Glacial lakes are critical indicators of the effects climate change and significant sources of natural hazards, such as Glacial Lake Outburst Floods (GLOFs), cascading events, etc. Monitoring their formation and evolution is essential for understanding cryospheric dynamics and supporting risk management, yet systematic mapping is hindered by the complexity of high-mountain environments. Developing robust, automated methods using remote sensing remains challenging due to rugged topography, snow, ice, and shadows causing misclassification.This paper proposes a multi-sensor methodology for glacial lake detection and monitoring, integrating optical data from Sentinel-2 and Synthetic Aperture Radar (SAR) data from Sentinel-1. The study focuses on the Western Alps using data from 2022 to 2024. The methodology applies optical indices using a double thresholding strategies and tests machine learning algorithms. On the other hand, it investigates the potential of the recently developed OASIS index for SAR-based detection, aiming to overcome cloud cover and illumination limitations inherent in optical imagery.Preliminary results show that optical indices perform well but require dynamic thresholding, as snowmelt and shadows remain major sources of uncertainty. Machine learning approaches demonstrate good potential in mitigating these limitations. The OASIS index (SAR) proves to be a promising complementary tool, especially under cloudy conditions, though still challenged by surface roughness. The integration of optical and radar data significantly increases the robustness of lake detection and reduces temporal gaps in monitoring. This methodology contributes to advancing automated systems for hazard assessment and climate change effects monitoring in alpine regions.
Alpine areas play a major role in the water supply of downstream valleys by releasing water during dry periods. Groundwater storage could significantly influence how changing climatic conditions affect the discharge regime. However, these processes are still not well understood in alpine areas. Studies on how climate change impacts water resources in alpine areas rarely consider the influence of geology explicitly. An integrated surface–subsurface hydrologic model (HydroGeoSphere) was used to highlight the effect of groundwater in buffering future summer low flows and to give new insights into the role of geology in controlling discharge. The spatially explicit model allows the quantification of groundwater storage and its variation in different geological formations of an alpine catchment, under current and future conditions. The results suggest that, under future extreme climate change conditions, the average catchment groundwater storage in Quaternary deposits increases in winter and decreases in summer; the annual mean groundwater storage for the entire catchment decreases due to the decrease in mean annual groundwater recharge; and the total catchment discharge decreases and low-flow period shifts from winter to late summer by the end of this century. Future summer low flows will remain higher than the current winter low flows but will approach them closely after very dry summers. Proportionally, the decrease in groundwater storage will be less severe than the decrease in discharge. Finally, a sensitivity analysis illustrates that both Quaternary deposits (especially moraine and talus units) and bedrock play an important role in supporting discharge during low-flow periods.
