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Cable Aerial Based Low-altitude Systems for Snowpack Spatialized Sensing

Project description

Snowpack monitoring for alpine regions

Predicting snowpack variability is key to ensuring economic stability and public safety in alpine and downstream regions. However, current monitoring methods provide limited spatial and temporal coverage. Supported by the Marie Skłodowska-Curie Actions programme, the CABLE-SENS3 project aims to develop a non-invasive monitoring system based on passive RFID technology in order to overcome these limitations. This method enables frequent and wide-area measurements of snowpack characteristics across different elevations. This approach enables the continuous observation of snowpack properties along elevation gradients, complemented by photogrammetric and geophysical measurements. The resulting datasets provide detailed insights into snow dynamics, supporting stakeholders such as hydropower operators and safety services.

Objective

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

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

METEO-FRANCE
Net EU contribution
€ 461 934,30
Address
AVENUE DE PARIS 73
94165 Saint Mande Cedex
France
Region
Ile-de-France Ile-de-France Val-de-Marne
Activity type
Research Organisations
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Total cost
No data

Partners (3)