Journal cover Journal topic
Advances in Geosciences An open-access journal for refereed proceedings and special publications

Journal metrics

  • CiteScore<br/> value: 0.83 CiteScore
  • SNIP value: 0.527 SNIP 0.527
  • SJR value: 0.544 SJR 0.544
  • IPP value: 0.728 IPP 0.728
  • h5-index value: 13 h5-index 13
Adv. Geosci., 32, 31-39, 2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
11 Dec 2012
Snow accumulation of a high alpine catchment derived from LiDAR measurements
K. Helfricht1,2, J. Schöber1,3, B. Seiser1,4, A. Fischer2,4, J. Stötter1,3, and M. Kuhn2 1alpS – Centre for Climate Change Adaptation Technologies, Innsbruck, Austria
2Institute of Meteorology and Geophysics, University of Innsbruck, Austria
3Institute of Geography, University of Innsbruck, Austria
4Institute of Mountain Research: Man and Environment, Austrian Academy of Sciences, Innsbruck, Austria
Abstract. The spatial distribution of snow accumulation substantially affects the seasonal course of water storage and runoff generation in high mountain catchments. Whereas the areal extent of snow cover can be recorded by satellite data, spatial distribution of snow depth and hence snow water equivalent (SWE) is difficult to measure on catchment scale. In this study we present the application of airborne LiDAR (Light Detecting And Ranging) data to extract snow depths and accumulation distribution in an alpine catchment.

Airborne LiDAR measurements were performed in a glacierized catchment in the Ötztal Alps at the beginning and the end of three accumulation seasons. The resulting digital elevation models (DEMs) were used to calculate surface elevation changes throughout the winter season. These surface elevation changes were primarily referred to as snow depths and are discussed concerning measured precipitation and the spatial characteristics of the accumulation distribution in glacierized and unglacierized areas. To determine the redistribution of catchment precipitation, snow depths were converted into SWE using a simple regression model. Snow accumulation gradients and snow redistribution were evaluated for 100 m elevation bands.

Mean surface elevation changes of the whole catchment ranges from 1.97 m to 2.65 m within the analyzed accumulation seasons. By analyzing the distribution of the snow depths, elevation dependent patterns were obtained as a function of the topography in terms of aspect and slope. The high resolution DEMs show clearly the higher variation of snow depths in rough unglacierized areas compared to snow depths on smooth glacier surfaces. Mean snow depths in glacierized areas are higher than in unglacierized areas. Maximum mean snow depths of 100 m elevation bands are found between 2900 m and 3000 m a.s.l. in unglacierized areas and between 2800 m and 2900 m a.s.l. in glacierized areas, respectively. Calculated accumulation gradients range from 8% to 13% per 100 m elevation band in the observed catchment. Elevation distribution of accumulation calculated by applying these seasonal gradients in comparison to elevation distribution of SWE obtained from airborne laser scanning (ALS) data show the total redistribution of snow from higher to lower elevation bands.

Revealing both, information about the spatial distribution of snow depths and hence the volume of the snow pack, ALS data are an important source for extensive snow accumulation measurements in high alpine catchments. These information about the spatial characteristics of snow distribution are crucial for calibrating hydrological models in order to realistically compute temporal runoff generation by snow melt.

Citation: Helfricht, K., Schöber, J., Seiser, B., Fischer, A., Stötter, J., and Kuhn, M.: Snow accumulation of a high alpine catchment derived from LiDAR measurements, Adv. Geosci., 32, 31-39, doi:10.5194/adgeo-32-31-2012, 2012.