Observing Snow Depth at Sub-Kilometer Resolution over the European Alps from Sentinel-1

Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution in mountainous regions, where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resources management, flood forecasting and numerical weather prediction.

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Detection of precipitation and snow cover trends in the the European Alps over the last century using model and observational data

10.5194/egusphere-egu2020-18274 ◽

2020 ◽

Author(s):

Julien Beaumet ◽

Martin Menegoz ◽

Hubert Gallée ◽

Vincent Vionnet ◽

Xavier Fettweis ◽

...

Keyword(s):

Climate Change ◽

Snow Cover ◽

Observational Data ◽

Snow Depth ◽

European Alps ◽

Temperature Changes ◽

In Situ Observations ◽

Precipitation Changes ◽

High Elevations

The European Alps are particularly sensitive to climate change. Compared to temperature, changes in precipitation are more challenging to detect and attribute to ongoing anthropic climate change mainly as a result of large inter-annual variability, lack of reliable measurements at high elevations and opposite signals depending on the season or the elevation considered. However, changes in precipitation and snow cover have significant socio-environmental impact mostly trough water resource availability. These changes are investigated within the framework of the Trajectories initiative (). The variability and changes in precipitation and snow cover in the European Alps has been simulated with the MAR regional climate model at a 7 km horizontal resolution driven by ERA20C (1902-2010) and ERA5 (1979-2018) reanalyses. For precipitation, MAR outputs were compared with EURO-4M, SAFRAN, SPAZM and E-OBS reanalyses as well as in-situ observations. The model was shown to reproduce correctly seasonal and inter-annual variability. The spatial biases of the model have the same order of magnitude as the differences between the three observational data sets. Model experiment has been used to detect precipitation changes over the last century. An increase in winter precipitation is simulated over the North-western part of the Alps at high altitudes (>1500m). Significant decreases in summer precipitation were found in many low elevation areas, especially the Po Plain while no significant trends where found at high elevations. Because of large internal variability, precipitation changes are significant (pvalue<0.05) only when considering their evolution over long period, typically 60-100 years in both model and observations.Snow depth and water equivalent (SWE) in the French Alps simulated with MAR have been compared to the SAFRAN-Crocus reanalyses and to in-situ observations. MAR was found to simulate a realistic distribution of SWE as function of the elevation in the French Alpine massifs, although it underestimates SWE at low elevations in the Pre-Alps. Snow cover over the whole European Alps is evaluated using MODIS satellite data. Finally, trends in snow cover and snow depth are highlighted as well as their relationships with the precipitation and temperature changes over the last century.

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Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽

10.3390/atmos11010046 ◽

2019 ◽

Vol 11 (1) ◽

pp. 46

Author(s):

Michael Matiu ◽

Marcello Petitta ◽

Claudia Notarnicola ◽

Marc Zebisch

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Winter Period ◽

European Alps ◽

Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

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Observed snow depth trends in the European Alps: 1971 to 2019

The Cryosphere ◽

10.5194/tc-15-1343-2021 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1343-1382

Author(s):

Michael Matiu ◽

Alice Crespi ◽

Giacomo Bertoldi ◽

Carlo Maria Carmagnola ◽

Christoph Marty ◽

...

Keyword(s):

Snow Depth ◽

Principal Component ◽

European Alps ◽

North West ◽

Modes Of Variability ◽

North East ◽

Regional Trends ◽

Spatial Coverage ◽

Snow Cover Duration ◽

Regional Analyses

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

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Observed snow depth trends in the European Alps 1971 to 2019

10.5194/tc-2020-289 ◽

2020 ◽

Author(s):

Michael Matiu ◽

Alice Crespi ◽

Giacomo Bertoldi ◽

Carlo Maria Carmagnola ◽

Christoph Marty ◽

...

Keyword(s):

Snow Depth ◽

Principal Component ◽

Alpine Region ◽

European Alps ◽

Climatic Forcing ◽

Climate Zones ◽

Modes Of Variability ◽

Regional Trends ◽

Spatial Coverage ◽

Regional Analyses

Abstract. The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north and high Alpine, northeast, northwest, southeast and southwest. Linear trends of mean monthly snow depth between 1971 to 2019 showed decreases in snow depth for 87 % of the stations. December to February trends were on average −1.1 cm decade−1 (min, max: −10.8, 4.4; elevation range 0–1000 m), −2.5 (−25.1, 4.4; 1000–2000 m) and −0.1 (−23.3, 9.9; 2000–3000 m), with stronger trends in March to May: −0.6 (−10.9, 1.0; 0–1000 m), −4.6 (−28.1, 4.1; 1000–2000 m) and −7.6 (−28.3, 10.5; 2000–3000 m). However, regional trends differed substantially, which challenges the notion of generalizing results from one Alpine region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

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Evaluating a prediction system for snow management

The Cryosphere ◽

10.5194/tc-15-3949-2021 ◽

2021 ◽

Vol 15 (8) ◽

pp. 3949-3973

Author(s):

Pirmin Philipp Ebner ◽

Franziska Koch ◽

Valentina Premier ◽

Carlo Marin ◽

Florian Hanzer ◽

...

Keyword(s):

Snow Depth ◽

Management Practices ◽

Mean Deviation ◽

Observation Data ◽

European Alps ◽

Major Step ◽

Ski Resort ◽

Ski Resorts ◽

The Impact ◽

Sentinel 2

Abstract. The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 2020 (H2020) project PROSNOW, a service to enable real-time optimization of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus, and SNOWPACK–Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both ground-based data (GNSS-measured snow depth) and spaceborne snow maps (Copernicus Sentinel-2). Particular attention has been devoted to characterizing the spatial performance of the simulated piste snow management at a resolution of 10 m. The simulated results showed a high overall accuracy of more than 80 % for snow-covered areas compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for local differences in the snow depth between the simulations and the measurements are mainly the impact of snow redistribution by skiers; compensation of uneven terrain when grooming; or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort into differently sized ski resort reference units (SRUs) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. As snow management assessment and prediction systems get integrated into the workflow of resort managers, the formulation of snow management can be refined in the future.

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Review comments for Matiu et al. “Observed snow depth trends in the European Alps 1971 to 2019”

10.5194/tc-2020-289-rc2 ◽

2020 ◽

Author(s):

Ross Brown

Keyword(s):

Snow Depth ◽

European Alps

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Observed snow depth trends in the European Alps 1971 to 2019

10.5194/egusphere-egu21-3287 ◽

2021 ◽

Author(s):

Michael Matiu ◽

Alice Crespi ◽

Giacomo Bertoldi ◽

Carlo Maria Carmagnola ◽

Christoph Marty ◽

...

Keyword(s):

Snow Depth ◽

Principal Component ◽

European Alps ◽

Climatic Forcing ◽

Modes Of Variability ◽

Regional Trends ◽

Spatial Coverage ◽

Trend Assessment ◽

Snow Cover Duration ◽

Regional Analyses

The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses, which complicates comparisons between regions and makes Alpine wide conclusions questionable. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations, of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north & high Alpine, northeast, northwest, southeast, and south & high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations for November to May. The average trend among all stations for seasonal (November to May) mean snow depth was -8.4 % per decade, for seasonal maximum snow depth -5.6 % per decade, and for seasonal snow cover duration -5.6 % per decade. However, regional trends differed substantially after accounting for elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

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Snow depth variability in the Northern Hemisphere mountains observed from space

Nature Communications ◽

10.1038/s41467-019-12566-y ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 20

Author(s):

Hans Lievens ◽

Matthias Demuzere ◽

Hans-Peter Marshall ◽

Rolf H. Reichle ◽

Ludovic Brucker ◽

...

Keyword(s):

Water Resources ◽

Sierra Nevada ◽

Northern Hemisphere ◽

Snow Depth ◽

Large Scale ◽

Reanalysis Data ◽

European Alps ◽

Mountain Ranges ◽

Detection Approach ◽

Snow Water

Abstract Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

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The evaluation of the potential of global data products for snow hydrological modelling in ungauged high alpine catchments

10.5194/hess-2020-326 ◽

2020 ◽

Author(s):

Michael Weber ◽

Franziska Koch ◽

Matthias Bernhardt ◽

Karsten Schulz

Keyword(s):

Land Surface ◽

Snow Depth ◽

Meteorological Data ◽

European Alps ◽

Depth Measurement ◽

Hybrid Data ◽

The Mean ◽

Alpine Catchments ◽

Global Data

Abstract. Worldwide, there is a strong discrepancy between the importance of high alpine catchments for the water cycle and the availability of meteorological and snow hydrological in situ measurements. Good knowledge on the timing and quantity of snow meltwater is crucial for numerous hydrological applications, also far way downstream. For several decades, the number of global data sets of different meteorological and land surface parameters has been increasing, but their applicability in modelling high alpine regions has been insufficiently investigated so far. We tested such data for a 10-year period with the physically-based Cold Regions Hydrological Model (CRHM). Our study site is the gauged high alpine Research Catchment Zugspitze (RCZ) of 12 km2 in the European Alps. We used a selection of nine different meteorological driver data setups including data transferred from another alpine station, data from an atmospheric model and hybrid data, whereof we investigated data for all meteorological parameters and substituting precipitation only. For one product, we applied an advanced downscaling approach to test the advantage of such methods. The range between all setups is high at 3.5 °C for the mean decadal temperature and at 1510 mm for the mean decadal precipitation sum. The comparison of all model results with measured snow depth and reference simulations driven with in situ meteorological data demonstrates that the setup with the transferred data performs best, followed by the substitution of precipitation only with hybrid data. All other setups were unrealistic or showed plausible results only for some parts of the RCZ. As a second goal, we investigated potential differences in model performance resulting from topographic parameterization according to three globally available digital elevation models (DEMs); two with 30 m and one with 1 km resolution. As reference, we used a 2.5 m resolution DEM. The simulations with all DEM setups performed well at the snow depth measurement sites and on catchment scale, even if they indicate considerable differences. Differences are mainly caused by product specific topography induced differences in solar radiation. Surprisingly, the setup with the coarsest DEM performed best in describing the catchment mean due to averaged out topographic differences. However, this was not the case for a finer resolution. For the two plausible meteorological setups and all DEM setups, we additionally investigated the maximum quantity and the temporal development of the snowpack as well as the runoff regime. Even those quite plausible setups revealed differences of up to 20 % in snowpack volume and duration, which consequently lead to considerable shifts in runoff. Overall, we could demonstrate that global data are a valuable source to substitute single missing meteorological variables or topographic information, but the exclusive use of such driver data does not provide sufficiently accurate results for the RCZ. For the future, however, we expect an increasing role of global data in modelling ungauged high alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

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