scholarly journals Inverting Topography for Landscape Evolution Model Process Representation: 1. Conceptualization and Sensitivity Analysis

2020 ◽  
Vol 125 (7) ◽  
Author(s):  
Katherine R. Barnhart ◽  
Gregory E. Tucker ◽  
Sandra G. Doty ◽  
Charles M. Shobe ◽  
Rachel C. Glade ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Landscape Evolution ◽  
Evolution Model ◽  
Process Representation

scholarly journals Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation

2020 ◽  
Vol 125 (7) ◽  
Author(s):  
Katherine R. Barnhart ◽  
Gregory E. Tucker ◽  
Sandra G. Doty ◽  
Charles M. Shobe ◽  
Rachel C. Glade ◽  
...  
Keyword(s):  
Landscape Evolution ◽  
Evolution Model ◽  
Calibration And Validation ◽  
Process Representation

GRAINSIZE DEPENDENT TRANSPORT AND DEPOSITION IN A LANDSCAPE EVOLUTION MODEL

2019 ◽  
Author(s):  
Erica Emry ◽  
◽  
Kyungdoe Han ◽  
Michael Berry ◽  
Jolante van Wijk ◽  
...  
Keyword(s):  
Landscape Evolution ◽  
Evolution Model ◽  
Dependent Transport

scholarly journals Global sensitivity analysis of parameter uncertainty in landscape evolution models

2018 ◽  
Vol 11 (12) ◽  
pp. 4873-4888 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Landscape Evolution ◽  
Relative Sensitivity ◽  
Function Approach ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Function ◽  
Sediment Yields ◽  
Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

SIGNUM: A Matlab, TIN-based landscape evolution model

2012 ◽  
Vol 45 ◽  
pp. 293-303 ◽  
Author(s):  
A. Refice ◽  
E. Giachetta ◽  
D. Capolongo
Keyword(s):  
Landscape Evolution ◽  
Evolution Model

scholarly journals Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

2017 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Global Sensitivity Analysis ◽  
Landscape Evolution ◽  
Sensitivity Analyses ◽  
Large Set ◽  
Sediment Yields ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Morris Method

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.

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