Tectonic versus rock-controlled mountain fronts – Geomorphometric and geostatistical approach (Sowie Mts., Central Europe)

Abstract

Distinguishing between mountain fronts of various origins is an issue of considerable scientific and practical importance. Whereas morphometry is often applied to fault-generated fronts and a range of indices has been proposed to assess their activity, comparative studies of fault-generated and fault-line escarpments seem very rare. In this paper we analyze two mountain fronts delimiting the Sowie Mountains block (SW Poland, Central Europe) from the opposite sides. One is evidently a fault-generated escarpment, whereas the other one coincides with a distinct lithological boundary, although along an old Variscan fault, and appears to be a fault-line escarpment. These two fronts are characterized in terms of sinuosity, spacing ratio, slope characteristics, altitude variability using geostatistical approach, longitudinal stream profiles, drainage basin characteristics and the presence of mid-slope flats within the elevated block. Clustering and statistical hypothesis testing were used to recognize patterns of spatial similarities and differences. The main findings emerging from this study are as follows. The two mountain fronts are different in terms of morphological characteristics, but only some indices and parameters have discriminating value, mainly SL index, drainage basin typology, altitude variability in the frontal zone. Some parameters, such as drainage basin asymmetry and the altitude position of water divide flats, change non-systematically and their interpretation remains ambiguous. Their variability probably reflects local controls. Overall, the results strongly indicate that geomorphic histories of the mountain fronts and overarching controls of their long-term evolution were different. Morphometric properties and landform features, if considered in isolation and without statistical treatment, appear to be ambiguous in interpretation. This applies particularly to drainage basin properties. However, drainage basin clustering using a k-means algorithm helped to discriminate between the fronts and may be recommended for similar studies elsewhere.