IDENTIFYING MORAINE SURFACES WITH SIMILAR HISTORIES USING LICHEN SIZE DISTRIBUTIONS AND THE U2 STATISTIC, SOUTHEAST ICELAND
Moraine ridges are commonly used to identify past glacier ice margins and so infer glacier mass balance changes in response to climatic variability. However, differences in the form of past ice margins and post-depositional modification of moraine surfaces can complicate these geomorphic records. As a result, simple relationships, such as distance from current ice margin, or linear alignments, may not necessarily indicate moraines deposited contemporaneously. These disturbances can also modify the size distribution of lichen populations, providing a distinctive signature for surfaces with similar histories and a means of identifying contemporaneous moraine surfaces. In this paper, statistical analysis of lichen size distributions is used to identify moraine surfaces with similar histories from complex suites of Little Ice Age moraine fragments in the proglacial areas of Skálafellsjökull (including Sultartungnajökull) and Heinabergsjökull, southeast Iceland. The analysis is based on a novel use of the goodness-of-fit statistic, Watson's U2 which provides a measure of ‘closeness’ between two sample distributions. Moraine fragments with similar histories are identified using cluster analysis of the U2 closeness values. The spatial pattern of the clustered moraines suggests three distinct phases of moraine deposition at Skálafellsjökull and Heinabergsjökull, four phases at Sultartungnajökull and a digitate planform margin at Heinabergsjökull. These spatial patterns are corroborated with tephrochronology. The success of the U2 statistical analysis in identifying surfaces with similar histories using lichen size distributions suggests that the technique may be useful in augmenting lichenometric surface dating as well as differentiating between other surfaces that support lichen populations, such as rock avalanche deposits.
Document Type: Research Article
Affiliations: 1: Department of Geography, University of Otago, Dunedin, New Zealand 2: Institute of Geography, University of Edinburgh, Edinburgh, UK 3: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada
Publication date: 2008-06-01