2010. spatial analysis and modelling of periglacial processes in hurd peninsula - poster

spatial analysis and modelling of periglacial processes in Hurd Peninsula, Livingston Island (Antarctic Peninsula)

Marco Jorge 1 , Gonçalo Vieira* 1, Miguel Ramos 21) CEG-IGOT - University of Lisbon , Portugal

2) Department of Physics, Universidad Alcalá de Henares, Spain

This work is the first attempt to model the spatial distribution of periglacial processes in Hurd Peninsula. As so, at this point methodologic issues are a major concern.

Both bivariate and multivariate statistical models were used to create models of susceptibility to the ocurrence (in the space domain) of solifluction lobes. The analysis was performed in the vicinity of the the Juan Carlos I Spanish Antarctic Sation, where detailed topographic information is available.

AIMSCarachterize the spatial distribution of stone-banked solifluction lobes; evaluate the environmental factors that control the spatial distribution of the lobes; and use statistical models to assess the susceptibility of the terrain to the the ocurrence of those geofeatures.

x x(1:9)

x1 x2 unique condition terrain units

bivariate weighting

multivariateweighting

SUSCEPTIBILITY MODELS

information value (Yin and Yan, 1988)

logistic regression

moraine ridgetillmoraine, vulcanic

bedrock

stone-banked solifluction lobes

rock glacier

scree / talus slopefrost-shattered debris

seasonal lake

fluvioglacial fan

sandy sediments

fluvioglacial terraceephemeral streamraised beach ridgecobble beach

lithology (surficial deposits)slopeaspect

SUSCEPTIBILITY MODEL WITHOUT THE "BEST COVARIATE" (LITHOLOGY)

BEST SUSCEPTIBILITY MODEL

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Solifluction lobes are abundant and affect mainly frost-shattered debris. These deposits derive from the older moraine material, longer exposed to the frost weathering. Thus, their spatial distribution relates closely to the pattern of deglaciation.

Both the Logistic regression model and the Information Value model yeld very similar results, both on the success rate and on the covariates that relate the most with the distribution of the stone-banked solifluction lobes.

The statistical models of susceptibility to the ocurrence of stone-banked solifluction lobes show good results. Though, the quality of these models is limited by the small dimension of the study area and by the fact that the activity of the lobes was not mapped

The next step is to make the modelling at the scale of the Hurd Peninsula (c. 20 square km). New susceptibily models will be made following the same methods and their prediction capabitilities will be evaluated in areas not used to build the models.

Then, If climate surrogates appear as strong covariates, the dimension "time" can be added to the modelling, so that susceptibility maps can be derived for different climatic conditions.

Bibliography

Yin KL and Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Landslides-Glissements de Terrain. Proceedings V International Symposium on Landslides, Vol. 2, Lausanne, Switzerland, pp 1269–1272

layers / variables crossed with the dependent layer

Results, discussion

Methods

geomorphologic map

Study AreaIntroduction, aims

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