Combined Use of Remote Sensing Data and Melting Models for Estimating Glacier Melt Contributions to Runoff

In the context of climate change, the declining contribution of snowmelt to runoff underscores the need for precise quantification of glacier meltwater contributions. Accurate differentiation between snowmelt and ice melt is crucial but challenging, requiring detailed knowledge of glacier surface land cover. High-resolution optical remote sensing data from platforms like Landsat and Sentinel-2 provide a valuable tool for assessing glacier surface cover, leveraging their rich spectral information to distinguish snow from ice effectively.

The inherent limitation of cloud occlusion in optical imagery can be mitigated by integrating high-resolution datasets with daily low-resolution observations through gap-filling techniques. These land cover maps can be used as input for a range of melting models, from simple temperature-index models to more complex physically-based models.

A preliminary study was conducted on the Hintereisferner glacier in Austria, a well-documented site with extensive historical data. The study compared glacier melt estimates derived from gap-filled high-resolution satellite data, orthorectified and classified webcam imagery, and terrestrial laser scanner (TLS) data (Voordendag et al. 2023). Results revealed strong agreement between the satellite-based melt maps and those derived from webcam and TLS measurements, demonstrating the potential of this approach.

Future applications will focus on reference glaciers in the Andes, where glacier melt contributions to downstream water resources are more significant than in Alpine catchments. The methodology aims to enhance our understanding of glacier dynamics and support water resource management in regions heavily reliant on glacier-fed runoff.

This work has been done in the context of the project SNOWCOP. This project has received funding from the European Union’s Horizon Research and Innovation Actions programme under Grant Agreement 10180133.

 

References:

Voordendag, A., Goger, B., Klug, C., Prinz, R., Rutzinger, M., Sauter, T., & Kaser, G. (2023). Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria). Frontiers in Earth Science, 11, 1085416.