Abstract
Mass movements of different types and sizes are the main processes of sea cliff evolution, being a considerable natural hazard, the assessment of which is a relevant issue in terms of human loss prevention and land use regulations. To predict the occurrence of future failures affecting the cliff top in slow retreating cliffs, a study was made using the logistic regression statistical method, a set of predisposing factors mainly related with geology (lithology, structure, faults), geomorphology (maximum, mean and standard variation of slope angle, height, aspect, curvatures, toe protection) and near offshore mean annual wave power, which were correlated with an aerial photo interpretation based inventory of cliff failures occurred in a 63 years period (1947–2010). The susceptibility model was validated against the inventory data using standard Receiver Operator Curves, which provided area under the curve (AUC) values higher than 0.8. In spite of the room for improvement of cliff failure inventories and predisposing factors to be used in these types of studies, namely those related to the rock mass strength and wave power nearshore, the results obtained indicate that the proposed approach is an effective contribution for objective and quantitative hazard assessment.
Highlights
Sea cliff evolution is dominated by the occurrence of slope mass movements, encompassing rock falls, topples and different types of landslides [1,2], all of which can be considered as a natural hazard and a major threat to human activities and safe land use in cliff dominated coastal areas [3]
To predict the possibility of occurrence of future failures affecting areas located along the top of the sea cliffs of Sintra and Cascais counties coast, a cliff instability hazard assessment was performed, based on the application of the landslide hazard proven multivariate statistical method of logistic regression (e.g., [36] and references therein), to a set of predisposing factors related with geology, geomorphology and wave power, which were correlated with an inventory of past cliff failures
Considering previous results obtained in this type of studies [12,37] which indicated that the logistic regression provided better models than bi variate statistical methods, the sea cliff failure inventory relations with the conditioning factors selected were assessed using this multi-variate statistical method [44], which consists of the regression of a dichotomic dependent variable (0 without instabilities, 1 with instabilities) with a set of explanatory independent variables which may be continuous, categorical or dichotomic
Summary
Sea cliff evolution is dominated by the occurrence of slope mass movements, encompassing rock falls, topples and different types of landslides [1,2], all of which can be considered as a natural hazard and a major threat to human activities and safe land use in cliff dominated coastal areas [3]. Sea cliff and rock coasts have received much lower research efforts than the fast evolution sandy shorelines [11], with soft cliff being the most commonly studied, and the lower retreat rate and strong and intermediate strength cliffs receiving much less attention [12] This is probably due to a combination of factors which include the difficulties in monitoring an episodic, comparatively low space and time frequency event-based process, located mostly in highly irregular and frequently inaccessible locations, which is poorly represented in aerial photos and maps. Due to the large extension of coast studied, maps of predisposing factors lithology, cliff toe protection and mean annual wave power are presented as supplementary material to the paper, together with sea cliff and cliff failure photographs
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