Numerical modeling of a gravity-driven instability of a cold hanging glacier: reanalysis of the 1895 break-off of Altelsgletscher, Switzerland

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The Altels hanging glacier in Switzerland broke off on 11 September 1895. The ice volume of this catastrophic rupture was estimated as 4 × 10 ⁶ m ³, the largest icefall event ever observed in the Alps. However, the causes of this collapse are not entirely clear. Based on previous studies, we reanalyzed this break-off event, with the help of a new numerical model, initially developed by Faillettaz and others (2010) for gravity-driven instabilities. The simulations indicate that a break-off event is only possible when the basal friction at the bedrock is reduced in a restricted area, possibly induced by the storage of infiltrated water within the glacier. Further, our simulations reveal a two-step behavior: (1) a first quiescent phase, without visible changes, with a duration depending on the rate of change in basal friction; (2) an active phase with a rapid increase of basal motion over a few days. The general lesson obtained from the comparison between the simulations and available observations is that detectable precursors (crevasse formation and velocity increase) of the destabilization process of a hanging glacier, resulting from a progressive warming of the ice/bed interface towards a temperate regime, will appear only a few days prior to the break-off.

Related collections

Most cited references 18

Record: found
Abstract: found
Article: found

Is Open Access

Power-law distributions in empirical data

Cosma Rohilla Shalizi, Aaron Clauset, M. Newman (2007)

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.