Globally, a large number of glaciers are retreating due to global warming. Along with climate change, glacial melting has been identified as one of the main triggers of landslide activity in high mountain areas. Evaluations of the triggered mechanism alone do not provide comprehensive insight into the overall impact of glacier accumulation and ablation on landslide-induced denudation. To investigate recent trends, we built landslide and glacier datasets for the HMA using a Landsat time-series covering the past 21 years (1998–2018). Landslides that may have been caused by major earthquakes were identified and removed, leaving an inventory that is used to explore changes that may be related to climate change. Our results show a shift in the frequency–area distribution that indicates an increasing trend of large landslides in the HMA over the last decade. A decline in glacier area is associated with the increase in landslide area.
Background: Landslide size distribution is widely found to obey a negative power law with a rollover in the smaller size, and has been exploited by many researchers to inspect landside physics or to assess landslide erosion and landslide hazard. Yet, sample size has effect on the statistics of landslide size even though we manage to avoid complications associated with landslide datasets and statistical treatments. Results: In this paper, a series of stochastic simulations were implemented to explicitly and systematically quantify the effect of sample size. The results show that, the errors of parameters estimated based on small sample size can be considerably large. For a sample size of 100, the relative error of the estimated landslide erosion rate that has a probability of 50 % can approach 100 %. In addition, small sample size also obscures the statistical significance of the variances in parameters between different subsets of the same dataset. Although inconsistency was found regarding how the power exponent varies with rainfall intensity, numerical results suggest that the variance observed in a dataset with a small sample size may be not statistically significant.
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