Abstract
Abstract. Ground-penetrating radar (GPR) is widely used for determining mountain glacier thickness. However, this method provides thickness data only along the acquisition lines, and therefore interpolation has to be made between them. Depending on the interpolation strategy, calculated ice volumes can differ and can lack an accurate error estimation. Furthermore, glacial basal topography is often characterized by complex geomorphological features, which can be hard to reproduce using classical interpolation methods, especially when the field data are sparse or when the morphological features are too complex. This study investigates the applicability of multiple-point statistics (MPS) simulations to interpolate glacier bedrock topography using GPR measurements. In 2018, a dense GPR data set was acquired on the Tsanfleuron Glacier (Switzerland). These data were used as the source for a bedrock interpolation. The results obtained with the direct-sampling MPS method are compared against those obtained with kriging and sequential Gaussian simulations (SGSs) on both a synthetic data set – with known reference volume and bedrock topography – and the real data underlying the Tsanfleuron Glacier. Using the MPS modeled bedrock, the ice volume for the Scex Rouge and Tsanfleuron glaciers is estimated to be 113.9 ± 1.6 million cubic meters. The direct-sampling approach, unlike the SGS and kriging, allowed not only an accurate volume estimation but also the generation of a set of realistic bedrock simulations. The complex karstic geomorphological features are reproduced and can be used to significantly improve for example the precision of subglacial flow estimation.
Highlights
It is widely accepted that global climatic changes impact future precipitation rates and temperatures
The importance of accurately simulating the roughness of subglacial topography was already highlighted by Goff et al (2014), who used a combination of multi-Gaussian simulations with deterministic trends, but the procedure that we propose with multiple-point statistics (MPS) is simpler to implement
This study presents an example of the benefits of using advanced geostatistical methods for basal topography interpolation and compares three methods: kriging, sequential Gaussian simulations, and multiple-point statistics
Summary
It is widely accepted that global climatic changes impact future precipitation rates and temperatures. In Switzerland, these changes will inevitably induce new stresses on alpine environments and on glacier mass balance (e.g., Haeberli et al, 2007; Beniston, 2012; Huss and Fischer, 2016). In this context, the monitoring of glaciers’ thickness and volume is crucial in order to predict their melt rate and the possible consequences for water resources, sediment production, and slope stabilization. The equipment has the advantage of being light and easy to use in a glacial environment This method only provides thickness data along the acquisition lines, and interpolation methods are needed to estimate the basal topography between sparse survey lines. Depending on the interpolation methods, the basal topography can change significantly and can lead to a wide range of calculated ice volumes
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