Applications of snow cover mapping in high mountain regions

Haefner, H.; Seidel, K.; Ehrler, H.

doi:10.1016/s0079-1946(97)00143-2

Cited by 35 publications

(28 citation statements)

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“…Furthermore, winter tourism, with its high economic importance in many alpine regions, strongly depends on snow reliability and snow cover duration (e.g. Haefner et al, 1997;Beniston, 2000;Fazzini et al, 2004;López-Moreno and Nogués-Bravo, 2006;Marty, 2008).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Grünewald¹,

Schirmer²,

Mott³

et al. 2010

The Cryosphere

242

274

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Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0-9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Grünewald¹,

Schirmer²,

Mott³

et al. 2010

The Cryosphere

242

274

View full text Add to dashboard Cite

show abstract

“…Accurate assessment of snow depth and its distribution can aid in the forecasting of water resources, the monitoring of natural hazards, and assessment of plant and fauna phenology (Haefner et al, 1997;López-Moreno et al, 2007 and references therein). Despite recent advances in remote sensing and the development of automated nivo-meteorological stations, which provide operational tools for snow analysis, the manual collection of point snow depth and density data is still widely used.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2011

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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 RioÉsera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m 2 ) was subdivided into a grid of 1 m 2 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 hetCorrespondence to: J. I. López-Moreno (nlopez@ipe.csic.es) erogeneous 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.

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“…Radar remote sensing offers the capability to deal with problems pertaining to rainfall estimation (Uijlenhoet and Russchenberg 1996) and the utilization of microwave tomography (new imaging method based on contrast in dielectric properties of materials) as an alternative to weather radar measurement in limited areas (Giuli et al 1996). Various researchers used the optical and SAR data as the basis for glaciological modeling and snow melt monitoring (Haefner et al 1996). Apart from its all-weather capability, the most vital advantage of using SAR imagery lies in its ability to sharply distinguish boundary between land and water.…”

Section: Microwave Remote Sensing In Groundwater Hydrologymentioning

confidence: 99%

Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling

Thakur¹,

2016

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Integration of remote sensing (RS), geographic information systems (GIS) and global positioning system (GPS) are emerging research areas in the field of groundwater hydrology, resource management, environmental monitoring and during emergency response. Recent advancements in the fields of RS, GIS, GPS and higher level of computation will help in providing and handling a range of data simultaneously in a time-and cost-efficient manner. This review paper deals with hydrological modeling, uses of remote sensing and GIS in hydrological modeling, models of integrations and their need and in last the conclusion. After dealing with these issues conceptually and technically, we can develop better methods and novel approaches to handle large data sets and in a better way to communicate information related with rapidly decreasing societal resources, i.e. groundwater.

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Applications of snow cover mapping in high mountain regions

Cited by 35 publications

References 1 publication

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

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

Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling

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