From snow depth maps to basin-scale snow water resources modelling

5-6 months, starting february 2027

The spatial distribution of snow and snowmelt is a key driver of streamflow in mountain catchments, yet hydrological models often represent snow cover in a simplified way. To test the added value of process-based, high-resolution snow models for hydrological application, the ongoing PhD project of Elise Navarre is setting up snow cover simulations using the physics-based model FSM2 (Mott et al. 2023, Essery et al. 2025) over headwater catchments in the French Alps. These simulations are intended to provide improved surface water input for subsequent simulations of catchment hydrology.
Evaluating spatially-distributed snow simulations requires spatially distributed snow cover datasets. In particular, photogrammetry from satellite or drone-based imagery (e.g. Deschamps-Berger et al. 2020) can provide valuable information on snow depth distribution at different times during the snow season. Yet, a conversion to snow water equivalent (SWE) is necessary to assess the water resources stored in the snowpack (e.g. Winkler et al. 2021). This conversion requires assumptions on the spatial distribution of snow density.
The goal of this internship is to establish a snow water equivalent product based on satellite imagery (Pléiades) and drone imagery acquired in the Grand Rousses and Aiguilles d’Arves massifs by applying different SWE models, and to estimate the associated uncertainties. The SWE maps will subsequently be used to assess the spatial snow distribution and snowmelt patterns produced by FSM2. The work will comprise three main tasks:
1. Contribution to regular ground-based field campaigns concurrent to drone imagery acquisition to document the evolution of snow water equivalent (SWE) and snow density at different elevations and aspects within the study domain.
2. Use of different existing parametrizations to convert snow depth to SWE maps, evaluation of these parametrizations with the data acquired under 1, estimation of the uncertainty of the SWE maps.
3. Simulation of the seasonal snow cover over the study domain with FSM2 and evaluation of the simulated spatial SWE patterns, focusing on accurate representation of spatial variability induced by complex topography. Suggestion of model improvements if needed.
The results of this internship will be essential for informing the use of FSM2 in hydrological modelling applications, provide key insights into spatio-temporal snow cover variability at the catchment scale, its drivers, and its importance for the catchment-scale water balance.
We are looking for motivated candidates with a strong interest in cryospheric sciences, hydrology, and numerical modelling of physical systems. Candidates should be proficient (or interested to progress) in a programming language (e.g. Python) and GIS tools, and willing to work in a team. Interested students should send a motivation and a CV to apply. Don’t hesitate to reach out if you have questions!

References:
Essery, R., Mazzotti, G., Barr, S., Jonas, T., Quaife, T., and Rutter, N.: A Flexible Snow Model (FSM 2.1.1) including a forest canopy, Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, 2025.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Mott R, Winstral A, Cluzet B, Helbig N, Magnusson J, Mazzotti G, Quéno L, Schirmer M, Webster C and Jonas T.: Operational snow-hydrological modeling for Switzerland. Front. Earth Sci. 11:1228158. https://doi.org/10.3389/feart.2023.1228158, 2023.
Winkler, M., Schellander, H., and Gruber, S.: Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165–1187, https://doi.org/10.5194/hess-25-1165-2021, 2021.