Distribution Of Snow Accumulation Research Articles

NUMERICAL STUDY ON THE INFLUENCE OF SLOPE AND SNOW-FENCE ON THE FLOW FIELD AROUND EMBANKMENT

The side slope of an embankment is an important factor affecting the flow field around the embankment. A snow-fence is a common snow prevention measurement in the road blowing snow disaster. It is beneficial to improve the distribution of snow accumulation around an embankment by changing the slope of the embankment and reasonably setting up the snow-fence in the windward. The flow field characteristic of an embankment with different slopes was studied using a numerical simulation method and the snow distribution of the embankment was analyzed. On the basic of this, the snow prevention effect of the snow-fence in different rows for the embankment was studied by installing snow-fences on the windward of the embankment. The result shows that the characteristics of the flow around the embankment are closely related to the slope and the range of wind speed weakening area formed due to the flow passing through the embankment is proportional to slope. By setting snow-fences with a reasonable parameter in a legitimate position, the flow field around the embankment can be improved and the snow drifting can be weakened to come into the effect of snow prevention.

Estimation of Snow Accumulation on Samudra Tapu Glacier, Western Himalaya Using Airborne Ground Penetrating Radar

In this study an airborne ground penetrating radar (GPR) is used to estimate spatial distribution of snow accumulation in the Samudra Tapu glacier (the Great Himalayan Range), Western Himalaya, India. An impulse radar system with 350 MHz antenna was mounted on a helicopter for the estimation of snow depth. The dielectric properties of snow were measured at a representative site (Patseo Observatory) using a snow fork to calibrate GPR data. The snow depths estimated from GPR signal were found to be in good agreement with those measured on ground with an absolute error of 0.04 m. The GPR survey was conducted over Samudra Tapu glacier in March 2009 and 2010. A kriging-based geostatistical interpolation method was used to generate a spatial snow accumulation map of the glacier with the GPR-collected data. The average accumulated snow depth and snow water equivalent (SWE) for a part of the glacier were found to be 2.23 m and 0.624 m for 2009 and 2.06 m and 0.496 m for 2010 respectively. Further, the snow accumulation data were analysed with various topographical parameters such as altitude, aspect and slope. The accumulated snow depth showed good correlation with altitude, having correlation coefficient varying between 0.57 and 0.84 for different parts of the glacier. Higher snow accumulation was observed in the north- and east-facing regions, and decrease in snow accumulation was found with an increase in the slope of the glacier. Thus, in this study we generate snow accumulation/SWE information using airborne GPR in the Himalayan terrain.

Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy.

The distribution of forest cover exerts strong controls on the spatiotemporal distribution of snow accumulation and snowmelt. The physical processes that govern these controls are poorly understood given a lack of detailed measurements of snow states. In this study, we address one of many measurement gaps by using contact spectroscopy to measure snow optical grain size at high spatial resolution in trenches dug between tree boles in a subalpine forest. Trenches were collocated with continuous measurements of snow depth and vertical profiles of snow temperature and supplemented with manual measurements of snow temperature, geometric grain size, grain type, and density from trench walls. There was a distinct difference in snow optical grain size between winter and spring periods. In winter and early spring, when facetted snow crystal types were dominant, snow optical grain size was 6% larger in canopy gaps versus under canopy positions; a difference that was smaller than the measurement uncertainty. By midspring, the magnitude of snow optical grain size differences increased dramatically and patterns of snow optical grain size became highly directional with 34% larger snow grains in areas south versus north of trees. In winter, snow temperature gradients were up to 5–15°C m−1 greater under the canopy due to shallower snow accumulation. However, in canopy gaps, snow depths were greater in fall and early winter and therefore more significant kinetic growth metamorphism occurred relative to under canopy positions, resulting in larger snow grains in canopy gaps. Our findings illustrate the novelty of our method of measuring snow optical grain size, allowing for future studies to advance the understanding of how forest and meteorological conditions interact to impact snowpack evolution.

Snow accumulation on a small high‐arctic glacier svenbreen: variability and topographic controls

One of the main controls on the net mass change of land‐terminating Arctic glaciers is the magnitude and distribution of snow accumulation. In Dickson Land, region of Svalbard with the greatest distance to the sea, the issue has not been receiving much scientific attention for decades. In this paper, new snow accumulation data are presented from Svenbreen in Dickson Land from end‐of‐winter surveys. The measured winter balance was 0.42 ± 0.15 m w.e. in 2010, 0.50 ± 0.10 m w.e. in 2011 and 0.62 ± 0.10 cm w.e. in 2012. Snow depth and water equivalent have been analysed in the background of altitude, slope and aspect extracted from the digital elevation model of the glacier. On steep northern slopes (>15°) accumulation was the highest, whereas it was decreased on southern slopes with moderate inclination (9–12°). Elevation, which on many glaciers proved to be highly correlated with snow depth, explained only 17–34% of snow depth variability due to complex interplay between local climate and geometry of a small valley.

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

Abstract. The storage of water within the seasonal snow cover is a substantial source of runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally, only a small number of precipitation measurements deliver precipitation input for modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by −0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution.

Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

AbstractSnow accumulation is an important component of the mass balance of alpine glaciers. To improve our understanding of the processes related to accumulation and their representation in state-of-the-art mass-balance models, extensive field measurements are required. We present measurements of snow accumulation distribution on Findelengletscher, Switzerland, for April 2010 using (1) in situ snow probings, (2) airborne ground-penetrating radar (GPR) and (3) differencing of two airborne light detection and ranging (lidar) digital elevation models (DEMs). Calculating high-resolution snow depth from DEM-differencing requires careful correction for vertical ice-flow velocity and densification in the accumulation area. All three methods reveal a general increase in snow depth with elevation, but also a significant small-scale spatial variability. Lidar-differencing and in situ snow probings show good agreement for the mean specific winter balance (0.72 and 0.78 m w.e., respectively). The lidar-derived distributed snow depth reveals significant zonal correlations with elevation, slope and curvature in a multiple linear regression model. Unlike lidar-differencing, GPR-derived snow depth is not affected by glacier dynamics or firn compaction, but to a smaller degree by snow density and liquid water content. It is thus a valuable independent data source for validation. The simultaneous availability of the three datasets facilitates the comparison of the methods and contributes to a better understanding of processes that govern winter accumulation distribution on alpine glaciers.

Snow accumulation of a high alpine catchment derived from LiDAR measurements

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.

Snowpack characteristics of vestfonna and de geerfonna (nordaustlandet, svalbard) – a spatiotemporal analysis based on multiyear snow‐pit data

Möller, M., Möller, R., Beaudon, É., Mattila, O.‐P., Finkelnburg, R., Braun, M., Grabiec, M., Jonsell, U., Luks, B., Puczko, D., Scherer, D. and Schneider, C., 2011. Snowpack characteristics of Vestfonna and De Geerfonna (Nordaustlandet, Svalbard) – a spatiotemporal analysis based on multiyear snow‐pit data. Geografiska Annaler, Series A: Physical Geography, 93, 273–285. DOI: 10.1111/j.1468‐0459.2011.00440.xExtensive glaciological field measurements were carried out on the ice cap Vestfonna as well as on the minor ice body De Geerfonna (Nordaustlandet, Svalbard) within the framework of IPY Kinnvika. Field campaigns were conducted during the period 2007–2010 in spring (April/May) and summer (August). In this study we compile and present snow cover information obtained from 22 snow pits that were dug on Vestfonna during this period. Locations are along two transects on the northwestern, land terminating slope of the ice cap, on its central summit, Ahlmann Summit, and at a set of several other locations in the eastern and northern part of the ice cap. Snow‐cover information acquired from four snow pits on adjacent De Geerfonna is also incorporated in this study. Field data are analysed regarding snow stratigraphy, snow density, snow hardness and snow temperature. Results reveal mean snow densities of around 400 kg m−3 for the snowpack of Vestfonna with no apparent spatial or interannual variability. A distinctly higher value of more than 450 kg m−3 was obtained for De Geerfonna. A spatial comparison of snow water equivalents above the previous end‐of‐summer surface serves for obtaining insights into the spatial distribution of snow accumulation across Vestfonna. Altitude was found to be the only significant spatial parameter for controlling snow accumulation across the ice cap.

The importance of wind-blown snow redistribution to snow accumulation on Bellingshausen Sea ice

AbstractSnow distribution is a dominating factor in sea-ice mass balance in the Bellingshausen Sea, Antarctica, through its roles in insulating the ice and contributing to snow-ice production. the wind has long been qualitatively recognized to influence the distribution of snow accumulation on sea ice, but the relative importance of drifting and blowing snow has not been quantified over Antarctic sea ice prior to this study. the presence and magnitude of drifting snow were monitored continuously along with wind speeds at two sites on an ice floe in the Bellingshausen Sea during the October 2007 Sea Ice Mass Balance in the Antarctic (SIMBA) experiment. Contemporaneous precipitation measurements collected on board the RVIB Nathaniel B. Palmer and accumulation measurements by automated ice mass-balance buoys (IMBs) allow us to document the proportion of snowfall that accumulated on level ice surfaces in the presence of high winds and blowing-snow conditions. Accumulation on the sea ice during the experiment averaged <0.01 m w.e. at both IMB sites, during a period when European Centre for Medium-Range Weather Forecasts analyses predicted >0.03 m w.e. of precipitation on the ice floe. Accumulation changes on the ice floe were clearly associated with drifting snow and high winds. Drifting-snow transport during the SIMBA experiment was supply-limited. Using these results to inform a preliminary study using a blowing-snow model, we show that over the entire Southern Ocean approximately half of the precipitation over sea ice could be lost to leads.

Snow accumulation distribution inferred from time‐lapse photography and simple modelling

AbstractThe spatial and temporal distribution of snow accumulation is complex and significantly influences the hydrological characteristics of mountain catchments. Many snow redistribution processes, such as avalanching, slushflow or wind drift, are controlled by topography, but their modelling remains challenging. In situ measurements of snow accumulation are laborious and generally have a coarse spatial or temporal resolution. In this respect, time‐lapse photography shows itself as a powerful tool for collecting information at relatively low cost and without the need for direct field access. In this paper, the snow accumulation distribution of an Alpine catchment is inferred by adjusting a simple snow accumulation model combined with a temperature index melt model to match the modelled melt‐out pattern evolution to the pattern monitored during an ablation season through terrestrial oblique photography. The comparison of the resulting end‐of‐winter snow water equivalent distribution with direct measurements shows that the achieved accuracy is comparable with that obtained with an inverse distance interpolation of the point measurements. On average over the ablation season, the observed melt‐out pattern can be reproduced correctly in 93% of the area visible from the fixed camera. The relations between inferred snow accumulation distribution and topographic variables indicate large scatter. However, a significant correlation with local slope is found and terrain curvature is detected as a factor limiting the maximal snow accumulation. Copyright © 2010 John Wiley & Sons, Ltd.

Ecohydrological controls on snowmelt partitioning in mixed‐conifer sub‐alpine forests

AbstractWe used co‐located observations of snow depth, soil temperature, and moisture and energy fluxes to monitor variability in snowmelt infiltration and vegetation water use at mixed‐conifer sub‐alpine forest sites in the Valles Caldera, New Mexico (3020 m) and on Niwot Ridge, Colorado (3050 m). At both sites, vegetation structure largely controlled the distribution of snow accumulation with 29% greater accumulation in open versus under‐canopy locations. Snow ablation rates were diminished by 39% in under‐canopy locations, indicating increases in vegetation density act to extend the duration of the snowmelt season. Similarly, differences in climate altered snow‐season duration, snowmelt infiltration and evapotranspiration. Commencement of the growing season was coincident with melt‐water input to the soil and lagged behind springtime increases in air temperature by 12 days on average, ranging from 2 to 33 days under warmer and colder conditions, respectively. Similarly, the timing of peak soil moisture was highly variable, lagging behind springtime increases in air temperature by 42 and 31 days on average at the Colorado and New Mexico sites, respectively. Latent heat flux and associated evaporative loss to the atmosphere was 28% greater for the year with earlier onset of snowmelt infiltration. Given the large and variable fraction of precipitation that was partitioned into water vapour loss, the combined effects of changes in vegetation structure, climate and associated changes to the timing and magnitude of snowmelt may have large effects on the partitioning of snowmelt into evapotranspiration, surface runoff and ground water recharge. Copyright © 2009 John Wiley & Sons, Ltd.

Distribution of snow accumulation on the Svartisen ice cap, Norway, assessed by a model of orographic precipitation

AbstractWe apply a linear model of orographic precipitation (LT model) to estimate snow accumulation on the western Svartisen ice cap (220 km2) in northern Norway. This model combines 3D airflow dynamics with simple parameterizations of cloud physics. The model is forced by large‐scale atmospheric input variables taken from the ECMWF Re‐analysis (ERA‐40) of the European Center for Medium Range Weather Forecast (ECMWF), and the model parameters are kept constant for the entire simulation period, after optimization. The domain covers a 120 × 125 km area surrounding the ice cap. The model is run using a 1‐km resolution digital elevation model, and 6‐h time steps over the period from 1958 to 2002. Precipitation data from surrounding meteorological stations and winter glacier mass balance measurements on several glaciers within the region are used to evaluate the model results. Precipitation obtained from the LT model agrees well with observations from precipitation gauges and there is also fair agreement between model results and specific winter mass balance observations on the ice cap if these are corrected for winter rain. The LT model reproduces well the spatial pattern of winter accumulation across the ice cap as well as the area‐averaged winter mass balances of several other glaciers in the region. This indicates that it is a useful tool in providing high‐resolution, deterministic estimates of precipitation in complex terrain as required for distributed hydrological and/or glacier mass balance modelling of unmeasured areas. Copyright © 2008 John Wiley & Sons, Ltd.

Effects of Spatial Aggregation of Initial Conditions and Forcing Data on Modeling Snowmelt Using a Land Surface Scheme

Abstract Small-scale topography and snow redistribution have important effects on snow-cover heterogeneity and the timing, rate, and duration of spring snowmelt in mountain tundra environments. However, land surface schemes (LSSs) are usually applied as a means to provide large-scale surface states and vertical fluxes to atmospheric models and do not normally incorporate topographic effects or horizontal fluxes in their calculations A study was conducted in Granger Creek, an 8-km2 catchment within Wolf Creek Research Basin in the Yukon Territory, Canada, to examine whether inclusion of the effects of wind redistribution of snow between landscape units, and slope and aspect in snowmelt calculations for tiles, could improve the simulation of snowmelt by an LSS. Measured snow accumulation, reflecting overwinter wind redistribution of snow, was used to provide initial conditions for the melt simulation, and physically based algorithms from a small-scale hydrological model were used to calculate radiation on slopes during melt. Based on consideration of the spatial distribution of snow accumulation, topography, and shrub cover in the basin, it was divided into five landscapes units (tiles) for simulation of mass and energy balance using an LSS during melt. Effects of averaging initial conditions and forcing data on LSS model performance were contrasted against distributed simulations. Results showed that, in most of the cases, simulations using aggregated initial conditions and forcing data gave unsuccessful descriptions of snow ablation whereas the incorporation of both snow-cover redistribution and slope and aspect effects in an LSS improved the prediction of snowmelt rate, timing, and duration.

Simulation of seasonal snow-cover distribution for glacierized sites on Sonnblick, Austria, with the Alpine3D model

AbstractA detailed model of Alpine surface processes is used to simulate the amount of preferential deposition as well as redistribution of snow due to snowdrift for two alpine glaciers (Goldbergkees and Kleinfleißkees, Austrian Alps). The sequence of snow-cover modelling consists of the simulation of the wind field with a mesoscale atmospheric model, a three-dimensional finite-element drift module, an energy-balance module and a snowpack module. All modules with the exception of the wind-field model are integrated within the Alpine3D model frame. The drift module of Alpine3D distinguishes between saltation and suspension and is able to capture preferential deposition of snow precipitation and redistribution of previously deposited snow. Validation of the simulated snow depth is done using the spatially dense snow-probing dataset collected during a campaign in May 2003. Simulated snow depths agree with measurements during winter 2002/03 at locations with detailed snow-height monitoring, taking into account the high spatial variability of snow depth on the glacier. Moreover, comparison of snow accumulation from model results with detailed probing on 1 May 2003 for the total glacier area shows that Alpine3D is able to capture major patterns of spatial distribution of snow accumulation. For the first time, the Alpine3D approach of using high-resolution wind fields from a meteorological model and a physical description of snow transport could be validated for a very steep glacierized area and for a full accumulation season. The results show that drift is a dominant factor to be considered for detailed glacier mass balances. Another dominant factor not considered in this study may be snow redistribution due to avalanches.

Historical behaviour of Dome C and Talos Dome (East Antarctica) as investigated by snow accumulation and ice velocity measurements

Ice divide–dome behaviour is used for ice sheet mass balance studies and interpretation of ice core records. In order to characterize the historical behaviour (last 400 yr) of Dome C and Talos Dome (East Antarctica), ice velocities have been measured since 1996 using a GPS system, and the palaeo-spatial variability of snow accumulation has been surveyed using snow radar and firn cores. The snow accumulation distribution of both domes indicates distributions of accumulation that are non-symmetrical in relation to dome morphology. Changes in spatial distributions have been observed over the last few centuries, with a decrease in snow accumulation gradient along the wind direction at Talos Dome and a counter-clockwise rotation of accumulation distribution in the northern part of Dome C. Observations at Dome C reveal a significant increase in accumulation since the 1950s, which could correlate to altered snow accumulation patterns due to changes in snowfall trajectory. Snow accumulation mechanisms are different at the two domes: a wind-driven snow accumulation process operates at Talos Dome, whereas snowfall trajectory direction is the main factor at Dome C. Repeated GPS measurements made at Talos Dome have highlighted changes in ice velocity, with a deceleration in the NE portion, acceleration in the SW portion and migration of dome summit, which are apparently correlated with changes in accumulation distribution. The observed behaviour in accumulation and velocity indicates that even the most remote areas of East Antarctica have changed from a decadal to secular scale.

The distribution of snow accumulation across the Austfonna ice cap, Svalbard: direct measurements and modelling

We present an analysis of the spatial variability in the snow accumulation on the Austfonna ice cap in Svalbard, Norway, based on the results of field investigations conducted in the spring of 1999, 2004 and 2005. During the campaigns ground penetrating radar measurements at 500 and 800 MHz were collected along profiles, along with additional manual snow sounding and pit stratigraphy work. The analysis of the data reveals a consistent pattern in the spatial distribution of the snow accumulation over the ice-cap, and therefore enables the compilation of a multiple regression model of the snow distribution across the ice cap. Once validated, the results of the model complement the information derived from direct measurements, with an accumulation index for every point on the ice cap, thus enhancing the accuracy of future mass balance studies and dynamic modelling of Austfonna. As very few direct meteorological measurements are performed in the eastern part of Svalbard, the accumulation measurements on the Austfonna ice cap provide valuable integrated information about winter precipitation in this region.

The distribution of snow accumulation across the Austfonna ice cap, Svalbard: direct measurements and modelling

We present an analysis of the spatial variability in the snow accumulation on the Austfonna ice cap in Svalbard, Norway, based on the results of field investigations conducted in the spring of 1999, 2004 and 2005. During the campaigns ground penetrating radar measurements at 500 and 800 MHz were collected along profiles, along with additional manual snow sounding and pit stratigraphy work. The analysis of the data reveals a consistent pattern in the spatial distribution of the snow accumulation over the ice-cap, and therefore enables the compilation of a multiple regression model of the snow distribution across the ice cap. Once validated, the results of the model complement the information derived from direct measurements, with an accumulation index for every point on the ice cap, thus enhancing the accuracy of future mass balance studies and dynamic modelling of Austfonna. As very few direct meteorological measurements are performed in the eastern part of Svalbard, the accumulation measurements on the Austfonna ice cap provide valuable integrated information about winter precipitation in this region.

Calibrating a surface mass-balance model for Austfonna ice cap, Svalbard

AbstractAustfonna (8120km2) is by far the largest ice mass in the Svalbard archipelago. There is considerable uncertainty about its current state of balance and its possible response to climate change. Over the 2004/05 period, we collected continuous meteorological data series from the ice cap, performed mass-balance measurements using a network of stakes distributed across the ice cap and mapped the distribution of snow accumulation using ground-penetrating radar along several profile lines. These data are used to drive and test a model of the surface mass balance. The spatial accumulation pattern was derived from the snow depth profiles using regression techniques, and ablation was calculated using a temperature-index approach. Model parameters were calibrated using the available field data. Parameter calibration was complicated by the fact that different parameter combinations yield equally acceptable matches to the stake data while the resulting calculated net mass balance differs considerably. Testing model results against multiple criteria is an efficient method to cope with non-uniqueness. In doing so, a range of different data and observations was compared to several different aspects of the model results. We find a systematic underestimation of net balance for parameter combinations that predict observed ice ablation, which suggests that refreezing processes play an important role. To represent these effects in the model, a simple PMAX approach was included in its formulation. Used as a diagnostic tool, the model suggests that the surface mass balance for the period 29 April 2004 to 23 April 2005 was negative (–318mmw.e.).

Interannual variability in the spatial distribution of winter accumulation at a high-Arctic glacier (Finsterwalderbreen, Svalbard), and its relationship with topography

AbstractGlacier mass balance and hydrology are strongly influenced by the distribution of snow accumulation at the start of the melt season. Two successive end-of-winter snow-cover surveys at Finsterwalderbreen, Svalbard, are here used to investigate the interannual variability in the spatial distribution of accumulation, and its relationship with topography. 40–62% of the variance in snow depth was not determined by elevation (assessed by linear regression of snow depth on surface elevation), which could not therefore necessarily be used as a sole predictor of the spatial distribution of accumulation here. Principal components (PC) analysis of the topographic variables elevation, slope, north–south and east–west aspects shows that only two of six PCs, determined for 2years’ sampling locations, had maximum loadings on altitude; aspect was more important, with maximum loadings on four PCs. Hierarchical cluster analysis was then applied to these PCs: significant correlations with accumulation in each of two terrain clusters were given by (1) elevation and slope, (2) east–west aspect only (1999); (1) elevation only, (2) no significant correlations (2000). There is strong interannual variability not only in the magnitude of winter accumulation (0.41 mw.e. in 1999, 0.58 mw.e. in 2000), but also in its spatial distribution, and its relationship with topography.

Geophysical survey at Talos Dome, East Antarctica: the search for a new deep-drilling site

AbstractTalos Dome is an ice dome on the edge of the East Antarctic plateau; because accumulation is higher here than in other domes of East Antarctica, the ice preserves a good geochemical and palaeoclimatic record. A new map of the Talos Dome area locates the dome summit using the global positioning system (GPS) (72˚47’ 14’’S, 159˚04’ 2’’ E; 2318.5m elevation (WGS84)). A surface strain network of nine stakes was measured using GPS. Data indicate that the stake closest to the summit moves south-southeast at a few cm a–1. The other stakes, located 8 km away, move up to 0.33ma–1. Airborne radar measurements indicate that the bedrock at the Talos Dome summit is about 400m in elevation, and that it is covered by about 1900 m of ice. Snow radar and GPS surveys show that internal layering is continuous and horizontal in the summit area (15 km radius). The depth distribution analysis of snow radar layers reveals that accumulation decreases downwind of the dome (north-northeast) and increases upwind (south-southwest). The palaeomorphology of the dome has changed during the past 500 years, probably due to variation in spatial distribution of snow accumulation, driven by wind sublimation. In order to calculate a preliminary age vs depth profile for Talos Dome, a simple one-dimensional steady-state model was formulated. This model predicts that the ice 100m above the bedrock may cover one glacial–interglacial period.