Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models Article Swipe
Chris Skinner
,
Tom Coulthard
,
Wolfgang Schwanghart
,
Marco Van DeWiel
,
Gregory Hancock
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.31223/osf.io/rkw2v
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.31223/osf.io/rkw2v
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.
Related Topics
Concepts
Sensitivity (control systems)
Sophistication
Computer science
Term (time)
Set (abstract data type)
Scale (ratio)
Function (biology)
Uncertainty analysis
Econometrics
Geography
Mathematics
Simulation
Cartography
Engineering
Electronic engineering
Programming language
Evolutionary biology
Quantum mechanics
Social science
Biology
Sociology
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31223/osf.io/rkw2v
- OA Status
- gold
- Cited By
- 1
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4248542478
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4248542478Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31223/osf.io/rkw2vDigital Object Identifier
- Title
-
Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-11-08Full publication date if available
- Authors
-
Chris Skinner, Tom Coulthard, Wolfgang Schwanghart, Marco Van DeWiel, Gregory HancockList of authors in order
- Landing page
-
https://doi.org/10.31223/osf.io/rkw2vPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.31223/osf.io/rkw2vDirect OA link when available
- Concepts
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Sensitivity (control systems), Sophistication, Computer science, Term (time), Set (abstract data type), Scale (ratio), Function (biology), Uncertainty analysis, Econometrics, Geography, Mathematics, Simulation, Cartography, Engineering, Electronic engineering, Programming language, Evolutionary biology, Quantum mechanics, Social science, Biology, Sociology, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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