scholarly journals Seasonal development of spatial snow-depth variability across different scales in the Swiss Alps

2011 ◽  
Vol 52 (58) ◽  
pp. 216-222 ◽  
Author(s):  
Luca Egli ◽  
Nena Griessinger ◽  
Tobias Jonas
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Weather Prediction ◽  
Ground Truth ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Seasonal Development ◽  
Precipitation Pattern ◽  
Scaling Analysis ◽  
Snow Hydrology

AbstarctThe depth of snow cover is temporally and spatially heterogeneous at different scales in Alpine regions. For snow hydrology/climatology the spatial variability of snow depths is a key parameter for capturing the total amount of snow in a given area. Here a scale analysis of the spatial variability of snow depths during the accumulation period is investigated. The development of the variability is characterized by a parameter, β, describing the relationship between the standard deviation and the mean of snow depths. The analysis includes two datasets: (1) 141 snow-depth point measurements representing flat-field observations, and (2) snow precipitation from the numerical weather prediction model COSMO-7. The results reveal that β is almost invariant at scales between 10 and 300 km. The COSMO-7 data exhibit the same scale invariance above 50 km, indicating that the spatial variability of snow depths is formed by the precipitation pattern at these scales. The scaling analysis of β allows determination of the absolute accuracy of estimating the total amount of snow in a given area and helps to validate different snow models or remote-sensing techniques by ground truth verification.

scholarly journals A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps

2010 ◽  
Vol 51 (54) ◽  
pp. 32-38 ◽  
Author(s):  
Luca Egli ◽  
Tobias Jonas ◽  
Jean-Marie Bettems
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Practical Importance ◽  
Cost Effective ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Virtual Network ◽  
Systematic Bias ◽  
Snow Water

AbstractDaily new snow water equivalent (HNW) and snow depth (HS) are of significant practical importance in cryospheric sciences such as snow hydrology and avalanche formation. In this study we present a virtual network (VN) for estimating HNW and HS on a regular mesh over Switzerland with a grid size of 7 km. The method is based on the HNW output data of the numerical weather prediction model COSMO-7, driving an external accumulation/melting routine. The verification of the VN shows that, on average, HNW can be estimated with a mean systematic bias close to 0 and an averaged absolute accuracy of 4.01 mm. The results are equivalent to the performance observed when comparing different automatic HNW point estimations with manual reference measurements. However, at the local scale, HS derived by the VN may significantly deviate from corresponding point measurements. We argue that the VN presented here may introduce promising cost-effective options as input for spatially distributed snow hydrological and avalanche risk management applications in the Swiss Alps.

scholarly journals Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

2011 ◽  
Vol 5 (3) ◽  
pp. 617-629 ◽  
Author(s):  
J. I. López-Moreno ◽  
S. R. Fassnacht ◽  
S. Beguería ◽  
J. B. P. Latron
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Snow Depth ◽  
Spatial Configuration ◽  
Estimation Error ◽  
Sampling Strategy ◽  
Ground Truth ◽  
Depth Estimation ◽  
Depth Estimate

Abstract. Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m2) was subdivided into a grid of 1 m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial. Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least five snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, the sampling strategy did not impact on the snow depth estimate under lack of spatial autocorrelation. However, with a high spatial autocorrelation a smaller error was obtained when the distance between measurements was greater.

scholarly journals Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

2011 ◽  
Vol 5 (3) ◽  
pp. 1627-1653
Author(s):  
J. I. López-Moreno ◽  
S. R. Fassnacht ◽  
S. Beguería ◽  
J. B. P. Latron
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Snow Depth ◽  
Spatial Configuration ◽  
Estimation Error ◽  
Sampling Strategy ◽  
Ground Truth ◽  
Depth Estimation ◽  
Data Points

Abstract. Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m2) was subdivided into a grid of 1 m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial. The spatial autocorrelation of snowpack distribution can affect the local representativeness of snowpack. Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least 5 snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, no particular sampling strategy provided an improved estimate of snow depth, but using a greater distance between measurements within a plot improved the representativeness of the estimates.

scholarly journals Density of Seasonal Snow in the Mountainous Environment of Five Slovak Ski Centers

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3563
Author(s):  
Michal Mikloš ◽  
Jaroslav Skvarenina ◽  
Martin Jančo ◽  
Jana Skvareninova
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Winter Season ◽  
Seasonal Development ◽  
Snow Density ◽  
Temporal And Spatial Variability ◽  
Development Processes ◽  
Temporal And Spatial ◽  
Natural Snow ◽  
Snowpack Properties

Climate change affects snowpack properties indirectly through the greater need for artificial snow production for ski centers. The seasonal snowpacks at five ski centers in Central Slovakia were examined over the course of three winter seasons to identify and compare the seasonal development and inter-seasonal and spatial variability of depth average snow density of ski piste snow and uncompacted natural snow. The spatial variability in the ski piste snow density was analyzed in relation to the snow depth and snow lances at the Košútka ski center using GIS. A special snow tube for high-density snowpack sampling was developed (named the MM snow tube) and tested against the commonly used VS-43 snow tube. Measurements showed that the MM snow tube was constructed appropriately and had comparable precision. Significant differences in mean snow density were identified for the studied snow types. The similar rates of increase for the densities of the ski piste snow and uncompacted natural snow suggested that the key density differences stem from the artificial (machine-made) versus natural snow versus processes after and not densification due to snow grooming machines and skiers, which was relevant only for ski piste snow. The ski piste snow density increased on slope with decreasing snow depth (18 kg/m³ per each 10 cm), while snow depth decreased 2 cm per each meter from the center of snow lances. Mean three seasons maximal measured density of ski piste snow was 917 ± 58 kg/m³ the density of ice. This study increases the understanding of the snowpack development processes in a manipulated mountainous environment through examinations of temporal and spatial variability in snow densities and an investigation into the development of natural and ski piste snow densities over the winter season.

scholarly journals Experimental investigation to characterize simple versus multi scaling analysis of hydraulic conductivity at a mesoscale

2021 ◽  
Author(s):  
Guglielmo Federico Antonio Brunetti ◽  
Samuele De Bartolo ◽  
Carmine Fallico ◽  
Ferdinando Frega ◽  
Maria Fernanda Rivera Velásquez ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Spatial Variability ◽  
Hydraulic Properties ◽  
Scaling Laws ◽  
Scaling Analysis ◽  
Field Scale ◽  
Porous Formation ◽  
Proper Design ◽  
First Time

AbstractThe spatial variability of the aquifers' hydraulic properties can be satisfactorily described by means of scaling laws. The latter enable one to relate the small (typically laboratory) scale to the larger (typically formation/regional) ones, therefore leading de facto to an upscaling procedure. In the present study, we are concerned with the spatial variability of the hydraulic conductivity K into a strongly heterogeneous porous formation. A strategy, allowing one to identify correctly the single/multiple scaling of K, is applied for the first time to a large caisson, where the medium was packed. In particular, we show how to identify the various scaling ranges with special emphasis on the determination of the related cut-off limits. Finally, we illustrate how the heterogeneity enhances with the increasing scale of observation, by identifying the proper law accounting for the transition from the laboratory to the field scale. Results of the present study are of paramount utility for the proper design of pumping tests in formations where the degree of spatial variability of the hydraulic conductivity does not allow regarding them as “weakly heterogeneous”, as well as for the study of dispersion mechanisms.

scholarly journals Past and future snowmelt trends in the Swiss Alps: the role of temperature and snowpack

2021 ◽  
Vol 165 (3-4) ◽  
Author(s):  
Maria Vorkauf ◽  
Christoph Marty ◽  
Ansgar Kahmen ◽  
Erika Hiltbrunner
Keyword(s):  
Snow Depth ◽  
Growing Season ◽  
High Elevation ◽  
Alpine Plants ◽  
Swiss Alps ◽  
Climate Change Scenarios ◽  
Freezing Damage ◽  
Strong Shift ◽  
Air Temperatures ◽  
Early Snowmelt

AbstractThe start of the growing season for alpine plants is primarily determined by the date of snowmelt. We analysed time series of snow depth at 23 manually operated and 15 automatic (IMIS) stations between 1055 and 2555 m asl in the Swiss Central Alps. Between 1958 and 2019, snowmelt dates occurred 2.8 ± 1.3 days earlier in the year per decade, with a strong shift towards earlier snowmelt dates during the late 1980s and early 1990s, but non-significant trends thereafter. Snowmelt dates at high-elevation automatic stations strongly correlated with snowmelt dates at lower-elevation manual stations. At all elevations, snowmelt dates strongly depended on spring air temperatures. More specifically, 44% of the variance in snowmelt dates was explained by the first day when a three-week running mean of daily air temperatures passed a 5 °C threshold. The mean winter snow depth accounted for 30% of the variance. We adopted the effects of air temperature and snowpack height to Swiss climate change scenarios to explore likely snowmelt trends throughout the twenty-first century. Under a high-emission scenario (RCP8.5), we simulated snowmelt dates to advance by 6 days per decade by the end of the century. By then, snowmelt dates could occur one month earlier than during the reference periods (1990–2019 and 2000–2019). Such early snowmelt may extend the alpine growing season by one third of its current duration while exposing alpine plants to shorter daylengths and adding a higher risk of freezing damage.

scholarly journals Lidar measurement of snow depth: a review

2013 ◽  
Vol 59 (215) ◽  
pp. 467-479 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Thomas H. Painter ◽  
David C. Finnegan
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Near Infrared ◽  
Ground Surface ◽  
Lidar Measurement ◽  
Snow Hydrology ◽  
Sensing Technology ◽  
Transfer Error ◽  
Scale Range ◽  
Visible Wavelengths

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

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