Surface processes in mountainous proglacial areas: Quantitative assessments and their accuracy

Abstract

This article presents a fundamental review of the most common contemporary methods used for quantitative assessment of geomorphological processes and surface transformation in proglacial areas. Our research is particularly relevant due to the significant climate change, which has taken place since the end of the Little Ice Age: its influence has been widespread in polar and mountainous proglacial regions, thereby resulting in the formation of “young” and highly dynamic proglacial areas. For such territories, of special interest is quantitative evaluation of their terrain transformation under various geomorphic processes. Here we compared different methods for their accuracy and applicability to various exogenic processes and spatial coverage. Detailed attention was given to terrestrial and airborne laser scanning (TLS, ALS), satellite radar interferometry (InSAR), elevation modeling using aerial and satellite images, and 3D surface reconstruction based on drone images (Structure from Motion, SfM). The main advantage of the above-listed methods is that they yield highly accurate quantitative results for areas of varying sizes, from individual points to whole proglacial basins. Their major weakness is the inability to measure and specify the contribution of each process taken alone to the overall surface transformation. To solve this problem, we suggested that all the methods should be combined with the ground-based ones (using benchmarks or sediment traps). As an example, the surface transformation of the buried ice outcrop located in the lower part of the lateral moraine within the Djankuat River catchment area (Central Caucasus) was assessed. Following an extreme event, the volume of the displaced material was 1880 m3 during the 2-day interval (the average displaced layer was 0.47 m), as compared to the displacement of 6330 m3 during the annual interval (the average displacement for the whole area was 1.6 m). This example demonstrates that certain extreme events may determine the proglacial terrain features.

Keywords

• proglacial area
• surface processes
• remote sensing
• quantitative assessment
• drones