Can Saharan dust deposition impact snowpack stability in the French Alps?

Dick, Oscar; Viallon-Galinier, Léo; Tuzet, François; Hagenmuller, Pascal; Fructus, Mathieu; Reuter, Benjamin; Lafaysse, Matthieu; Dumont, Marie

doi:10.5194/tc-17-1755-2023

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…The month of February also saw a large number of wet label samples. This can be explained by the mild weather in February 2021 [66], and the presence of Saharan dust, which accelerates the warming of the snowpack [67]. This results in a classifier that performs well during this period of the year, but is less efficient in the rest of the year.…”

Section: Dataset Creationmentioning

confidence: 99%

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Gallet,

Atto,

Karbou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this paper, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using Synthetic Aperture Radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a Digital Elevation Model (DEM). The labelling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over thirteen combinations of labelled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, Fractional Snow Cover (FSC) and SAR Wet Snow (SWS). We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season.

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Section: Dataset Creationmentioning

confidence: 99%

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Gallet,

Atto,

Karbou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…As only difference, the snow metamorphism options based on Flanner and Zehnder (2006), which is not yet available in the standalone version of Crocus, were replaced by an improved metamorphism parametrization recently developed at MétéoFrance ('B21', unpublished but used e.g. in Dick et al, 2023). The Crocus options used for these runs are listed in the Appendix A2.…”

Section: Overview Of Fsmcro Simulationsmentioning

confidence: 99%

Exploring the potential of forest snow modelling at the tree and snowpack layer scale

Mazzotti,

Nousu,

Vionnet

et al. 2023

Preprint

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Abstract. Boreal and subalpine forests host seasonal snow for multiple months per year, however snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, tree-scale process resolving canopy representations so far only exist in a few snow-hydrological models. A framework that enables layer and microstructure resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modelling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2), and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and subalpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations, and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tool in interdisciplinary studies.

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Spatial variability of Saharan dust deposition revealed through a citizen science campaign

Dumont

Gascoin

Réveillet

et al. 2023

Earth Syst. Sci. Data

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Abstract. Saharan dust outbreaks have profound effects on ecosystems, climate, human health, and the cryosphere in Europe. However, the spatial deposition pattern of Saharan dust is poorly known due to a sparse network of ground measurements. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This somewhat improvised campaign triggered wide interest since 152 samples were collected from the snow in the Pyrenees, the French Alps, and the Swiss Alps in less than 4 weeks. Among the 152 samples, 113 in total could be analysed, corresponding to 70 different locations. The analysis of the samples showed a large variability in the dust properties and amount. We found a decrease in the deposited mass and particle sizes with distance from the source along the transport path. This spatial trend was also evident in the elemental composition of the dust as the iron mass fraction decreased from 11 % in the Pyrenees to 2 % in the Swiss Alps. At the local scale, we found a higher dust mass on south-facing slopes, in agreement with estimates from high-resolution remote sensing data. This unique dataset, which resulted from the collaboration of several research laboratories and citizens, is provided as an open dataset to benefit a large community and to enable further scientific investigations. Data presented in this study are available at https://doi.org/10.5281/zenodo.7969515 (Dumont et al., 2022a).

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Can Saharan dust deposition impact snowpack stability in the French Alps?

Cited by 4 publications

References 51 publications

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Exploring the potential of forest snow modelling at the tree and snowpack layer scale

Spatial variability of Saharan dust deposition revealed through a citizen science campaign

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