Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-236
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model evaluation paper
18 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models
Christopher J. Skinner1, Tom J. Coulthard1, Wolfgang Schwanghart2, Marco J. Van De Wiel3, and Greg Hancock4 1University of Hull, Hull, UK
2Postdam University, Postdam, Germany
3Coventry University, Coventry, UK
4University of Newcastle, Callaghan, Australia
Abstract. Landscape Evolution Models have a long history of use as exploratory models, providing greater understanding of the role large scale processes have on the long-term development of the Earth’s surface. As computational power has advanced so has the development and sophistication of these models. This has seen them applied at increasingly smaller scale and shorter-term simulations at greater detail. However, this has not gone hand-in-hand with more rigorous verifications that are commonplace in the applications of other types of environmental models- for example Sensitivity Analyses.

This can be attributed to a paucity of data and methods available in order to calibrate, validate and verify the models, and also to the extra complexity Landscape Evolution Models represent – without these it is not possible to produce a reliable Objective Function against which model performance can be judged. To overcome this deficiency, we present a set of Model Functions – each representing an aspect of model behaviour – and use these to assess the relative sensitivity of a Landscape Evolution Model (CAESAR-Lisflood) to a large set of parameters via a global Sensitivity Analysis using the Morris Method. This novel combination of behavioural Model Functions and the Morris Method provides insight into which parameters are the greatest source of uncertainty in the model, and which have the greatest influence over different model behaviours. The method was repeated over two different catchments, showing that across both catchments and across most model behaviours the choice of Sediment Transport formula was the dominate source of uncertainty in the CAESAR-Lisflood model, although there were some differences between the two catchments. Crucially, different parameters influenced the model behaviours in different ways, with Model Functions related to internal geomorphic changes responding in different ways to those related to sediment yields from the catchment outlet.

This method of behavioural sensitivity analysis provides a useful method of assessing the performance of Landscape Evolution Models in the absence of data and methods for an Objective Function approach.


Citation: Skinner, C. J., Coulthard, T. J., Schwanghart, W., Van De Wiel, M. J., and Hancock, G.: Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-236, in review, 2017.
Christopher J. Skinner et al.
Christopher J. Skinner et al.
Christopher J. Skinner et al.

Viewed

Total article views: 334 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
247 80 7 334 10 4 5

Views and downloads (calculated since 18 Oct 2017)

Cumulative views and downloads (calculated since 18 Oct 2017)

Viewed (geographical distribution)

Total article views: 334 (including HTML, PDF, and XML)

Thereof 324 with geography defined and 10 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 20 Nov 2017
Publications Copernicus
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, yet to do so 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...
Share