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

2020 ◽  
Vol 14 (4) ◽  
pp. 1409-1423
Author(s):  
Céline Portenier ◽  
Fabia Hüsler ◽  
Stefan Härer ◽  
Stefan Wunderle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Monitoring Network ◽  
Swiss Alps ◽  
European Alps ◽  
Small Catchment ◽  
Point Field ◽  
Wide Angle Lens ◽  
Camera Orientation ◽  
Elevation Model

Abstract. Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in 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 novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network.

Vulnerability of Norway spruce to climate change in mountain forests of the European Alps

2014 ◽  
Vol 60 (2) ◽  
pp. 119-132 ◽  
Author(s):  
C Hartl-Meier ◽  
C Zang ◽  
C Dittmar ◽  
J Esper ◽  
A Göttlein ◽  
...  
Keyword(s):  
Climate Change ◽  
Norway Spruce ◽  
European Alps ◽  
Mountain Forests

scholarly journals Transmission of solar radiation through clouds on melting glaciers: a comparison of parameterizations and their impact on melt modelling

2011 ◽  
Vol 57 (202) ◽  
pp. 367-381 ◽  
Author(s):  
Francesca Pellicciotti ◽  
Thomas Raschle ◽  
Thomas Huerlimann ◽  
Marco Carenzo ◽  
Paolo Burlando
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Radiative Forcing ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Swiss Alps ◽  
Cloud Radiative Forcing ◽  
Correct Definition ◽  
Diurnal Range ◽  
The Impact

AbstractWe explore the robustness and transferability of parameterizations of cloud radiative forcing used in glacier melt models at two sites in the Swiss Alps. We also look at the rationale behind some of the most commonly used approaches, and explore the relationship between cloud transmittance and several standard meteorological variables. The 2 m air-temperature diurnal range is the best predictor of variations in cloud transmittance. However, linear and exponential parameterizations can only explain 30–50% of the observed variance in computed cloud transmittance factors. We examine the impact of modelled cloud transmittance factors on both solar radiation and ablation rates computed with an enhanced temperature-index model. The melt model performance decreases when modelled radiation is used, the reduction being due to an underestimation of incoming solar radiation on clear-sky days. The model works well under overcast conditions. We also seek alternatives to the use of in situ ground data. However, outputs from an atmospheric model (2.2 km horizontal resolution) do not seem to provide an alternative to the parameterizations of cloud radiative forcing based on observations of air temperature at glacier automatic weather stations. Conversely, the correct definition of overcast conditions is important.

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

&lt;p&gt;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.&lt;/p&gt;

Assessment of nominal data requirements for robust estimation of fractional snow cover in alpine-forested terrain

2021 ◽  
Author(s):  
Elzbieta Wisniewski ◽  
Wit Wisniewski
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Model Performance ◽  
Slope Aspect ◽  
Ann Model ◽  
Linear Modeling ◽  
Nominal Data ◽  
Fractional Snow Cover ◽  
Non Linear ◽  
Input Variables

&lt;p&gt;The presented research examines what minimum combination of input variables are required to obtain state-of-the-art fractional snow cover (FSC) estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships among numerous compounded and possibly non-linear bio-geophysical relations encountered in alpine terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution IKONOS images are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators (ex. two multispectral bands for NDSI, etc.). Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. In this study, nine representative scenarios of input data are selected to analyze the FSC performance. Numerous selections of input data combinations produced good results attesting to the powerful ability of ANNs to extract information and utilize redundancy. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used. The need for non-linear modeling to estimate FSC was verified by forcing the ANN to behave linearly. The linear ANN model exhibited profoundly decreased FSC performance, indicating that non-linear processing more optimally estimates FSC in alpine-forested environments.&lt;/p&gt;

scholarly journals Semi-automated calibration method for modelling of mountain permafrost evolution in Switzerland

2016 ◽  
Vol 10 (6) ◽  
pp. 2693-2719 ◽  
Author(s):  
Antoine Marmy ◽  
Jan Rajczak ◽  
Reynald Delaloye ◽  
Christin Hilbich ◽  
Martin Hoelzle ◽  
...  
Keyword(s):  
Large Scale ◽  
Calibration Method ◽  
Swiss Alps ◽  
Climate Projections ◽  
European Alps ◽  
Permafrost Degradation ◽  
Future Evolution ◽  
Mountainous Regions ◽  
Automated Calibration ◽  
The Future

Abstract. Permafrost is a widespread phenomenon in mountainous regions of the world such as the European Alps. Many important topics such as the future evolution of permafrost related to climate change and the detection of permafrost related to potential natural hazards sites are of major concern to our society. Numerical permafrost models are the only tools which allow for the projection of the future evolution of permafrost. Due to the complexity of the processes involved and the heterogeneity of Alpine terrain, models must be carefully calibrated, and results should be compared with observations at the site (borehole) scale. However, for large-scale applications, a site-specific model calibration for a multitude of grid points would be very time-consuming. To tackle this issue, this study presents a semi-automated calibration method using the Generalized Likelihood Uncertainty Estimation (GLUE) as implemented in a 1-D soil model (CoupModel) and applies it to six permafrost sites in the Swiss Alps. We show that this semi-automated calibration method is able to accurately reproduce the main thermal condition characteristics with some limitations at sites with unique conditions such as 3-D air or water circulation, which have to be calibrated manually. The calibration obtained was used for global and regional climate model (GCM/RCM)-based long-term climate projections under the A1B climate scenario (EU-ENSEMBLES project) specifically downscaled at each borehole site. The projection shows general permafrost degradation with thawing at 10 m, even partially reaching 20 m depth by the end of the century, but with different timing among the sites and with partly considerable uncertainties due to the spread of the applied climatic forcing.

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 Hydrological effects of Norway spruce and European beech on snow cover in a mid-mountain region of the Polana mts., Slovakia / Hydrologický vplyv smreka obyčajného a buka lesného na snehovú pokrývku v stredohorských polohách pohoria poľana na slovensku

2012 ◽  
Vol 60 (4) ◽  
pp. 319-332 ◽  
Author(s):  
Matus Hribik ◽  
Tomas Vida ◽  
Jaroslav Skvarenina ◽  
Jana Skvareninova ◽  
Lubomir Ivan
Keyword(s):  
Snow Cover ◽  
Norway Spruce ◽  
Forest Canopy ◽  
Biosphere Reserve ◽  
European Beech ◽  
Mountain Region ◽  
Runoff Generation ◽  
Forest Stands ◽  
Snow Melting ◽  
Beech Stand

The paper evaluates the results of a 6-year-monitoring of the eco-hydrological influence of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus silvatica L.) forest stands on the hydro-physical properties of snow cover. The experiment was carried out in the artificially regenerated 20-25-year-old forest stands approaching the pole timber stage in the middle mountain region of the Polana Mts. - Biosphere reserve situated at about 600 m a.s.l. during the period of maximum snow supply in winters of years 2004 - -2009. Forest canopy plays a decisive role at both the snow cover duration and spring snow melting and runoff generation. A spruce stand is the poorest of snow at the beginning of winter. High interception of spruce canopy hampers the throughfall of snow to soil. During the same period, the soil surface of a beech stand accumulates greater amount of snow. However, a spruce stand accumulates snow by creating snow heaps during the periods of maximum snow cumulation and stand´s microclimate slows down snow melting. These processes are in detail discussed in the paper. The forest stands of the whole biosphere reserve slow down to a significant extent both the snow cover melting and the spring runoff of the whole watershed.

scholarly journals Hydrodynamic Modeling of a Reef-Fringed Pocket Beach Using a Phase-Resolved Non-Hydrostatic Model

2020 ◽  
Vol 8 (11) ◽  
pp. 877
Author(s):  
Johan Risandi ◽  
Dirk P. Rijnsdorp ◽  
Jeff E. Hansen ◽  
Ryan J. Lowe
Keyword(s):  
Model Performance ◽  
Water Levels ◽  
Wave Flow ◽  
Strong Impact ◽  
Wave Heights ◽  
Hydrodynamic Processes ◽  
Hydrostatic Model ◽  
Flow Through ◽  
Wave Induced ◽  
Infragravity Wave

The non-hydrostatic wave-flow model SWASH was used to investigate the hydrodynamic processes at a reef fringed pocket beach in southwestern Australia (Gnarabup Beach). Gnarabup Beach is a ~1.5 km long beach with highly variable bathymetry that is bounded by rocky headlands. The site is also exposed to large waves from the Southern Ocean. The model performance was evaluated using observations collected during a field program measuring waves, currents and water levels between June and July 2017. Modeled sea-swell wave heights (periods 5–25 s), infragravity wave heights (periods 25–600 s), and wave-induced setup exhibited moderate to good agreement with the observations throughout the model domain. The mean currents, which were highly-spatially variable across the study site, were less accurately predicted at most sites. Model agreement with the observations tended to be the worst in the areas with the most uncertain bathymetry (i.e., areas where high resolution survey data was not available). The nearshore sea-swell wave heights, infragravity wave heights and setup were strongly modulated by the offshore waves. The headlands and offshore reefs also had a strong impact on the hydrodynamics within the lagoon (bordered by the reefs) by dissipating much of the offshore sea-swell wave energy and modifying the pattern of the nearshore flows (magnitude and direction). Wave breaking on the reef platforms drove strong onshore directed mean currents over the reefs, resulting in off-shore flow through channels between the reefs and headlands where water exchanges from the lagoon to ocean. Our results demonstrate that the SWASH model is able to produce realistic predictions of the hydrodynamic processes within bathymetrically-complex nearshore systems.

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