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Snow Depth and Surface Conditions of Austfonna Ice Cap (Svalbard) Using Field Observations and Satellite Altimetry

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Snow Depth and Surface Conditions of Austfonna Ice Cap (Svalbard) Using Field Observations and Satellite Altimetry SNOW DEPTH AND SURFACE CONDITIONS OF AUSTFONNA ICE CAP (SVALBARD) USING FIELD OBSERVATIONS AND SATELLITE ALTIMETRY Alexei Kouraev(1,2), Benoît Legrésy(1), Frédérique Rémy(1), Andrea Taurisano(3,4), Jack Kohler(3) 1) Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), 14, avenue Edouard Belin, 31400 Toulouse, France, [email protected], [email protected], [email protected] 2) State Oceanography Institute (SOI), St. Petersburg branch, Vasilyevskiy ostrov, 23 liniya, 2a, St Petersburg, Russia 3) Norwegian Polar Institute (NPI), Polarmiljøsenteret, Hjalmar Johansens gate 14, Tromsø, Norway, ,[email protected] 4) Multiconsult, Fiolveien 13 N-9016 Tromsø, Norway, [email protected] ABSTRACT Nordaustlandet has two main ice caps - Vestfonna (surface 2500 km2) and Austfonna, which is the largest We have studied snow cover and surface state of the ice cap in the Eurasian Arctic (surface 8120 km2). largest ice cap in the Eurasian Arctic - the Austfonna on Though this Austfonna has been stable during the last the Svalbard. Our study is based on satellite radar 30 years, there are outlet glaciers that have experienced altimetry observations combined with the field large changes, such as Etonbreen glacier (Fig. 2). Using measurements. We use ENVISAT altimetry data (radar multitemporal SPOT imagery we have estimated that altimeter and radiometer) over the Austfonna since 1995 between April 1987 and March 1998 Etonbreen and its in combination with GPR (ground penetrating radar) neighbour glacier Basin 03 have lost about 7.5 km2, surveys done by the NPI in 2005. Radar waveform giving an average 0.7 km2/year retreat. parameters provide useful information on the surface conditions, especially on melting/freezing. Passive microwave data from MWR radiometer onboard ENVISAT also provide estimates of the snow depth. By combining these in situ and satellite observations we perform cross-comparison of snow depth estimates and analyse spatial and temporal distribution of snow depth and surface conditions of Austfonna. 1. INTRODUCTION Glaciers and ice caps are extremely sensitive to global climate variability. We have studied snow cover and surface state of the ice caps on the Nordaustlandet (Svalbard) (fig. 1). Figure 2. Map showing changes in the frontal positions of Wahlenbergfjord outlet glaciers between 1987 and 1998. This map is available online at http://www.legos.obs- mip.fr/en/equipes/glacio/integral.html In the framework of the INTEGRAL project we have also created a regional glacial reference database REGARD (figure 3). This database includes interactive multimedia modules with GIS-like capabilities. Modules 1-3 cover three regions: Svalbard, Novaya Zemlya and Franz-Josef Land. These modules provide on a background satellite image (MODIS) various information such as glacier boundaries, glaciers parameters, geographical names etc. Module 4 consist of the Austfonna Remote Sensing Data Catalogue that provides samples of remote sensing (SPOT, ERS-1,2) Figure 1. Map of Nordaustlandet and other types (DTM) data used in the INTEGRAL project. All these stand-alone modules are easy-to-use _____________________________________________________ Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007) and easy-to-understand, stand-alone modules, what perpendicular to the satellite track, we refer all points makes them useful reference tools for scientists and that fall between these two boundaries, to the chosen wide public, including schools. They are realised as reference point. Adobe flash (requires free plug-in from www.adobe.com) or a Windows *.exe files, size 1-2 Mb each module. 150015001500 mmm 400 m Figure 3. An information sheet illustrating the content Figure 4. Sample of spatial distribution of 18 Hz and capabilities of the REGARD database. This sheet ENVISAT measures (cycles 10 to 36) over Austfonna. and database REGARD are available online at Data from cycle 20 have been chosen as reference http://www.legos.obs- points (red dots). Dashed line – boundaries for one mip.fr/en/equipes/glacio/integral.html reference point. 2. DATA USED 2.2. In situ snow depth measures Our study is based satellite radar altimetry observations In 2004 and 2005, field measurements of snow depth complemented by field measurements. were carried out on Austfonna by the Norwegian Polar Institute (NPI) and University of Oslo by using Ground 2.1. Radar altimetry data Penetrating Radar (GPR) and manual probing. The measurements were performed along transects and We use ENVISAT altimetry data (radar altimeter and allow mapping the distribution of snow accumulation radiometer) over the Austfonna since 1995. Radar across the ice cap (Fig. 5). waveform parameters (backscatter) could provide useful information on the surface conditions such as surface By combining these in situ and satellite observations we roughness, melting and refreezing processes. Beside perform cross-comparison of snow depth estimates and this, passive microwave data from MWR radiometer analyse spatial and temporal distribution of snow depth onboard ENVISAT also provide estimates of the snow and surface conditions of Austfonna. depth. In order to precisely analyse spatial and temporal variability of altimetric measures we need to refer all altimetric measures to the common spatial reference points. As orbit changes across track are much larger than the distance between high-frequency altimetric measures (for example, for ENVISAT across-track changes are 1500 m, while distance between 18 Hz measures is about 400 m, Fig. 4), use of latitude or longitude values for study of temporal and spatial changes would be misleading. A viable solution is to Figure 5. NPI Ground Penetrating Radar (GPR) snow choose a set of unique reference points. This is done by depth (2004) and snow depth map arbitrarily choosing one cycle and defining all high- frequency points in this cycle as the reference points for all other cycles (see Fig. 4). By choosing boundaries as lines located halfway between reference points and 3. RESULTS 3.2. Brightness temperature For brightness temperature (TB) we use a gradient ratio Using ENVISAT altimetric and radiometric measures it (GR) that combines the information from the two is possible to estimate surface conditions and snow available MWR frequencies: properties for Austfonna. Here we present spatial and GR=1000*(TB365- TB238)/(TB238+TB365) (1) temporal distribution and variability of two ENVISAT parameters: a) backscatter coefficient (ICE2 algorithm) High values of this parameter indicate the snow in Ku band and b) brightness temperature from the presence and GR is linearly related with the snow depth. microwave radiometer (MWR) also present onboard Over Austfonna (figure 7) in winter the highest values ENVISAT. We analyse these parameters over are observed over the top of ice dome (GR*1000 is 20- Austfonna in general and over Duvebreen glacier and 30 and more) and they increase westward. This adjacent regions in particular corresponds well to the general pattern of snow accumulation (Figure 9), coming from the Barents Sea 3.1. Backscatter in Ku band. in the east and resulting in the maximal snow depth on For the whole Nordaustlandet (Figure 6) backscatter in the eastern slopes and low snow accumulation on the Ku band shows low values (-5 to 0 dB) on the outer western. boundaries of ice dome, with gradual increase towards the top (5-10 dB). In winter backscatter values are low (maximal values 6-8 dB), but starting from May, snow We have used the NPI GPR measures taken along the melt result in rapid increase of backscatter (values go up west-east profile in April 2005 (Figure 8) to establish to 10-14 dB), with maximum in August-September. the relation between the measured snow depth and Starting from October, snow accumulation brings ENVISAT GR parameter. Using ENVISAT data at the backscatter back to its winter values. High sensitivity of points where ground tracks cross the profile, and backscatter to water makes it possible to reliably detect discarding observations that are more than 2 weeks the timing and extent of melting events. before and after the profile was made, we obtain 7 measures (Figure 9). Figure 6. Seasonal variability of Backscatter (Ku band) parameter of ENVISAT observations over Nordaustlandet Nordaustlandet, Svalbard JAN FEB MAR GR distribution 80.4 ENVISAT data cycles 10 to 40 80.2 (October 2002 - August 2005) e 79.9 ud Year t i t a 80.4 L 79.7 50 79.4 80.2 40 79.2 30 APR MAY JUN 80.4 e 79.9 ud 20 t i 80.2 t a e L 10 79.9 79.7 ud t i 50 t a 0 L 79.7 40 79.4 79.4 -10 30 79.2 -30 20 79.2 JUL AUG SEP 18 20 22 24 26 -50 80.4 10 Longitude 80.2 0 e -10 79.9 ud Summer (Jun-Sep) Winter (Nov-May) t i t a -30 80.4 L 79.7 -50 79.4 80.2 79.2 e 79.9 OCT NOV DEC ud t i 80.4 t a L 79.7 80.2 e 79.9 79.4 ud t i t a L 79.7 79.2 18 20 22 24 26 18 20 22 24 26 79.4 Longitude Longitude 79.2 18 20 22 24 26 18 20 22 24 26 18 20 22 24 26 Longitude Longitude Longitude Figure 7. Seasonal variability of GR*1000 parameter of ENVISAT observations (cycles 10 to 40) over Nordaustlandet. 40 38 2.4 36 2.3 34 2.2 32 2.1 30 2 28 1.9 26 1.8 0 24 0 0 1.7 1 * m 22 R , 1.6 h G t p 20 T e 1.5 A d S 18 I w 1.4 V o N n 16 s 1.3 E R 14 P 1.2 G 1.1 12 1 10 0.9 8 0.8 6 0.7 4 0.6 2 0.5 0 0.4 22 23 24 25 26 27 Figure 8.
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