SNOW DEPTH AND SURFACE CONDITIONS OF AUSTFONNA ICE CAP (SVALBARD) USING FIELD OBSERVATIONS AND SATELLITE ALTIMETRY

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1 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, 314 Toulouse, France, kouraev@legos.cnes.fr, legresy@legos.obs-mip.fr, remy@legos.obs-mip.fr 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,,jack.kohler@npolar.no 4) Multiconsult, Fiolveien 13 N-916 Tromsø, Norway, andrea.taurisano@multiconsult.no ABSTRACT We have studied snow cover and surface state of the largest ice cap in the Eurasian Arctic - the Austfonna on the Svalbard. Our study is based on satellite radar altimetry observations combined with the field measurements. We use ENVISAT altimetry data (radar altimeter and radiometer) over the Austfonna since 1995 in combination with GPR (ground penetrating radar) surveys done by the NPI in 25. Radar waveform 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. Nordaustlandet has two main ice caps - Vestfonna (surface 25 km 2 ) and Austfonna, which is the largest ice cap in the Eurasian Arctic (surface 812 km 2 ). Though this Austfonna has been stable during the last 3 years, there are outlet glaciers that have experienced large changes, such as Etonbreen glacier (Fig. 2). Using multitemporal SPOT imagery we have estimated that between April 1987 and March 1998 Etonbreen and its neighbour glacier Basin 3 have lost about 7.5 km 2, giving an average.7 km 2 /year retreat. 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 1. Map of Nordaustlandet Figure 2. Map showing changes in the frontal positions of Wahlenbergfjord outlet glaciers between 1987 and This map is available online at 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) and other types (DTM) data used in the INTEGRAL project. All these stand-alone modules are easy-to-use Proc. Envisat Symposium 27, Montreux, Switzerland April 27 (ESA SP-636, July 27)

2 and easy-to-understand, stand-alone modules, what makes them useful reference tools for scientists and wide public, including schools. They are realised as Adobe flash (requires free plug-in from or a Windows *.exe files, size 1-2 Mb each module. perpendicular to the satellite track, we refer all points that fall between these two boundaries, to the chosen reference point m m m m m m m m m 4 m Figure 3. An information sheet illustrating the content and capabilities of the REGARD database. This sheet and database REGARD are available online at 2. DATA USED Our study is based satellite radar altimetry observations complemented by field measurements Radar altimetry data We use ENVISAT altimetry data (radar altimeter and radiometer) over the Austfonna since Radar waveform parameters (backscatter) could provide useful information on the surface conditions such as surface roughness, melting and refreezing processes. Beside this, passive microwave data from MWR radiometer onboard ENVISAT also provide estimates of the snow 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 15 m, while distance between 18 Hz measures is about 4 m, Fig. 4), use of latitude or longitude values for study of temporal and spatial changes would be misleading. A viable solution is to choose a set of unique reference points. This is done by arbitrarily choosing one cycle and defining all highfrequency 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 Figure 4. Sample of spatial distribution of 18 Hz ENVISAT measures (cycles 1 to 36) over Austfonna. Data from cycle 2 have been chosen as reference points (red dots). Dashed line boundaries for one reference point In situ snow depth measures In 24 and 25, field measurements of snow depth were carried out on Austfonna by the Norwegian Polar Institute (NPI) and University of Oslo by using Ground Penetrating Radar (GPR) and manual probing. The measurements were performed along transects and allow mapping the distribution of snow accumulation across the ice cap (Fig. 5). 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. Figure 5. NPI Ground Penetrating Radar (GPR) snow depth (24) and snow depth map

3 3. RESULTS Using ENVISAT altimetric and radiometric measures it is possible to estimate surface conditions and snow properties for Austfonna. Here we present spatial and temporal distribution and variability of two ENVISAT parameters: a) backscatter coefficient (ICE2 algorithm) in Ku band and b) brightness temperature from the microwave radiometer (MWR) also present onboard ENVISAT. We analyse these parameters over Austfonna in general and over Duvebreen glacier and adjacent regions in particular 3.1. Backscatter in Ku band. For the whole Nordaustlandet (Figure 6) backscatter in Ku band shows low values (-5 to db) on the outer boundaries of ice dome, with gradual increase towards the top (5-1 db). In winter backscatter values are low (maximal values 6-8 db), but starting from May, snow melt result in rapid increase of backscatter (values go up to 1-14 db), with maximum in August-September. Starting from October, snow accumulation brings backscatter back to its winter values. High sensitivity of backscatter to water makes it possible to reliably detect the timing and extent of melting events Brightness temperature For brightness temperature (TB) we use a gradient ratio (GR) that combines the information from the two available MWR frequencies: GR=1*(TB365- TB238)/(TB238+TB365) (1) High values of this parameter indicate the snow presence and GR is linearly related with the snow depth. Over Austfonna (figure 7) in winter the highest values are observed over the top of ice dome (GR*1 is 2-3 and more) and they increase westward. This corresponds well to the general pattern of snow accumulation (Figure 9), coming from the Barents Sea in the east and resulting in the maximal snow depth on the eastern slopes and low snow accumulation on the western. We have used the NPI GPR measures taken along the west-east profile in April 25 (Figure 8) to establish the relation between the measured snow depth and ENVISAT GR parameter. Using ENVISAT data at the points where ground tracks cross the profile, and discarding observations that are more than 2 weeks 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

4 Nordaustlandet, Svalbard GR distribution ENVISAT data cycles 1 to 4 (October 22 - August 25) Year 5 JAN FEB MAR APR MAY JUN JUL AUG SEP 2 1 Summer (Jun-Sep) Winter (Nov-May) OCT NOV DEC Figure 7. Seasonal variability of GR*1 parameter of ENVISAT observations (cycles 1 to 4) over Nordaustlandet. 4 GPR snow depth, m ENVISAT GR*1 Figure 8. NPI ground penetrating radar snow depth measurements for 28-3 April Figure 9. NPI ground penetrating radar snow depth measurements (black crosses, left axis) for 28-3 April 25 and ENVISAT GR*1 parameter (red dots, right axis). NPI GPR measures show an increase of snow depth from Etonbreen (western part of the profile) towards the top of the ice dome (.7 to 1.4 m). Just after the crossing of the ice divide line, the snow depth rapidly increases

5 to m, what is associated with the precipitation coming from the east. Further eastward snow depth decrease to m. Superposition of the two graphs shows that the GR ratio reflects well the distribution of snow depth. Bearing in mind that radiometer footprint is 35 km for 23 GHz and 22 km for 37 GHz, and TB measures are provided for each 1 sec measure (6.8 km distance between measures), GR represent a moving average of the snow depth. This is well illustrated by the point at the ice divide line (Gr*1=23), which integrate data over areas both with low (to the west) and high (to the east) snow depth. Using this comparison, we may estimate that the GR*1 ratio and snow depth (SD, in cm) are related by the following equation: SD=.528*GR*1+.48 (2) By using data from other GPR profiles we plan to provide more accurate and detailed relation between the two parameters. But even at this intermediate stage one may estimate spatial and temporal variability of snow depth. Mean monthly distribution of GR*1 parameter for Duvebreen (not shown here) indicate that for January-May snow depth increases from the lower (GR -1, SD -1 m) to the upper part of the glacier (GR 2-3, SD m). Maximal values are observed for the upper part in March (GR 3-35, SD m). In June snow starts to melt, snow depth decreases to and in July-August GR becomes negative (presence of meltwater on the surface). Snow accumulation starts again in September-October (GR 1-2, SD -1 m), and by December it reaches its maximum annual values (GR 35-4, SD m). Temporal variability of GR and estimated snow depth is presented on Figure 14. As noted before, elevated GR values are associated with tracks crossing higher parts of the glaciers and decrease downward (From Zone I to Zones IV and V, see figure 3). Interannual changes in the snow accumulation are also observed - while winters 22/23 and 24/25 have similar maximal values of snow depth (up to 2.1 m in upper parts), winter 23/24 has values up to 2.6 m and winter 25/26 has the highest values (more than 2.6 m) for the period of observations. This data provide additional independent information for estimation and modelling of mass balance of Austfonna glaciers. V 681 IV III II Figure 1. ENVISAT ground tracks over Duvebreen I GR* Gr_39 Gr_767 Gr_223 Gr_448 Gr_82 Gr_362 Gr_96 Gr_681 Snow depth, m Year Figure 11. Temporal variability of GR*1 parameter for various ENVISAT tracks over Duvebreen (see figure 3 for zones). Zone I - red lines, Zone II - Yellow lines, Zone III - light blue lines, Zones IV and V - dark blue lines

6 5. CONCLUSION Combination of observations from ENVISAT radar altimeter and radiometer with in situ snow depth data enhance our capability to analyse spatial and temporal changes of glacier regime. We plan to expand the dataset by using data from other GPR profiles, as well as to address influence of melting and start of snow accumulation on GR measures. 6. ACKNOWLEDGEMENTS Work supported by the FP6 EC INTEGRAL project (Contract No. SST3-CT ). The altimetry data were obtained from the Centre for Topographic studies of the Oceans and Hydrosphere (CTOH) at the LEGOS laboratory ( SPOT 1-4 imagery for 1987, 1988, 1991, 1993, 1998 are from ISIS project