Alpine snowpack dynamics are crucial to hydropower generation, irrigation, winter tourism, and avalanche and flood forecasting. The ability to predict snowpack fluctuations is essential for ensuring economic stability and public safety in both alpine and downstream regions. However, current snowpack monitoring techniques are labor-intensive and provide only sparse, large-scale data, which limits the capacity to capture rapid and localized changes. This poses a significant challenge in addressing key operational needs. Cryospheric scientists have therefore identified the collection of spatiotemporal data on snowpack variability, particularly along elevation gradients, as a top research priority. The overall aim of the CABLE-SENS3 project is thus to develop an innovative snowpack monitoring system that addresses these challenges. The project proposes to use passive RFID tags, combined with an aerial RFID reader mounted on a cable car, to continuously monitor snowpack properties across large elevation gradients. This approach offers a breakthrough by merging high-resolution spatial and temporal data—something current methods cannot achieve simultaneously. Additionally, the proposed system is non-invasive, overcoming the limitations of traditional methods that rely on low-frequency or intrusive measurements. Incorporating technologies such as photogrammetry and microwave geophysics will enable the system to measure variables like snow water equivalent (SWE), liquid water content (LWC), and snow depth. By deploying RFID tags across large, often inaccessible areas, and conducting frequent aerial surveys, the project will generate unprecedented spatiotemporal data on snowpack dynamics. Such data will be invaluable to stakeholders such as hydropower companies, ski resorts, and avalanche safety services, who increasingly depend on data-driven decision-making. Moreover, the project will provide critical data to address many scientific questions related to the impacts of cli

Snow is a pillar of the Earth’s climate system, affecting all its components with critical impacts for Nature and human societies. Perennial snow evolves to firn and ice, providing unique records of the past climate. Yet today no snow model adequately simulates relevant snow variables worldwide, not to mention their inability to represent firn processes and snow/permafrost interactions. I argue that this is because current models focus on a limited number of physical processes and none suitably consider snow microstructure. IVORI’s goal is to build a microstructure-based model encompassing all the relevant snow and firn physical variables. Drawing on advanced observations of snow and firn, the proposal has three objectives: (1) Understand the role of water vapour transport in snow and its subsequent impacts on the ground thermal regime governing permafrost evolution; (2) Understand how initial changes in surface snow microstructure are transferred deeper into the firn and affect ice core records; (3) Determine the contributions of snow-climate feedbacks, triggered by changes in the albedo and insulating capacity of snow to the past and future of snow cover and ground temperature. To this aim, I will build a microstructure-based model, with a novel physics core, unifying the evolution of snow and firn. IVORI will also deliver unprecedented season-long observations of snow microstructure in the Arctic, Alps and Antarctica using X-ray tomography. These observations will significantly advance our understanding of the physical processes involved and be used for a thorough evaluation of the model. The model will provide a reliable assessment of snow-climate feedbacks in a changing climate and a rigorous appraisal of the modelling uncertainties. When completed, this work will pave the way for crucial advances in our understanding of glaciers, ice sheets and past climate through ice core records, with many fallouts for sea ice and permafrost evolution.