Thermal regime of rock and its relation to snow cover in steep alpine rock walls: gemsstock, central swiss alps

2015 ◽  
Vol 97 (3) ◽  
pp. 579-597 ◽  
Author(s):  
Anna Haberkorn ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
Hansueli Rhyner ◽  
Mathias Bavay ◽  
...  
Keyword(s):  
Snow Cover ◽  
Thermal Regime ◽  
Swiss Alps

Spatial Distribution of Snow Storage and Snowmelt Dynamics in Central Yenisei Siberia

2021 ◽  
pp. 82-92
Author(s):  
I. V. Danilova ◽  
◽  
A. A. Onuchin ◽  
◽  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Sea Level ◽  
Thermal Regime ◽  
Regression Models ◽  
Weather Station ◽  
Digital Maps ◽  
Independent Variables ◽  
Snow Storage

In this paper the spatial distribution of water reserves in the snow cover and the dynamics of snow cover melting due to the peculiarity of the thermal regime were analyzed for the central part of Yenisei Siberia. To create digital maps of water reserves in the snow cover, regression models were developed. The geographic coordinates, elevation above sea level and the distance from the orographic boundaries were used as independent variables in regression models. Based on the created maps, the dynamics of snow cover melting was obtained in the study area, taking into account the thermal regime at a key weather station.

The future of ski resorts in the Swiss Alps: using DMDU to identify tipping points

2021 ◽  
Author(s):  
Saeid Ashraf Vaghefi ◽  
Veruska Muccione ◽  
Kees C.H. van Ginkel ◽  
Marjolijn Haasnoot
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Swiss Alps ◽  
Tipping Points ◽  
Annual Variability ◽  
Ski Resorts ◽  
The Future ◽  
Total Profit ◽  
Climate Uncertainty ◽  
Snow Conditions

<p>The future of ski resorts in the Swiss Alps is highly uncertain. Being dependent on snow cover conditions, winter sport tourism is highly susceptible to changes in temperature and precipitation. With the observed warming of the European Alps being well above global average warming, snow cover in Switzerland is projected to shrink at a rapid pace. Climate uncertainty originates from greenhouse gas emission trajectories (RCPs) and differences between climate models. Beyond climate uncertainty, the snow conditions are strongly subject to intra-annual variability. Series of unfavorable years have already led to the financial collapse of several low-altitude ski resorts. Such abrupt collapses with a large impact on the regional economy can be referred to as climate change induced socio-economic tipping points. To some degree, tipping points may be avoided by adaptation measures such as artificial snowmaking, although these measures are also subject to physical and economical constraints. In this study, we use a variety of exploratory modeling techniques to identify tipping points in a coupled physical-economic model applied to six representative ski resorts in the Swiss Alps. New high-resolution climate projections (CH2018) are used to represent climate uncertainty. To improve the coverage of the uncertainty space and accounting for the intra-annual variability of the climate models, a resampling technique was used to produce new climate realizations. A snow process model is used to simulate daily snow-cover in each of the ski resorts. The likelihood of survival of each resort is evaluated from the number of days with good snow conditions for skiing compared to the minimum thresholds obtained from the literature. Economically, the good snow days are translated into the total profit of ski resorts per season of operation. Multiple unfavorable years of total profit may lead to a tipping point. We use scenario discovery to identify the conditions under which these tipping points occur, and reflect on their implications for the future of snow tourism in the Swiss Alps.</p>

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Ground thermal regime in the vicinity of relict rock glaciers (Cantabrian Mountains, NW Spain)

Finisterra ◽  
2012 ◽  
Vol 44 (87) ◽  
Author(s):  
Javier Santos-González ◽  
Rosa González-Gutiérrez ◽  
Amélia Gómes-Villar ◽  
José Redondo-Vega
Keyword(s):  
Snow Cover ◽  
Thermal Regime ◽  
Strong Influence ◽  
Temperature Data ◽  
Ground Temperature ◽  
Cantabrian Mountains ◽  
Nw Spain ◽  
Freeze Thaw ◽  
Rock Glaciers ◽  
Frost Penetration

Ground temperature data obtained from 2002 to 2007 in sites near relict rock glaciers in the cantabrian mountains, at altitudes between 1500 and 2300 meters is analysed. Snow cover lasted between 3 and 9 months and had a strong influence on the thermal regime. When snow was present, the soil was normally frozen in the first 5 to 10 cm, but daily freeze-thaw cycles were rare. In well developed soils located at sunny faces frost penetration rarely reached more than 10 cm. on the contrary in shady and windy faces with scarce snow cover, frost penetration reached, at least, 40 cm. In persistent snow patches the temperature was stable at 0 ºc, even in relict rock glaciers, where subnival winter air fluxes appear to have been very rare.

Ground thermal contrasts and variability at an alpine pyramidal peak in central Austria (Innerer Knorrkogel, Venediger Mountains)

2021 ◽  
Author(s):  
Andreas Kellerer-Pirklbauer ◽  
Gerhard Karl Lieb
Keyword(s):  
Snow Cover ◽  
Thermal Regime ◽  
Ground Surface ◽  
Temperature Monitoring ◽  
Ground Temperature ◽  
Freeze Thaw ◽  
Ground Temperatures ◽  
Frost Weathering ◽  
Second Year ◽  
Ridge Flank

<p>Ground temperatures in alpine environments are severely influenced by slope orientation (aspect), slope inclination, local topoclimatic conditions, and thermal properties of the rock material. Small differences in one of these factors may substantially impact the ground thermal regime, weathering by freeze-thaw action or the occurrence of permafrost. To improve the understanding of differences, variations, and ranges of ground temperatures at single mountain summits, we studied the ground thermal conditions at a triangle-shaped (plan view), moderately steep pyramidal peak over a two-year period (2018-2020).</p><p>We installed 18 monitoring sites with 23 sensors near the summit of Innerer Knorrkogel (2882m asl), in summer 2018 with one- and multi-channel datalogger (Geoprecision). All three mountain ridges (east-, northwest-, and southwest-facing) and flanks (northeast-, west-, and south-facing) were instrumented with one-channel dataloggers at two different elevations (2840 and 2860m asl) at each ridge/flank to monitor ground surface temperatures. Three bedrock temperature monitoring sites with shallow boreholes (40cm) equipped with three sensors per site at each of the three mountain flanks (2870m asl) were established. Additionally, two ground surface temperature monitoring sites were installed at the summit.</p><p>Results show remarkable differences in mean annual ground temperatures (MAGT) between the 23 different sensors and the two years despite the small spatial extent (0.023 km²) and elevation differences (46m). Intersite variability at the entire mountain pyramid was 3.74°C in 2018/19 (mean MAGT: -0.40°C; minimum: -1.78°C; maximum: 1.96°C;) and 3.27°C in 2019/20 (mean MAGT: 0.08°C; minimum: -1.54°C; maximum: 1,73°C;). Minimum was in both years at the northeast-facing flank, maximum at the south-facing flank. In all but three sites, the second monitoring year was warmer than the first one (mean +0.48°C) related to atmospheric differences and site-specific snow conditions. The comparison of the MAGT-values of the two years (MAGT-2018/19 minus MAGT-2019/20) revealed large thermal inhomogeneities in the mountain summit ranging from +0.65° (2018/19 warmer than 2019/20) to -1.76°C (2018/19 colder than 2019/20) at identical sensors. Temperature ranges at the three different aspects but at equal elevations were 1.7-2.2°C at ridges and 1.8-3.7°C at flanks for single years. The higher temperature range for flank-sites is related to seasonal snow cover effects combined with higher radiation at sun-exposed sites. Although the ground temperature was substantially higher in the second year, the snow cover difference between the two years was variable. Some sites experienced longer snow cover periods in the second year 2019/20 (up to +85 days) whereas at other sites the opposite was observed (up to -85 days). Other frost weathering-related indicators (diurnal freeze-thaw cycles, frost-cracking window) show also large intersite and interannual differences.</p><p>Our study shows that the thermal regime at a triangle-shaped moderately steep pyramidal peak is very heterogeneous between different aspects and landforms (ridge/flank/summit) and between two monitoring years confirming earlier monitoring and modelling results. Due to high intersite and interannual variabilities, temperature-related processes such as frost-weathering can vary largely between neighbouring sites. Our study highlights the need for systematic and long-term ground temperature monitoring in alpine terrain to improve the understanding of small- to medium-scale temperature variabilities.</p>

Growth response of Norway spruce saplings in two forest gaps in the Swiss Alps to artificial browsing, infection with black snow mold, and competition by ground vegetation

2006 ◽  
Vol 36 (11) ◽  
pp. 2782-2793 ◽  
Author(s):  
Catherine Cunningham ◽  
Niklaus E Zimmermann ◽  
Veronika Stoeckli ◽  
Harald Bugmann
Keyword(s):  
Snow Cover ◽  
Norway Spruce ◽  
Model Performance ◽  
Swiss Alps ◽  
Ground Vegetation ◽  
Strong Impact ◽  
European Alps ◽  
Snow Mold ◽  
Forest Gaps ◽  
Sapling Growth

Black snow mold (Herpotrichia juniperi (Duby) Petr.) infection and browsing byungulates influence the growth of Norway spruce (Picea abies (L.) Karst.) saplings in subalpine forests in the European Alps. To isolate the impacts of artificial browsing (clipping of shoots) and snow mold infection on growth, we conducted a 2 year field experiment with planted saplings in two forest gaps in the subalpine zone of the Swiss Alps. In the first year (2003) saplings responded slightly positively to clipping and negatively to snow mold infection; sapling growth behavior was site-specific (ANOVA, r2 = 0.35). In 2004, saplings responded negatively to clipping, snow mold infection, long-lasting snow cover, and shading by ground vegetation (ANOVA, r2 = 0.59). The difference in mean annual growth rates between noninfected and infected saplings was large; long-lasting snow was found to enhance snow mold coverage. Removing these variables from general linear models strongly reduced model performance (d2 = 0.32 for the full model, d2 = 0.23 for no clipping, d2 = 0.16 for no snow cover). Sapling growth was negatively related to shading by ground vegetation, especially in 2004. We conclude that these biotic factors have a strong impact on growth, both individually and in combination, and that their effect is enhanced by interaction with environmental factors such as snow duration.

scholarly journals Towards a webcam-based snow cover monitoring network: methodology and evaluation

2019 ◽  
Author(s):  
Céline Portenier ◽  
Fabia Hüsler ◽  
Stefan Härer ◽  
Stefan Wunderle
Keyword(s):  
Snow Cover ◽  
Monitoring Network ◽  
Grid Cell ◽  
Swiss Alps ◽  
European Alps ◽  
Catchment Scale ◽  
Small Catchment ◽  
Point Field ◽  
Camera Orientation ◽  
Elevation Model

Abstract. Snow cover variability has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to the small catchment scale. Here, we present a semi-automatic procedure to derive snow cover maps from arbitrary webcam images. We use freely available webcam images of the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a new registration approach that automatically resolves camera parameters (camera orientation, principal point, field of view (FOV)) by using an estimate of the webcams position and a high-resolution digital elevation model (DEM). Furthermore, two recent snow classification methods are compared and analyzed. The resulting snow cover maps have the same spatial resolution as the DEM and indicate whether a grid cell is snow-covered, snow-free, or not visible from webcams' positions. GCPs were used to evaluate our novel automatic image registration approach. The evaluation reveals in a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV 

scholarly journals Long-term energy balance measurements at three different mountain permafrost sites in the Swiss Alps

2021 ◽  
Author(s):  
Martin Hoelzle ◽  
Christian Hauck ◽  
Tamara Mathys ◽  
Jeannette Noetzli ◽  
Cécile Pellet ◽  
...  
Keyword(s):  
Energy Balance ◽  
Air Temperature ◽  
Thermal Regime ◽  
Surface Energy Balance ◽  
Heat Fluxes ◽  
Swiss Alps ◽  
Net Radiation ◽  
Ground Heat Flux

Abstract. The surface energy balance is a key factor influencing the ground thermal regime. With ongoing climate change, it is crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers as well as their relative impacts on permafrost thermal regime. A unique set of high-altitude meteorological measurements has been analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps (Murtèl-Corvatsch, Schilthorn and Stockhorn), where data is being collected since the late 1990s in collaboration with the Swiss Permafrost Monitoring Network (PERMOS). All stations are equipped with sensors for four-component radiation, air temperature, humidity, wind speed and direction as well as ground temperatures and snow height. The three sites differ considerably in their surface and ground material composition as well as their ground ice contents. The energy fluxes are calculated based on two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes), larger uncertainties exist for the determination of turbulent sensible and latent heat fluxes. Our results show that mean air temperature at Murtèl-Corvatsch (1997–2018, 2600 m asl.) is −1.66 °C and has increased by about 0.7 °C during the measurement period. At the Schilthorn site (1999–2018, 2900 m asl.) a mean air temperature of −2.60 °C with a mean increase of 1.0 °C was measured. The Stockhorn site (2003–2018, 3400 m asl.) recorded lower air temperatures with a mean of −6.18 °C and an increase of 0.7 °C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 30.59 W m−2 for Murtèl-Corvatsch, 32.40 W m−2 for Schilthorn and 6.91 W m−2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 13 W m−2 using the Bowen ratio method and 3 to 15 W m−2 using the bulk method at all sites. Large differences are observed regarding the energy used for melting of the snow cover: at Schilthorn a value of 8.46 W m−2, at Murtèl-Corvatsch of 4.17 W m−2 and at Stockhorn of 2.26 W m−2 is calculated reflecting the differences in snow height at the three sites. In general, we found considerable differences in the energy fluxes at the different sites. These differences may help to explain and interpret the causes of the varying reactions of the permafrost thermal regime at the three sites to a warming atmosphere. We recognize a strong relation between the net radiation and the ground heat flux. Our results further demonstrate the importance of long-term monitoring in order to better understand the impacts of changes in the surface energy balance components on the permafrost thermal regime. The dataset presented can be used to improve permafrost modelling studies aiming at e.g. advancing knowledge about permafrost thaw processes. The data presented and described in this study is available for download at the following site http://dx.doi.org/10.13093/permos-meteo-2021-01 (Hoelzle et al., 2021).

scholarly journals Sensitivity of active layer freezing process to snow cover in Arctic Alaska

2018 ◽  
Author(s):  
Yonghong Yi ◽  
John S. Kimball ◽  
Richard H. Chen ◽  
Mahta Moghaddam ◽  
Charles E. Miller
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Active Layer ◽  
Thermal Regime ◽  
Freezing Process ◽  
Organic Layer ◽  
Soil Organic Layer ◽  
Arctic Alaska ◽  
In Situ Observations

Abstract. The contribution of cold season soil respiration to Arctic-boreal carbon cycle and potential feedbacks to global climate system remain poorly quantified, partly due to a poor understanding of the changes in the soil thermal regime and liquid water content during the soil freezing process. Here, we characterized the processes controlling active layer freezing in Arctic Alaska using an integrated approach combining in-situ observations, local scale (~ 50 m) longwave radar retrievals from NASA Airborne P-band polarimetric SAR (PolSAR), and a remote sensing driven permafrost model. To better capture landscape variability in snow cover and its influence on soil thermal regime, we downscaled global coarse-resolution (~ 0.5°) reanalysis snow data using finer scale (500 m) MODIS (MODerate resolution Imaging Spectroradiometer) snow cover extent (SCE) observations. The downscaled 1-km snow depth dataset captured fine-scale variability associated with local topography, and compared well with in-situ observations across Alaska, with a mean RMSE of 0.16 m and bias of −0.01 m in Arctic Alaska, which was used to drive the permafrost model. We also used the in-situ soil dielectric constant (ɛ) profile measurements to guide model parameterization of soil organic layer and unfrozen water content curve. Across a 2° latitudinal zone along the Dalton highway in the Alaska North Slope, the model simulated mean zero-curtain period was generally consistent with in-situ observations (R: 0.6 ± 0.2; RMSE: 19 ± 6 days), which showed mean zero-curtain periods of 61 ± 11 to 73 ± 15 days from depths of 0.25 m to 0.45 m. Along the same transect, both the observed and model simulated zero-curtain periods were positively correlated (R > 0.55, p 

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