Constraining regional glacier reconstructions using past ice thickness of deglaciating areas - a case study in the European Alps - Sommer, Christian; Fürst, Johannes; Huss, Matthias; Braun, Matthias (Cryosphere, 2023)
In order to assess future glacier evolution and meltwater runoff, accurate knowledge on the volume and the ice thickness distribution of glaciers is crucial. However, in situ observations of glacier thickness are sparse in many regions worldwide due to the difficulty of undertaking field surveys. This lack of in situ measurements can be partially overcome by remote-sensing information. Multi-temporal and contemporaneous data on glacier extent and surface elevation provide past information on ice thickness for retreating glaciers in the newly deglacierized regions. However, these observations are concentrated near the glacier snouts, which is disadvantageous because it is known to introduce biases in ice thickness reconstruction approaches. Here, we show a strategy to overcome this generic limitation of so-called retreat thickness observations by applying an empirical relationship between the ice viscosity at locations with in situ observations and observations from digital elevation model (DEM) differencing at the glacier margins. Various datasets from the European Alps are combined to model the ice thickness distribution of Alpine glaciers for two time steps (1970 and 2003) based on the observed thickness in regions uncovered from ice during the study period. Our results show that the average ice thickness would be substantially underestimated (∼40%) when relying solely on thickness observations from previously glacierized areas. Thus, a transferable topography-based viscosity scaling is developed to correct the modelled ice thickness distribution. It is shown that the presented approach is able to reproduce region-wide glacier volumes, although larger uncertainties remain at a local scale, and thus might represent a powerful tool for application in regions with sparse observations.
CRIS-Team Scopus Importer:2024-12-16