Abstract ID: 3.13084 | Accepted as Talk | Talk | TBA | TBA

Glaciers are crucial components of the Earth’s climate system and serve as indicators of climate change. Their substantial mass loss due to global warming significantly contributes to sea-level rise and impacts regional hydrology, downstream ecosystems and settlements. Despite considerable advancements in observational and modelling techniques, accurate quantification of glacier responses to climate change and prediction of their future behaviour remains challenging, particularly in regions characterized by rapidly changing glaciers and complex topography. Remote sensing data is widely used for direct glacial mass change estimation and as input for mountain glacier models, offering global observations with relatively regular time steps and can be helpful for the representation of spatial variability within individual glaciers due to multiple observational points. However, remote sensing-derived datasets often suffer from scale mismatches, uncertainties, and significant errors in regions with steep topography and spatially heterogeneous glacial dynamics, limiting their direct use for model forcing and validation. Therefore, one of the key limitations in this field remains the availability of high-resolution regional datasets that can be used for glacial models. In this study, we investigate the application of remote sensing data downscaling techniques to improve spatial and temporal resolution of glacial mass balance estimates. We focus on the integration of relatively high-resolution surface elevation changes derived from satellite altimetry with coarse-resolution mass changes inferred from satellite gravimetry data to localize mass changes. This study mainly focuses on the glaciers of the Patagonia region. This region, characterized by rapid glacier retreat and complex climatic influences, serves as an ideal case study for integrating multiple satellite datasets and regional models. Preliminary results highlight the potential of enhanced data integration techniques to resolve sub-regional mass changes and improve model-data comparisons in glaciological studies. The potential outcomes of this work aim to benefit the glacial model forcing, parameterization, and data assimilation by providing a refined glacial mass balance dataset and a transferable framework for application in other regions.