Exploring the sensitivity on a soil area-slope-grading relationship to
changes in process parameters using a pedogenesis model

Welivitiya, W. D. D. P.; Willgoose, Garry; Hancock, Greg; Cohen, Sagy

doi:10.5194/esurf-4-607-2016

Cited by 25 publications

(16 citation statements)

References 47 publications

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“…For clarity, and in line with how the choice is presented within the graphical user interface of the model, we henceforth consider this choice in the same way as a parameter. The sediment transport formulae employed for SED were from Einstein (derived for sand-bed rivers) (Einstein, 1950) and Wilcock and Crowe (formulated on sediment ranges between 0.5 and 64 mm) (Wilcock and Crowe, 2003). These were not selected as representing the best fit for the catchments simulated but because they are the formulae available in the unmodified version of CAESAR-Lisflood.…”

Section: User-defined Parametersmentioning

confidence: 99%

Global sensitivity analysis of parameter uncertainty in landscape evolution models

et al. 2018

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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.

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Section: User-defined Parametersmentioning

confidence: 99%

Global sensitivity analysis of parameter uncertainty in landscape evolution models

et al. 2018

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“…increasingly complex processes such as pedogenesis (Vanwalleghem et al, 2013;Welivitiya et al, 2016) and periglacial processes (Andersen et al, 2015;Egholm et al, 2015). Other processes are now being handled in more detail such as hydrodynamic flow models and aeolian processes (Adams et al, 2017;Liu and Coulthard, 2017).…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

Skinner¹,

Coulthard²,

Schwanghart³

et al. 2017

Preprint

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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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“…Extending the mARM model, Cohen et al (2010) developed mARM3D, which can model soil profile particle-size distribution at a large spatial extent. The SSSPAM (Welivitiya et al, 2016) generalized the formulation of mARM3D and extended the previous research to test more general conditions. Other models such as MILESD (Vanwalleghem et al, 2013) and LORICA (Temme & Vanwalleghem, 2016) incorporated various chemical and biological processes in the simulation.…”

Section: Mechanistic Pedological Models In Dsmmentioning

confidence: 98%

“…Researchers have found that there are strong links between the soils and geomorphology of the landform where they occur (Welivitiya et al, 2016). Landscape evolution models try to model soil development as governed by weathering, erosion or deposition.…”

Section: Mechanistic Pedological Models In Dsmmentioning

confidence: 99%

Pedology and digital soil mapping (DSM)

Minasny

Malone

et al. 2019

European J Soil Science

175

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Pedology focuses on understanding soil genesis in the field and includes soil classification and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and mapping to the creation and population of spatial soil information systems by using field and laboratory observations coupled with environmental covariates. Pedological knowledge of soil distribution and processes can be useful for digital soil mapping. Conversely, digital soil mapping can bring new insights to pedogenesis, detailed information on vertical and lateral soil variation, and can generate research questions that were not considered in traditional pedology. This review highlights the relevance and synergy of pedology in soil spatial prediction through the expansion of pedological knowledge. We also discuss how DSM can support further advances in pedology through improved representation of spatial soil information. Some major findings of this review are as follows: (a) soil classes can be mapped accurately using DSM, (b) the occurrence and thickness of soil horizons, whole soil profiles and soil parent material can be predicted successfully with DSM techniques, (c) DSM can provide valuable information on pedogenic processes (e.g. addition, removal, transformation and translocation), (d) pedological knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge, and (e) there is the potential to use process‐based soil–landscape evolution modelling in DSM. Based on these findings, the combination of data‐driven and knowledge‐based methods promotes even greater interactions between pedology and DSM. Highlights Demonstrates relevance and synergy of pedology in soil spatial prediction, and links pedology and DSM. Indicates the successful application of DSM in mapping soil classes, profiles, pedological features and processes. Shows how DSM can help in forming new hypotheses and gaining new insights about soil and soil processes. Combination of data‐driven and knowledge‐based methods recommended to promote greater interactions between DSM and pedology.

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Exploring the sensitivity on a soil area-slope-grading relationship to changes in process parameters using a pedogenesis model

Cited by 25 publications

References 47 publications

Global sensitivity analysis of parameter uncertainty in landscape evolution models

Global sensitivity analysis of parameter uncertainty in landscape evolution models

Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

Pedology and digital soil mapping (DSM)

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