Snow accumulation of a high alpine catchment derived from LiDAR measurements

Abstract

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 theÖtztal 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.

Mass balance observations and reconstruction for Batysh Sook Glacier, Tien Shan, from 2004 to 2016

“…A good estimate of the accumulation gradient seems to be very difficult because of insufficient data to better constrain the spatial distribution of snow. Several studies (e.g., Sold et al, 2016;Helfricht et al, 2012Helfricht et al, , 2014 show that an improvement in the mass balance determination is possible through the combination of conventional mass balance data and data obtained by new methods such as helicopter-borne Ground Penetrating Radar or Light Detection And Ranging (LiDAR) measurements. Such data also enables an improvement in the interpolation of the accumulation distribution.…”

A conceptual, distributed snow redistribution model

“…Models following the second approach use the fact that snow patterns resemble each other every year (Helfricht et al, 2012). Since our model is following the empirical approach, too, the presented paper concentrates on that approach.…”

“…Since our model is following the empirical approach, too, the presented paper concentrates on that approach. Snow accumulation gradients determined by airborne lidar measurements (Helfricht et al, 2012) were used by Schöber et al (2014) to improve hydrological modelling using the distributed energy balance model SES (Snow and Ice Melt; Asztalos, 2004). Lidar data, however, are relatively expensive to obtain.…”

Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements

“…Deems et al, 2008;Melvold and Skaugen, 2013), but also in glacierized catchments (e.g. Dadic et al, 2010;Helfricht et al, 2012;Sold et al, 2013). Since 2001, ALS surveys of annual and seasonal surface elevation changes ( z ALS ) were performed in glacierized catchment of the Ötztal Alps (Austria, 46 • 50 N, 10 • 50 E) (Geist and Stötter, 2007;Helfricht et al, 2012).…”

“…Dadic et al, 2010;Helfricht et al, 2012;Sold et al, 2013). Since 2001, ALS surveys of annual and seasonal surface elevation changes ( z ALS ) were performed in glacierized catchment of the Ötztal Alps (Austria, 46 • 50 N, 10 • 50 E) (Geist and Stötter, 2007;Helfricht et al, 2012). In 2010/11 an ALS survey of the whole mountain range of the Ötztal Alps was conducted to estimate seasonal snow depths.…”