Spatial and temporal variations in snow conditions are key drivers of alpine ecosystem dynamics. In mountain environments, snow controls both the length of the growing season and the availability of water during summer, thereby shaping the structure and functioning of alpine grasslands. As climate change alters snow regimes across Europe’s mountains, ecologists anticipate profound impacts on alpine vegetation. Warmer and drier conditions are expected to shift the main constraint on alpine plant communities from temperature limitation to water limitation, potentially increasing the importance of snow as a regulator of soil moisture and plant productivity.

This PhD proposal aims to integrate fine-scale, multi-temporal information on snow cover and snowpack properties to better understand and predict vegetation responses in two contrasting study areas: the Swiss National Park (SNP, Switzerland) and the Pyrenees National Park (PNP, France). In the Pyrenees, the snow cover is presumably a major determinant of water availability for alpine grasslands, while in the SNP the transition toward water limitation is expected to be only emerging. Studying these two systems together provides a unique opportunity to anticipate ecological thresholds and shifts in vegetation functioning under climate change. 

In addition, the SNP and PNP offer outstanding opportunities from both a scientific and logistical standpoint: the Swiss National Park benefits from century-long Braun-Blanquet permanent-plot surveys (some plots established as early as 1917) that provide invaluable time series of plant community change while the PNP also supports long-term ecological monitoring along a gradient of climatic and altitudinal settings. Both parks have infrastructures, accessible field stations, and management collaboration that make repeated field campaigns and integration of remote sensing and ground validation feasible under realistic logistic conditions, thereby ensuring that the proposed snow–vegetation linkage work is both ambitious and practically achievable. 

The Centre d’Etudes Spatiales de la BIOsphère (CESBIO, Toulouse) and the Laboratoire d’Écologie alpine (LECA, Grenoble) have developed a range of methods to characterize snowpack dynamics and vegetation trends in mountain environments:

● Snow cover at 20 m resolution every 5 days (in the absence of clouds), produced in real time since 2016 from Sentinel-2 images (Gascoin et al., 2019). These datasets allow derivation of ecological indicators such as annual snow cover duration or snow melt-out date (SMOD), which have already been linked to vegetation productivity .

● Historical reconstruction is possible back to 1984 using Landsat and SPOT archives (Barrou Dumont et al., 2024; Bayle et al., 2025). These reconstructions are already done in the French mountains but can be extended to the SNP area.

● High-resolution snow depth mapping (4 m) from Pléiades or CO3D images, once or twice a year (Marti et al., 2016). Snow depth, unlike SMOD, directly relates to the amount of water stored in the snowpack and thus to subsequent soil water availability for vegetation.

● A novel snow depth retrieval method based on ICESat-2 laser data was developed at CESBIO to estimate snow depth on a larger scale and under forest cover, which is not possible with Pléiades (Deschamps-Berger et al., 2023).

By combining these datasets within the snow data assimilation framework developed at CESBIO (Alonso-González et al., 2022; Sourp et al., 2025), this project will deliver a comprehensive collection of snow indicators, including snow depth and snow water equivalent, that go beyond simple melt-out dates. These indicators will be explicitly linked with alpine vegetation productivity metrics (Bayle et al., 2024) and exceptional time series of vegetation surveys, enabling a better understanding of how snow controls plant community composition, productivity, and resilience under climate change. In addition, the data produced in this thesis project will represent a valuable resource for other researchers and future students and will provide the PhD candidate with concrete opportunities to engage in interdisciplinary collaborations beyond the core scope of the thesis.

The PhD will be supervised by S. Gascoin, A. Bayle and M. Stoffel (U. Geneva). M. Stoffel is a member of the scientific committee of the SNP and S. Gascoin (CESBIO) is a member of the scientific committee of the PNP, which guarantees a smooth collaboration with the parks staff.

References

Alonso-González https://doi.org/10.5194/gmd-15-9127-2022

Barrou Dumont https://doi.org/10.5194/egusphere-2024-3505

Bayle https://doi.org/10.1111/ecog.07394

Bayle https://doi.org/10.1038/s41597-025-05044-2

Deschamps-Berger https://doi.org/10.5194/tc-17-2779-2023

Gascoin https://doi.org/10.5194/essd-11-493-2019

Marti https://doi.org/10.5194/tc-10-1361-2016

Sourp https://doi.org/10.5194/hess-29-597-2025

=================

For more Information about the topics and the co-financial partner (found by the lab!); contact Directeur de thèse - simon.gascoin@utoulouse.fr

Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online  before March 13th, 2026 Midnight Paris time!