/10.5194/gmd-17-8735-2024, 2024
Short summary
Explicit stochastic advection algorithms for the regional-scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)
Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024,https://doi.org/10.5194/gmd-17-8399-2024, 2024
Short summary
Enhancing Single-Precision with Quasi Double-Precision: Achieving Double-Precision Accuracy in the Model for Prediction Across Scales-Atmosphere (MPAS-A) version 8.2.1
Jiayi Lai, Lanning Wang, Qizhong Wu, Yizhou Yang, and Fang Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2986,https://doi.org/10.5194/egusphere-2024-2986, 2024
Short summary

Cited articles

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.
Download
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.