Articles | Volume 19, issue 7
https://doi.org/10.5194/tc-19-2407-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-2407-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2025
Research article |
| 04 Jul 2025
| 04 Jul 2025
Trends in the annual snow melt-out day over the French Alps and Pyrenees from 38 years of high-resolution satellite data (1986–2023)
Zacharie Barrou Dumont
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère, CESBIO, CNES/CNRS/IRD/UT3 Paul Sabatier, Toulouse, France
Magellium, Ramonville, France
Simon Gascoin
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère, CESBIO, CNES/CNRS/IRD/UT3 Paul Sabatier, Toulouse, France
Jordi Inglada
Centre d'Etudes Spatiales de la Biosphère, CESBIO, CNES/CNRS/IRD/UT3 Paul Sabatier, Toulouse, France
Andreas Dietz
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, 82234 Wessling, Germany
Jonas Köhler
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, 82234 Wessling, Germany
Matthieu Lafaysse
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etude de la Neige, Grenoble, France
Diego Monteiro
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etude de la Neige, Grenoble, France
Carlo Carmagnola
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etude de la Neige, Grenoble, France
Arthur Bayle
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, Laboratoire d'Ecologie Alpine (LECA), 38000 Grenoble, France
Jean-Pierre Dedieu
Institute of Environmental Geosciences (IGE), Université Grenoble Alpes/CNRS/Grenoble INP/INRAE/IRD, Grenoble, France
Olivier Hagolle
Centre d'Etudes Spatiales de la Biosphère, CESBIO, CNES/CNRS/IRD/UT3 Paul Sabatier, Toulouse, France
Philippe Choler
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, Laboratoire d'Ecologie Alpine (LECA), 38000 Grenoble, France
Related authors
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Audrey Goutard, Marion Réveillet, Fanny Brun, Delphine Six, Kevin Fourteau, Charles Amory, Xavier Fettweis, Mathieu Fructus, Arbindra Khadka, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-2947, https://doi.org/10.5194/egusphere-2025-2947, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
A new scheme has been developed in the SURFEX/ISBA-Crocus model, to consider the impact of liquid water dynamics on bare ice, including albedo feedback and refreezing. When applied to the Mera Glacier in Nepal, the model reveals strong seasonal effects on the energy and mass balance, with increased melting in dry seasons and significant refreezing during the monsoon. This development improves mass balance modeling under increasing rainfall and bare ice exposure due to climate warming.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2347, https://doi.org/10.5194/egusphere-2025-2347, 2025
Short summary
Short summary
Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1741, https://doi.org/10.5194/egusphere-2025-1741, 2025
Short summary
Short summary
Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Atmos. Meas. Tech., 18, 1731–1755, https://doi.org/10.5194/amt-18-1731-2025, https://doi.org/10.5194/amt-18-1731-2025, 2025
Short summary
Short summary
This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
Andreas J. Dietz and Sebastian Roessler
EGUsphere, https://doi.org/10.5194/egusphere-2025-382, https://doi.org/10.5194/egusphere-2025-382, 2025
Short summary
Short summary
The "Global SnowPack" product of the German Aerospace Center (DLR) contains binary information about the presence or absence of snow on a global scale since the year 2000. Now incorporating new input datasets, it was possible to increase the spatial resolution to 370 m. The detailed accuracy assessment proves the feasibility of the applied methods to remove data gaps caused by clouds and polar darkness.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba
Hydrol. Earth Syst. Sci., 29, 597–611, https://doi.org/10.5194/hess-29-597-2025, https://doi.org/10.5194/hess-29-597-2025, 2025
Short summary
Short summary
Accurate knowledge of the spatial distribution of snow masses across landscapes is important for water management in mountain catchments. We present a new tool for estimating snow water resources without ground measurements. We evaluate the output of this tool using accurate airborne measurements in the Sierra Nevada and find that it provides realistic estimates of snow mass and snow depth at the catchment scale.
Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath
Earth Syst. Sci. Data, 17, 65–78, https://doi.org/10.5194/essd-17-65-2025, https://doi.org/10.5194/essd-17-65-2025, 2025
Short summary
Short summary
Glacier calving front positions are important for understanding glacier dynamics and constraining ice modelling. We apply a deep-learning framework to multi-spectral Landsat imagery to create a calving front record for 42 key outlet glaciers of the Antarctic Peninsula Ice Sheet. The resulting data product includes 4817 calving front locations from 2013 to 2023 and achieves sub-seasonal temporal resolution.
Thibault Xavier, Laurent Orgogozo, Anatoly S. Prokushkin, Esteban Alonso-González, Simon Gascoin, and Oleg S. Pokrovsky
The Cryosphere, 18, 5865–5885, https://doi.org/10.5194/tc-18-5865-2024, https://doi.org/10.5194/tc-18-5865-2024, 2024
Short summary
Short summary
Permafrost (permanently frozen soil at depth) is thawing as a result of climate change. However, estimating its future degradation is particularly challenging due to the complex multi-physical processes involved. In this work, we designed and ran numerical simulations for months on a supercomputer to quantify the impact of climate change in a forested valley of central Siberia. There, climate change could increase the thickness of the seasonally thawed soil layer in summer by up to 65 % by 2100.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
Short summary
Short summary
Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Lahoucine Hanich, Ouiaam Lahnik, Simon Gascoin, Adnane Chakir, and Vincent Simonneaux
Proc. IAHS, 385, 387–391, https://doi.org/10.5194/piahs-385-387-2024, https://doi.org/10.5194/piahs-385-387-2024, 2024
Short summary
Short summary
Using a dataset measured with the eddy covariance system (EC) for a period from September 2020 to January 2021 at the Tazaghart plateau, located in the High Atlas of Marrakech, the sublimation was estimated. The average daily sublimation rate measured was 0.41 mm per day. Measured sublimation accounted for 42 % and 40 % of snow ablation, based on the energy and water balances, respectively.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
Short summary
Short summary
The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Eliot Sicaud, Daniel Fortier, Jean-Pierre Dedieu, and Jan Franssen
Hydrol. Earth Syst. Sci., 28, 65–86, https://doi.org/10.5194/hess-28-65-2024, https://doi.org/10.5194/hess-28-65-2024, 2024
Short summary
Short summary
For vast northern watersheds, hydrological data are often sparse and incomplete. Our study used remote sensing and clustering to produce classifications of the George River watershed (GRW). Results show two types of subwatersheds with different hydrological behaviors. The GRW experienced a homogenization of subwatershed types likely due to an increase in vegetation productivity, which could explain the measured decline of 1 % (~0.16 km3 y−1) in the George River’s discharge since the mid-1970s.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Philippe Choler
Biogeosciences, 20, 4259–4272, https://doi.org/10.5194/bg-20-4259-2023, https://doi.org/10.5194/bg-20-4259-2023, 2023
Short summary
Short summary
The year 2022 was unique in that the summer heat wave and drought led to a widespread reduction in vegetation growth at high elevation in the European Alps. This impact was unprecedented in the southwestern, warm, and dry part of the Alps. Over the last 2 decades, water has become a co-dominant control of vegetation activity in areas that were, so far, primarily controlled by temperature, and the growth of mountain grasslands has become increasingly sensitive to moisture availability.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
Short summary
Short summary
Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
Short summary
Short summary
Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. 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 campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
Short summary
Short summary
The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Arthur Bayle, Bradley Z. Carlson, Anaïs Zimmer, Sophie Vallée, Antoine Rabatel, Edoardo Cremonese, Gianluca Filippa, Cédric Dentant, Christophe Randin, Andrea Mainetti, Erwan Roussel, Simon Gascoin, Dov Corenblit, and Philippe Choler
Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, https://doi.org/10.5194/bg-20-1649-2023, 2023
Short summary
Short summary
Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. We used remote sensing approaches to study early succession dynamics as it allows to analyze the deglaciation, colonization, and vegetation growth within a single framework. We found that the heterogeneity of early succession dynamics is deterministic and can be explained well by local environmental context. This work has been done by an international consortium.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
Short summary
Short summary
Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
Short summary
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Maximillian Van Wyk de Vries, Shashank Bhushan, Mylène Jacquemart, César Deschamps-Berger, Etienne Berthier, Simon Gascoin, David E. Shean, Dan H. Shugar, and Andreas Kääb
Nat. Hazards Earth Syst. Sci., 22, 3309–3327, https://doi.org/10.5194/nhess-22-3309-2022, https://doi.org/10.5194/nhess-22-3309-2022, 2022
Short summary
Short summary
On 7 February 2021, a large rock–ice avalanche occurred in Chamoli, Indian Himalaya. The resulting debris flow swept down the nearby valley, leaving over 200 people dead or missing. We use a range of satellite datasets to investigate how the collapse area changed prior to collapse. We show that signs of instability were visible as early 5 years prior to collapse. However, it would likely not have been possible to predict the timing of the event from current satellite datasets.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
Short summary
Short summary
We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
Short summary
Short summary
Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
Short summary
Short summary
This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Mariel C. Dirscherl, Andreas J. Dietz, and Claudia Kuenzer
The Cryosphere, 15, 5205–5226, https://doi.org/10.5194/tc-15-5205-2021, https://doi.org/10.5194/tc-15-5205-2021, 2021
Short summary
Short summary
We provide novel insight into the temporal evolution of supraglacial lakes across six major Antarctic ice shelves in 2015–2021. For Antarctic Peninsula ice shelves, we observe extensive meltwater ponding during the 2019–2020 and 2020–2021 summers. Over East Antarctica, lakes were widespread during 2016–2019 and at a minimum in 2020–2021. We investigate environmental controls, revealing lake ponding to be coupled to atmospheric modes, the near-surface climate and the local glaciological setting.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, https://doi.org/10.5194/npg-28-467-2021, 2021
Short summary
Short summary
Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
Short summary
Short summary
Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Celia A. Baumhoer, Andreas J. Dietz, Christof Kneisel, Heiko Paeth, and Claudia Kuenzer
The Cryosphere, 15, 2357–2381, https://doi.org/10.5194/tc-15-2357-2021, https://doi.org/10.5194/tc-15-2357-2021, 2021
Short summary
Short summary
We present a record of circum-Antarctic glacier and ice shelf front change over the last two decades in combination with potential environmental variables forcing frontal retreat. Along the Antarctic coastline, glacier and ice shelf front retreat dominated between 1997–2008 and advance between 2009–2018. Decreasing sea ice days, intense snowmelt, weakening easterly winds, and relative changes in sea surface temperature were identified as enabling factors for glacier and ice shelf front retreat.
Andreas Kääb, Mylène Jacquemart, Adrien Gilbert, Silvan Leinss, Luc Girod, Christian Huggel, Daniel Falaschi, Felipe Ugalde, Dmitry Petrakov, Sergey Chernomorets, Mikhail Dokukin, Frank Paul, Simon Gascoin, Etienne Berthier, and Jeffrey S. Kargel
The Cryosphere, 15, 1751–1785, https://doi.org/10.5194/tc-15-1751-2021, https://doi.org/10.5194/tc-15-1751-2021, 2021
Short summary
Short summary
Hardly recognized so far, giant catastrophic detachments of glaciers are a rare but great potential for loss of lives and massive damage in mountain regions. Several of the events compiled in our study involve volumes (up to 100 million m3 and more), avalanche speeds (up to 300 km/h), and reaches (tens of kilometres) that are hard to imagine. We show that current climate change is able to enhance associated hazards. For the first time, we elaborate a set of factors that could cause these events.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
Short summary
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
Short summary
Short summary
Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
El Mahdi El Khalki, Yves Tramblay, Christian Massari, Luca Brocca, Vincent Simonneaux, Simon Gascoin, and Mohamed El Mehdi Saidi
Nat. Hazards Earth Syst. Sci., 20, 2591–2607, https://doi.org/10.5194/nhess-20-2591-2020, https://doi.org/10.5194/nhess-20-2591-2020, 2020
Short summary
Short summary
In North Africa, the vulnerability to floods is high, and there is a need to improve the flood-forecasting systems. Remote-sensing and reanalysis data can palliate the lack of in situ measurements, in particular for soil moisture, which is a crucial parameter to consider when modeling floods. In this study we provide an evaluation of recent globally available soil moisture products for flood modeling in Morocco.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
Short summary
Short summary
We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Patrick Le Moigne, François Besson, Eric Martin, Julien Boé, Aaron Boone, Bertrand Decharme, Pierre Etchevers, Stéphanie Faroux, Florence Habets, Matthieu Lafaysse, Delphine Leroux, and Fabienne Rousset-Regimbeau
Geosci. Model Dev., 13, 3925–3946, https://doi.org/10.5194/gmd-13-3925-2020, https://doi.org/10.5194/gmd-13-3925-2020, 2020
Short summary
Short summary
The study describes how a hydrometeorological model, operational at Météo-France, has been improved. Particular emphasis is placed on the impact of climatic data, surface, and soil parametrizations on the model results. Model simulations and evaluations carried out on a variety of measurements of river flows and snow depths are presented. All improvements in climate, surface data, and model physics have a positive impact on system performance.
Cited articles
Adler, C., Wester, P., Bhatt, I., Huggel, C., Insarov, G. E., Morecroft, M. D., Muccione, V., and Prakash, A.: Cross-Chapter Paper 5: Mountains, in: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009325844.022, pp. 2273–2318, 2022. a
Alonso-González, E., Ilzarbe-Senosiain, I., Lopez-Moreno, J. I., Lucas-Borja, M. E., Vicente-Serrano, S. M., Beguería, S., and Gascoin, S.: The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014), Environ. Res. Lett., 19, 064058, https://doi.org/10.1088/1748-9326/ad4e4c, 2024. a
Barrou Dumont, Z. and Gascoin, S.: Snow melt out day in the French Alps and Pyrenees from SPOT, Landsat and Sentinel-2 data, Version v2, Zenodo [data set], https://doi.org/10.5281/zenodo.15063934, 2024. a
Barrou Dumont, Z., Gascoin, S., Hagolle, O., Ablain, M., Jugier, R., Salgues, G., Marti, F., Dupuis, A., Dumont, M., and Morin, S.: Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service, The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, 2021. a, b
Barrou Dumont, Z., Gascoin, S., and Inglada, J.: Contribution de SPOT World Heritage aux séries temporelles d'observation satellitaires des montagnes françaises, Revue Française de Photogrammétrie et de Télédétection, 225, 1–8, https://doi.org/10.52638/rfpt.2023.623, 2023. a, b
Bayle, A., Carlson, B. Z., Zimmer, A., Vallée, S., Rabatel, A., Cremonese, E., Filippa, G., Dentant, C., Randin, C., Mainetti, A., Roussel, E., Gascoin, S., Corenblit, D., and Choler, P.: Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields, Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, 2023. a
Bayle, A., Carlson, B. Z., Nicoud, B., Francon, L., Corona, C., Lavorel, S., and Choler, P.: Uncovering the distribution and limiting factors of Ericaceae-dominated shrublands in the French Alps, Frontiers of Biogeography, 16, 1, https://doi.org/10.21425/F5FBG61746, 2024a. a
Bayle, A., Gascoin, S., Berner, L. T., and Choler, P.: Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations, Ecography, 2024, e07394, https://doi.org/10.1111/ecog.07394, 2024b. a, b
Beaumet, J., Ménégoz, M., Morin, S., Gallée, H., Fettweis, X., Six, D., Vincent, C., Wilhelm, B., and Anquetin, S.: Twentieth century temperature and snow cover changes in the French Alps, Reg. Environ. Change, 21, 114, https://doi.org/10.1007/s10113-021-01830-x, 2021. a, b
Blöschl, G.: Scaling issues in snow hydrology, Hydrol. Process., 13, 2149–2175, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8, 1999. a
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating snow-cover trends from space, Nat. Clim. Change, 8, 924–928, https://doi.org/10.1038/s41558-018-0318-3, publisher: Nature Publishing Group, 2018. a
Böhner, J. and Antonić, O.: Chapter 8 Land-Surface Parameters Specific to Topo-Climatology, in: Developments in Soil Science, edited by: Hengl, T. and Reuter, H. I., vol. 33 of Geomorphometry, Elsevier, https://doi.org/10.1016/S0166-2481(08)00008-1, pp. 195–226, 2009. a
Carlson, B. Z., Corona, M. C., Dentant, C., Bonet, R., Thuiller, W., and Choler, P.: Observed long-term greening of alpine vegetation—a case study in the French Alps, Environ. Res. Lett., 12, 114006, https://doi.org/10.1088/1748-9326/aa84bd, 2017. a
Choler, P.: Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration, Biogeosciences, 12, 3885–3897, https://doi.org/10.5194/bg-12-3885-2015, 2015. a
Choler, P.: Above-treeline ecosystems facing drought: lessons from the 2022 European summer heat wave, Biogeosciences, 20, 4259–4272, https://doi.org/10.5194/bg-20-4259-2023, 2023. a
Choler, P., Bayle, A., Fort, N., and Gascoin, S.: Waning snowfields have transformed into hotspots of greening within the alpine zone, Nat. Clim. Change, 15, 80–85, https://doi.org/10.1038/s41558-024-02177-x, 2024. a, b
Copernicus Climate Change Service (C3S): European State of the Climate 2023, Copernicus Climate Change Service, https://doi.org/10.24381/BS9V-8C66, 2024. a
Cristea, N. C., Breckheimer, I., Raleigh, M. S., HilleRisLambers, J., and Lundquist, J. D.: An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery, Water Resour. Res., 53, 6802–6820, https://doi.org/10.1002/2017WR020799, 2017. a
Dedieu, J. P., Lessard-Fontaine, A., Ravazzani, G., Cremonese, E., Shalpykova, G., and Beniston, M.: Shifting mountain snow patterns in a changing climate from remote sensing retrieval, Sci. Total Environ., 493, 1267–1279, https://doi.org/10.1016/j.scitotenv.2014.04.078, 2014. a
Dedieu, J.-P., Carlson, B. Z., Bigot, S., Sirguey, P., Vionnet, V., and Choler, P.: On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat, Remote Sens.-Basel, 8, 481, https://doi.org/10.3390/rs8060481, 2016. a
Dietz, A. J., Wohner, C., and Kuenzer, C.: European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products, Remote Sens.-Basel, 4, 2432–2454, https://doi.org/10.3390/rs4082432, 2012. a
Dietz, A. J., Kuenzer, C., and Dech, S.: Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent, Remote Sens. Lett., 6, 844–853, https://doi.org/10.1080/2150704X.2015.1084551, 2015. a
Durand, M., Molotch, N. P., and Margulis, S. A.: Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction, Remote Sens. Environ., 112, 1212–1225, https://doi.org/10.1016/j.rse.2007.08.010, 2008. a
Durand, Y., Giraud, G., Brun, E., Mérindol, L., and Martin, E.: A computer-based system simulating snowpack structures as a tool for regional avalanche forecasting, J. Glaciol., 45, 469–484, https://doi.org/10.3189/S0022143000001337, 1999. a, b
Durand, Y., Giraud, G., Laternser, M., Etchevers, P., Mérindol, L., and Lesaffre, B.: Reanalysis of 47 Years of Climate in the French Alps (1958–2005): Climatology and Trends for Snow Cover, J. Appl. Meteorol. Clim., 48, 2487–2512, https://doi.org/10.1175/2009JAMC1810.1, 2009. a, b, c
Edwards, A. C., Scalenghe, R., and Freppaz, M.: Changes in the seasonal snow cover of alpine regions and its effect on soil processes: A review, Quatern. Int., 162–163, 172–181, https://doi.org/10.1016/j.quaint.2006.10.027, 2007. a
EEA: Tree Cover Density 2015 (raster 20 m), Europe, 3-yearly, Mar. 2018, EEA [data set], https://doi.org/10.2909/8bfbda74-7b62-4659-96dd-86600ea425405a2, 2018. a
EEA: EU-Hydro River Network Database 2006-2012 (vector), Europe – version 1.3, Nov. 2020, EEA [data set], https://doi.org/10.2909/393359a7-7ebd-4a52-80ac-1a18d5f3db9c, 2020. a
ESA and Airbus: Copernicus DEM, Copernicus data space ecosystem, ESA and Airbus [data set], https://doi.org/10.5270/ESA-c5d3d65, 2022. a
Francon, L., Roussel, E., Lopez-Saez, J., Saulnier, M., Stoffel, M., and Corona, C.: Alpine shrubs have benefited more than trees from 20th century warming at a treeline ecotone site in the French Pyrenees, Agr. Forest Meteorol., 329, 109284, https://doi.org/10.1016/j.agrformet.2022.109284, 2023. a
Fugazza, D., Manara, V., Senese, A., Diolaiuti, G., and Maugeri, M.: Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019), Remote Sens.-Basel, 13, 2945, https://doi.org/10.3390/rs13152945, 2021. a
Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, 2015. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019. a, b, c, d
Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R.: MAJA Algorithm Theoretical Basis Document, Zenodo [technical document], https://doi.org/10.5281/zenodo.1209633, 2017. a
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y., Jackson, M., Kääb, A., Kang, S., Stanislav Kutuzov, Milner, A., Molau, U., Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157964.004, pp. 131–202, 2019. a, b, c
Hu, Z., Dietz, A., and Kuenzer, C.: The potential of retrieving snow line dynamics from Landsat during the end of the ablation seasons between 1982 and 2017 in European mountains, Int. J. Appl. Earth Obs., 78, 138–148, https://doi.org/10.1016/j.jag.2019.01.010, 2019a. a
Hu, Z., Dietz, A. J., and Kuenzer, C.: Deriving Regional Snow Line Dynamics during the Ablation Seasons 1984–2018 in European Mountains, Remote Sens.-Basel, 11, 933, https://doi.org/10.3390/rs11080933, 2019b. a
Hu, Z., Dietz, A., Zhao, A., Uereyen, S., Zhang, H., Wang, M., Mederer, P., and Kuenzer, C.: Snow Moving to Higher Elevations: Analyzing Three Decades of Snowline Dynamics in the Alps, Geophys. Res. Lett., 47, 12, https://doi.org/10.1029/2019GL085742, 2020. a, b
Hurrell, J. W.: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation, Science, 269, 676–679, https://doi.org/10.1126/science.269.5224.676, 1995. a
Hussain, M. M. and Mahmud, I.: pyMannKendall: a python package for non parametric Mann Kendall family of trend tests, Journal of Open Source Software, 4, 1556, https://doi.org/10.21105/joss.01556, 2019. a
Hüsler, F., Jonas, T., Riffler, M., Musial, J. P., and Wunderle, S.: A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data, The Cryosphere, 8, 73–90, https://doi.org/10.5194/tc-8-73-2014, 2014. a, b, c, d
Jonas, T., Rixen, C., Sturm, M., and Stoeckli, V.: How alpine plant growth is linked to snow cover and climate variability, J. Geophys. Res.-Biogeo., 113, G3, https://doi.org/10.1029/2007JG000680, 2008. a
Ju, J. and Roy, D. P.: The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally, Remote Sens. Environ., 112, 1196–1211, https://doi.org/10.1016/j.rse.2007.08.011, 2008. a
Kendall, M.: Rank correlation methods, Griffin, Oxford, England, 1948. a
Klein, G., Vitasse, Y., Rixen, C., Marty, C., and Rebetez, M.: Shorter snow cover duration since 1970 in the Swiss Alps due to earlier snowmelt more than to later snow onset, Climatic Change, 139, 637–649, https://doi.org/10.1007/s10584-016-1806-y, 2016. a, b
Koehler, J., Bauer, A., Dietz, A. J., and Kuenzer, C.: Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series, Remote Sens.-Basel, 14, 4461, https://doi.org/10.3390/rs14184461, 2022a. a, b, c, d
Koehler, J., Dietz, A. J., Zellner, P., Baumhoer, C. A., Dirscherl, M., Cattani, L., Vlahovic, Z., Alasawedah, M. H., Mayer, K., Haslinger, K., Bertoldi, G., Jacob, A., and Kuenzer, C.: Drought in Northern Italy: Long Earth Observation Time Series Reveal Snow Line Elevation to Be Several Hundred Meters Above Long-Term Average in 2022, Remote Sens.-Basel, 14, 6091, https://doi.org/10.3390/rs14236091, 2022b. a, b, c
Lemus-Canovas, M., Alonso-González, E., Bonsoms, J., and López-Moreno, J. I.: Daily concentration of snowfalls in the mountains of the Iberian Peninsula, Int. J. Climatol., 44, 485–500, https://doi.org/10.1002/joc.8338, 2024. a
Lindsay, C., Zhu, J., Miller, A. E., Kirchner, P., and Wilson, T. L.: Deriving Snow Cover Metrics for Alaska from MODIS, Remote Sens.-Basel, 7, 12961–12985, https://doi.org/10.3390/rs71012961, 2015. a
Loveland, T. R. and Irons, J. R.: Landsat 8: The plans, the reality, and the legacy, Remote Sens. Environ., 185, 1–6, https://doi.org/10.1016/j.rse.2016.07.033, 2016. a
López-Moreno, J. I., Revuelto, J., Gilaberte, M., Morán-Tejeda, E., Pons, M., Jover, E., Esteban, P., García, C., and Pomeroy, J. W.: The effect of slope aspect on the response of snowpack to climate warming in the Pyrenees, Theor. Appl. Climatol., 117, 207–219, https://doi.org/10.1007/s00704-013-0991-0, 2014. a
López-Moreno, J. I., Soubeyroux, J. M., Gascoin, S., Alonso-Gonzalez, E., Durán-Gómez, N., Lafaysse, M., Vernay, M., Carmagnola, C., and Morin, S.: Long-term trends (1958–2017) in snow cover duration and depth in the Pyrenees, Int. J. Climatol., 40, 6122–6136, https://doi.org/10.1002/joc.6571, 2020. a, b, c, d, e, f, g, h
López-Moreno, J. I., Callow, N., McGowan, H., Webb, R., Schwartz, A., Bilish, S., Revuelto, J., Gascoin, S., Deschamps-Berger, C., and Alonso-González, E.: Marginal snowpacks: The basis for a global definition and existing research needs, Earth-Sci. Rev., 252, 104751, https://doi.org/10.1016/j.earscirev.2024.104751, 2024. a
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945. a
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.: A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015), J. Hydrometeorol., 17, 1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a
Marty, C.: Regime shift of snow days in Switzerland, Geophys. Res. Lett., 35, 12, https://doi.org/10.1029/2008GL033998, 2008. a
Marty, C., Tilg, A.-M., and Jonas, T.: Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps, J. Hydrometeorol., 18, 1021–1031, https://doi.org/10.1175/JHM-D-16-0188.1, 2017. a
Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., Schöner, W., Cat Berro, D., Chiogna, G., De Gregorio, L., Kotlarski, S., Majone, B., Resch, G., Terzago, S., Valt, M., Beozzo, W., Cianfarra, P., Gouttevin, I., Marcolini, G., Notarnicola, C., Petitta, M., Scherrer, S. C., Strasser, U., Winkler, M., Zebisch, M., Cicogna, A., Cremonini, R., Debernardi, A., Faletto, M., Gaddo, M., Giovannini, L., Mercalli, L., Soubeyroux, J.-M., Sušnik, A., Trenti, A., Urbani, S., and Weilguni, V.: Observed snow depth trends in the European Alps: 1971 to 2019, The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, 2021. a, b, c, d
Mendoza, P. A., Musselman, K. N., Revuelto, J., Deems, J. S., López-Moreno, J. I., and McPhee, J.: Interannual and Seasonal Variability of Snow Depth Scaling Behavior in a Subalpine Catchment, Water Resour. Res., 56, e2020WR027343, https://doi.org/10.1029/2020WR027343, 2020. a
Metsämäki, S., Böttcher, K., Pulliainen, J., Luojus, K., Cohen, J., Takala, M., Mattila, O.-P., Schwaizer, G., Derksen, C., and Koponen, S.: The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe, Remote Sens. Environ., 211, 1–12, https://doi.org/10.1016/j.rse.2018.03.029, 2018. a
Nedelcev, O. and Jenicek, M.: Trends in seasonal snowpack and their relation to climate variables in mountain catchments in Czechia, Hydrolog. Sci. J., 66, 2340–2356, https://doi.org/10.1080/02626667.2021.1990298, 2021. a
Notarnicola, C.: Hotspots of snow cover changes in global mountain regions over 2000–2018, Remote Sens. Environ., 243, 111781, https://doi.org/10.1016/j.rse.2020.111781, 2020. a
Notarnicola, C.: Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020, Sci. Rep.-UK, 12, 13731, https://doi.org/10.1038/s41598-022-16743-w, 2022. a, b, c
Pons, M. R., San-Martín, D., Herrera, S., and Gutiérrez, J. M.: Snow trends in Northern Spain: analysis and simulation with statistical downscaling methods, Int. J. Climatol., 30, 1795–1806, https://doi.org/10.1002/joc.2016, 2010. a
Revuelto, J., Gómez, D., Alonso-González, E., Vidaller, I., Rojas-Heredia, F., Deschamps-Berger, C., García-Jiménez, J., Rodríguez-López, G., Sobrino, J., Montorio, R., Perez-Cabello, F., and López-Moreno, J. I.: Intermediate snowpack melt-out dates guarantee the highest seasonal grasslands greening in the Pyrenees, Sci. Rep.-UK, 12, 18328, https://doi.org/10.1038/s41598-022-22391-x, 2022. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines, Version 6, Randolph Glacier Inventory Consortium, https://doi.org/10.7265/4m1f-gd79, 2017. a
Rohrer, M., Salzmann, N., Stoffel, M., and Kulkarni, A. V.: Missing (in-situ) snow cover data hampers climate change and runoff studies in the Greater Himalayas, Sci. Total Environ., 468–469, S60–S70, https://doi.org/10.1016/j.scitotenv.2013.09.056, 2013. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W., and Frangi, A., MICCAI 2015, Lecture Notes in Computer Science, vol. 9351, Springer, Cham, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
Rottler, E., Kormann, C., Francke, T., and Bronstert, A.: Elevation-dependent warming in the Swiss Alps 1981–2017: Features, forcings and feedbacks, Int. J. Climatol., 39, 2556–2568, https://doi.org/10.1002/joc.5970, 2019. a
Rumpf, S. B., Gravey, M., Brönnimann, O., Luoto, M., Cianfrani, C., Mariethoz, G., and Guisan, A.: From white to green: Snow cover loss and increased vegetation productivity in the European Alps, Science, 376, 1119–1122, https://doi.org/10.1126/science.abn6697, 2022. a
Scherrer, S. C.: Temperature monitoring in mountain regions using reanalyses: lessons from the Alps, Environ. Res. Lett., 15, 044005, https://doi.org/10.1088/1748-9326/ab702d, 2020. a
Scherrer, S. C., Appenzeller, C., and Laternser, M.: Trends in Swiss Alpine snow days: The role of local- and large-scale climate variability, Geophys. Res. Lett., 31, https://doi.org/10.1029/2004GL020255, 2004. a
Schöner, W., Koch, R., Matulla, C., Marty, C., and Tilg, A.-M.: Spatiotemporal patterns of snow depth within the Swiss-Austrian Alps for the past half century (1961 to 2012) and linkages to climate change, Int. J. Climatol., 39, 1589–1603, https://doi.org/10.1002/joc.5902, 2019. a
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stati. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968. a
Simon, G., Manuel, G., Tristan, K., and Germain, S.: Algorithm theoretical basis documentation for an operational snow cover product from Sentinel-2 and Landsat-8 data (Let-it-snow) (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.1414452, 2018. a
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields, Water Resour. Res., 43, 7, https://doi.org/10.1029/2006WR005317, 2007. a
Valt, M. and Cianfarra, P.: Recent snow cover variability in the Italian Alps, Cold Reg. Sci. Technol., 64, 146–157, https://doi.org/10.1016/j.coldregions.2010.08.008, 2010. a
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.: The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022. a, b
Vidaller, I., Revuelto, J., Izagirre, E., Rojas-Heredia, F., Alonso-González, E., Gascoin, S., René, P., Berthier, E., Rico, I., Moreno, A., Serrano, E., Serreta, A., and López-Moreno, J. I.: Toward an Ice-Free Mountain Range: Demise of Pyrenean Glaciers During 2011–2020, Geophys. Res. Lett., 48, 18, https://doi.org/10.1029/2021GL094339, 2021. a
Wang, L., Toose, P., Brown, R., and Derksen, C.: Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013, The Cryosphere, 10, 2589–2602, https://doi.org/10.5194/tc-10-2589-2016, 2016. a
Wang, X. and Xie, H.: New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua, J. Hydrol., 371, 192–200, https://doi.org/10.1016/j.jhydrol.2009.03.028, 2009. a
Zhang, Y., Woodcock, C. E., Arévalo, P., Olofsson, P., Tang, X., Stanimirova, R., Bullock, E., Tarrio, K. R., Zhu, Z., and Friedl, M. A.: A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale, Front. Remote Sens., 3, 894618, https://doi.org/10.3389/frsen.2022.894618, 2022. a
Short summary
We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from 10 different satellites. This study fills a knowledge gap regarding the evolution of mountain snow in Europe by covering a much longer period and characterizing trends at much higher resolutions than previous studies. We found a trend for earlier melt-out with average reductions of 5.51 d per decade over the French Alps and 4.04 d per decade over the Pyrenees for the period 1986–2023.
We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from...
