Evaluating choices in multi-process landscape evolution models

Temme, A.J.A.M.; Claessens, Lieven; Veldkamp, A.; Schoorl, J.M.

doi:10.1016/j.geomorph.2010.10.007

Cited by 49 publications

(47 citation statements)

References 66 publications

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“…Such landscape evolution models have been used for different landforms to test the capability of the models (Perron et al, 2009;Bowman et al, 2010;Berthling and Etzelmüller, 2011;Fujioka and Chappell, 2011;Temme et al, 2011;Ciampalini et al, 2012;Egholm et al, 2012;Lehmkuhl et al, 2012;Ravazzi et al, 2013). The application of landscape evolution models in relation to climate change (Temme et al, 2009), soil hydraulic properties (Beerten et al, 2012), and natural landslide disasters (Hsieh et al, 2012) has also been discussed from different viewpoints.…”

Section: Introductionmentioning

confidence: 99%

Modeling the evolution of loess-covered landforms in the Loess Plateau of China using a DEM of underground bedrock surface

Xiong

Tang

et al. 2014

Geomorphology

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Section: Introductionmentioning

confidence: 99%

Modeling the evolution of loess-covered landforms in the Loess Plateau of China using a DEM of underground bedrock surface

Xiong

Tang

et al. 2014

Geomorphology

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“…These rules evolve for a number of time steps specified by the user for each land use category. First the Expander rule is applied, allowing both the expansion as well as the contraction, of previous patches to their nearest neighbour cells [9,21]. The remaining cells are then allocated to new independent patches using the Patcher rule.…”

Section: Modelling Approachmentioning

confidence: 99%

“…Fuzzy similarity is a multi-resolution validation technique that derives the overall similarity using two types of membership: no fuzziness and fuzziness of location, within a neighbourhood value [9,45]. The technique verifies the agreement between the observed and the simulated land use/cover datasets by obtaining the number of coincident cells within increasing window sizes of a neighbourhood [23,46] by means of a fuzzy neighbourhood vector.…”

Section: Modelling Approachmentioning

confidence: 99%

“…In general, different studies have demonstrated the importance of understanding and modelling the land use/cover change, to assess soil erosion and instability [6,7], to estimate carbon emissions from deforestation and forest degradation [8,9], to model water quality and quantity [10][11][12], to predict species habitat [13][14][15] , and on the intensity and size of wildfires [16].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Dynamic Modelling of Future Scenarios of Land Use Change in Vaud and Valais, Western Switzerland

Gago-Silva

Ray

Lehmann

2017

IJGI

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Abstract:We use Bayesian methods with a weights of evidence approach to model the probability of land use change over the Western part of Switzerland. This first model is followed by a cellular automata model for spatial allocation of land use classes. Our results extend and enhance current land use scenarios studies by applying Dinamica Environment for Geoprocessing Objects (Dinamica EG) to a study area comprising of the upper Rhone river basin in the Cantons of Vaud and Valais. In order to take into account the topography, we divide the study area into four regions, based on their altitude and administrative region. We show that the different regions are affected in differing ways by the same driving forces. We analyse possible outcomes in land use change in 2050 for three different scenarios: "business as usual", "liberalisation" and a "lowered agriculture production". The "business-as-usual" scenario results indicate a decrease in agriculture, mostly in extensive agriculture, with a share in the total area of 12. Gains in open land habitat with the increase of the share of extensive agriculture area under the "lowered agricultural production" scenario are expected to increase by 3.2% in 2050, while the share of intensive agriculture area is expected to decrease by 5.6%.

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“…Many different LEMs have been developed in the last two decades: APERO (Carretier and Lucazeau, 2005); CAESAR ; CASCADE (Braun and Sambridge, 1997), DELIM (DEtachment-LIMited) (Howard, 1994); EROS (Crave and Davy, 2001); GOLEM (Tucker and Slingerland, 1994); LANDSAP (Luo, 2001); LAPSUS (Temme et al, 2011b); LEGS (Paik, 2012); SIBERIA (Willgoose et al, 1991); and SIGNUM (Refice et al, 2012). Currently, the LEMs that have the most active user community are SIBERIA (Hancock et al, 2000), CHILD (Tucker and Bras, 1998), LAPSUS (Temme et al, 2011b) and CAESAR (Van De Wiel et al, 2007). Although these particular models allow selective fluvial grain size transport, most LEMs typically do not include landscape-soil feedbacks.…”

Section: Soil Transport Modelsmentioning

confidence: 99%