Table 2 - uploaded by Michał Laska
Content may be subject to copyright.
Positive degree days (PDD) and their total in successive months of 2014 in Hornsund, on the H4, H6 and H9 Hansbreen sites.
Source publication
In recent years, the Svalbard area, especially its southern section, has been characterised by an exceptionally thin snow cover, which has a significant impact of the annual mass balance of glaciers. The objective of this study was to determine melting processes of the snow cover deposited on 11 glaciers that terminate into Hornsund Fjord during th...
Context in source publication
Context 1
... is particularly evident in the summer, when tundra, which is not insulated by snow, is strongly heated and undergoes intensive evaporation. This causes the number of positive degree days (PDD) to vary significantly from site to site (Table 2). The accumulated total of PDD ranged from 164.3 (H9) to 585.4 (HOR), and positive mean diurnal air temperature was recorded nearly twice as frequently in Hornsund (47% of the year) as in the accumulation zone of Hansbreen (25% of the year). ...
Similar publications
Suspended sediment (SS) is an important water quality indicator of coastal and estuarine ecosystems. Field measurement and satellite remote sensing are the most common approaches for water quality monitoring. However, the efficiency and precision of both methods are typically affected by their sampling strategy (time and interval), especially in hi...
Risk-informed flood-risk assessment for dams requires statistics of Inflow Design Floods, for return periods up to order 1000-10,000 years. Conventional statistical extrapolation of discharge observation records (if available at all) can produce significant statistical uncertainties. Moreover, statistical extrapolation is not suited for situations...
Citations
... Baishui Glacier No. 1. In the Svalbard region, rain-on-snow events significantly affect the structure and melting rate of the snow cover [17]. Furthermore, the interaction of energy on the snow surface is critical for snowmelt [18], with the sublimation of snow caused by solar radiation and the accumulation of dry snow by strong winds being areas of research worth attention [19]. ...
Hourly automatic snow depth stations have enhanced insights into the dynamics and spatial variability of daily snowmelt. From 2021 to 2022, we gathered hourly snow depth measurements from six Hulun Buir grassland stations. Our analysis shed light on the dynamics of snowmelt and the key drivers in this northern region. We found that in northern China’s mid-high latitude grasslands, winter snow cover persists for about 80 to 134 days. The transition to the melting phase in early March spans 5 to 12 days, with continuous and rapid phases. Snow under 3 cm quickly collapses. If the average temperature from 10:00 to 18:00 exceeds 0 °C, complete melting occurs within 36 h. Daily snow melting sees initial stability, swift decline, and gradual reduction, peaking between 11:00 and 14:00. Finally, thermal conditions primarily drive snow melt dynamics, with 14:00 ground temperature being pivotal. These findings shed light on snow dynamics and key factors in the mid-high latitude grasslands of northern China.
... w pasmach Himalajów, Karakorum czy Kunlun Shan (Bhardwaj et al., 2015;Liu et al., 2017;Wang et al., 2017;Chudley i Willis, 2019;Sahu i Gupta, 2019a;Sahu i Gupta, 2019b;Jia et al., 2021). Badania glacjologiczne zdalnymi metodami dotyczą również obszaru Spitsbergenu, czego dowodem mogą być publikacje związane ze zjawiskiem szarży lodowca Aavatsmarka (Sobota et al., 2016), pomiarem prędkości powierzchniowej lodowca Kronebreen (Kääb et al., 2005) oraz oceną stopnia topnienia pokrywy śnieżnej lodowców wypustowych w rejonie Hornsundu (Laska et al., 2017). ...
The main aim of the presented work was to assess Landsat 8 satellite imagery for the presence of cloud cover over the terminal zone of the Aavatsmark Glacier (NW Spitsbergen, Svalbard). The work used all downloadable Landsat 8 imagery taken from the start of the mission (early 2013) to the end of 2020 and covering the entire area of interest (AOI). There were a total of 868 satellite images. The degree of visibility of the AOI zone in each image was calculated using Quality Assessment Band image (QA), which is an integral part of the Landsat 8 dataset. The QA data were reclassified, grouped into specific visibility classes and presented on an annual and monthly basis. An analysis of the incidence of usable imagery, i.e. imagery with no more than 5% cloud cover, was also carried out. Of all the available imagery, over the years analysed, only 176 (approx. 20%) contained a fully visible area, while approx. 60% of the images had more than 95% cloud cover. These data were also compared with the results of cloud cover at the nearest weather station in Ny-Ålesund.
... The fjord is influenced by both Arctic and Atlantic sea currents (Promińska et al., 2017;Jakacki et al., 2017). About 67% of the fjord's surface is covered by tidewater glaciers (11 glaciers; Laska et al., 2017). Glaciers are the primary sources of freshwater to the fjord (approximately 64% of the total freshwater supply, Błaszczyk et al., 2019). ...
... The SI SAR patches occur in the results of all tested methods. Laska et al. [36] indicate that the low slopes, low number of crevasses, and poor englacial drainage system of Storbreen promoted the formation of meltwater ponds in the 2014 ablation season. This could explain the pattern of SI SAR patches, whose origin could be a frozen meltwater pond from the ablation season, which was not drained out from the glacial system before the accumulation season. ...
Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.
... The average equilibrium line altitude (ELA) in 2014 was detected at 342 m a.s.l. [90] On the west side, four tributary glaciers (Fuglebreen, Tuvbreen, Deileggbreen, and Staszelisen) feed the main trunk. The boundary in the east is awkward to define because of the transfluence of ice from the accumulation field to Kvitungisen (a tributary of Paierlbreen) through a glacial breach [89]. ...
This paper explores the potential of ground-penetrating radar (GPR) monitoring for an advanced understanding of snow cover processes and structure. For this purpose, the study uses the Hansbreen (SW Spitsbergen) records that are among the longest and the most comprehensive snow-cover GPR monitoring records available on Svalbard. While snow depth (HS) is frequently the only feature derived from high-frequency radio-echo sounding (RES), this study also offers an analysis of the physical characteristics (grain shape, size, hardness, and density) of the snow cover structure. We demonstrate that, based on GPR data (800 MHz) and a single snow pit, it is possible to extrapolate the detailed features of snow cover to the accumulation area. Field studies (snow pits and RES) were conducted at the end of selected accumulation seasons in the period 2008–2019, under dry snow conditions and HS close to the maximum. The paper shows that although the snow cover structure varies in space and from season to season, a single snow pit site can represent the entire center line of the accumulation zone. Numerous hard layers (HLs) (up to 30% of the snow column) were observed that reflect progressive climate change, but there is no trend in quantity, thickness, or percentage contribution in total snow depth in the study period. HLs with strong crystal bonds create a “framework” in the snowpack, which reduces compaction and, consequently, the ice formation layers slow down the rate of snowpack metamorphosis. The extrapolation of snow pit data through radar profiling is a novel solution that can improve spatial recognition of snow cover characteristics and the accuracy of calculation of snow water equivalent (SWE).
... It includes all 79 available snow profiles collected on Hansbreen in 1989-2021. These were performed: to calibrate mass balance measurements and modelling; 19,[25][26][27][28][29][30][31] to estimate the snow cover depth using indirect methods, such as ground-penetrating radar (GPR) [32][33][34] and remote sensing [35][36][37] ; to determine the evolution of the seasonal snow cover 17,18,[38][39][40] ; and to calculate concentration and fluxes of pollutants deposited in the snowpack 23,[41][42][43][44] . ...
... Grain shape, after Fierz et al. 11 , primarily described the main morphological classes, except single profiles, in which the observer used a detailed division into subclasses (2014, 2015, 2018, 2019). The only subclass, often specified even in the basic classification, was Melt-freeze crusts (MFcr), distinguished due to their significant influence on melting dynamics 35,38 , circulation of water 50 , heat and matter transfer in the snow cover 51 , avalanche safety 52 and relationship with rain-on-snow episodes 39 . Detailed information on unification from different classifications of grain shape is provided in the Background & Summary section and in Table 1. ...
Snow cover is a key element in the water cycle, global heat balance and in the condition of glaciers. Characterised by high temporal and spatial variability, it is subject to short- and long-term changes in climatic conditions. This paper presents a unique dataset of snow measurements on Hansbreen, an Arctic glacier in Svalbard. The dataset includes 79 archived snow profiles performed from 1989 to 2021. It presents all available observations of physical properties for snow cover, such as grain shape and size, hardness, wetness, temperature and density, supplemented with organised metadata. All data has been revised and unified with current protocols and the present International Classification for Seasonal Snow on the Ground, allowing comparison of data from different periods and locations. The information included is essential for estimations of glacier mass balance or snow depth using indirect methods, such as ground-penetrating radar. A wide range of input data makes this dataset valuable to the greater community involved in the study of snow cover evolution and modelling related to glaciology, ecology and hydrology of glacierised areas.
... Rhdomonas salina, one of the cryptophytes, can grow from below 5 to 50 PSU (Klaveness, 1989) and live in diverse estuarine habitats (Yoo et al., 2017) implying that cryptophytes have a strong tolerance to salinity change. In addition, the peak melting times of glacier snow in the Svalbard fjords are from July to August, leaving only approximately 43% of the total snow by the end of August (Laska et al., 2017). This enhanced glacier melting leads to a decrease in salinity of 22.76 PSU, which matches the optimal salinity range for cryptophytes. ...
Glacial melting and massive spring blooms caused by global warming have significantly altered the environmental conditions in the Svalbard fjords of the European Arctic. These changes included reduced salinity (the gradient of salinity from inner to outer fjords, ranging from 23 to 34 PSU), stratification of the water column, increased turbidity (>135 FTU), low nutrient conditions (0.06–1.13 μM PO4⁻, 1.19–3.54 μM NO3⁻, 1.19–3.54 μM NH4⁺, and −2.1 to 0.9 N*), reduced light penetration, and release of organic matter, resulting in changes in the structure and composition of the phytoplankton. Our study, conducted in Isfjorden, van Mijenfjorden/Bellsund, and Hornsund of Svalbard in early August 2019, observed the dominance of cryptophytes in the phytoplankton composition after the spring bloom. Our results show a different phenomenon from the previous diatom/dinoflagellate dominance in the late 1970s and the early 2020s. Changes in phytoplankton composition can be explained as follows. (1) The excessive consumption of nutrients during spring bloom and the reduction of nutrient mixing in the water column stratification due to glacier melting has formed nutrient-depleted conditions, providing favorable conditions for the small-sized phytoplankton that easily find nutrients. (2) A wide range of salinities has created beneficial conditions for cryptophytes, capable of controlling osmotic stress against various salinities, of surviving compared to diatoms and dinoflagellates. (3) Finally, the influx of organic matter into fjords due to glacier melting can increase turbidity and decrease light availability; therefore, cryptophytes with mixotrophic metabolisms could be more viable than diatoms with only autotrophic metabolisms. In summary, the dynamic environmental conditions after enhanced spring bloom and glacier melting will further alter phytoplankton compositions and, in turn, influence food webs at higher tropical levels in European Arctic fjord ecosystems.
... This stems well with observations of Blaszczyk et al. [19] and Schuler et al. [10], who reported high velocities only on Hambergbreen and the lower part of Sykorabreen-the glacier with largest surface elevation losses. The mass loss across all parts of Hambergbreen, despite its snow line of 398 m a.s.l. as identified by Laska et al. [49], might also be explained by high flow velocities moving the ice mass downglacier from the accumulation area. The redistribution of snow due to the combination of terrain morphology and prevailing wind may also be an important factor [21,50]. ...
Tidewater glaciers on the east coast of Svalbard were examined for surface elevation
changes and retreat rate. An archival digital elevation model (DEM) from 1970 (generated from aerial images by the Norwegian Polar Institute) in combination with recent ArcticDEM were used to compare the surface elevation changes of eleven glaciers. This approach was complemented by a retreat rate estimation based on the analysis of Landsat and Sentinel-2 images. In total, four of the 11 tidewater glaciers became land-based due to the retreat of their termini. The remaining tidewater glaciers retreated at an average annual retreat rate of 48 m year1, and with range between 10–150 m year1. All the glaciers studied experienced thinning in their frontal zones with maximum surface elevation loss exceeding 100min the ablation areas of three glaciers. In contrast to the massive retreat and thinning of the frontal zones, a minor increase in ice thickness was recorded in some accumulation areas of the glaciers, exceeding 10 m on three glaciers. The change in glacier geometry suggests an important shift in glacier dynamics over the last 50 years, which very likely reflects the overall trend of increasing air temperatures. Such changes in glacier geometry are common at surging glaciers in their quiescent phase. Surging was detected on two glaciers studied, and was documented by the glacier front readvance and massive surface thinning in high elevated areas.
... The FWF in the central part of the fjord and at the fjord entrance is the lowest and oscillates around 0%. The highest FWF in Hansbukta and Brepollen was observed in July, which agrees with the time of highest glacier ablation (Laska et al., 2017b). ...
... In general, metal concentrations peak in the summer period (Fig. 7). The highest concentrations of dissolved Pb, Zn and Cu which correlate with each other were noted at the end of June, the period when very strong melting of snow cover from land and glaciers takes place (Laska et al., 2017b). Interestingly, the peak of dissolved Pb, Zn, and Cu concentrations is not contemporaneous with the peak of particulate Pb, Zn, and Cu concentrations, which appears later, in July. ...
The temporal and spatial variability of heavy metal distribution was studied in an Arctic fjord (Hornsund, Spitsbergen). Seawater from 8 sampling stations and 3 sampling depths was collected in 6 successive months and used for measurement of dissolved and particulate heavy metal concentrations. Salinity and temperature profiles were determined prior to sampling and water masses were classified according to their properties. Isotopic lead composition (²⁰⁶Pb/²⁰⁷Pb and ²⁰⁶Pb/²⁰⁸Pb ratios) was studied to find the sources of Pb to the fjord seawater. Hornsund seawater was contaminated with the studied heavy metals (particularly during the summer months). Extremely high contamination with Cd was measured (dissolved up to 488 ng·L⁻¹, while particulate up to 303 ng·L⁻¹), which is most probably connected to high atmospheric deposition. Depending on the season and the region, metal distribution was modified by glacier meltwater and surface run-off discharges, melting of fast ice, direct atmospheric deposition, transport of sea salt, intrusion of Atlantic water, sediment re-suspension, as well as re-mobilization.
... Glacier zones in Svalbard have been detected by analysis of multispectral images during the ablation season (e.g. Pope and Rees, 2014;Wójcik and Sobota, 2020), also for glaciers in Hornsund (Laska et al., 2017a). However, for detection of annual changes of glaciers zones and possible support of mass balance studies, a good quality, cloud-free image acquired at the end of ablation season is necessary. ...
... In 2010 the glacier covered an area of 53.9 km 2 (Błaszczyk et al., 2013) with a mean ice thickness of 171 m and volume ~ 9.6 km 3 (for 2004: Grabiec et al., 2012). The average Equilibrium-Line Altitude (ELA) of Hansbreen was detected at 342 m a.s.l. in 2014 (Laska et al., 2017a). The snow accumulation at the main trunk of the glacier is asymmetrical, favouring the western over the eastern side (e.g. ...
... The ELA in 2014 was located at 383 m a.s.l. (Laska et al., 2017a). Storbreen's average frontal velocity in 2014 was estimated at 132 m a −1 (Błaszczyk et al., 2019b). ...
Changes in glacier facies (glacier zones), such as firn or superimposed ice (SI), are good indicators of glacier response to climate change. They are especially important for fast-warming Svalbard, where only a few glaciers are under glaciological mass balance monitoring. This paper presents a first study of changes of glacier facies extent for three tidewater glaciers located in southern Spitsbergen (Svalbard) and it is based on both satellite remote sensing and terrestrial data analysis, covering two time spans: 2007–2017 for Hansbreen and 2012–2017 for Storbreen and Hornbreen. Satellite remote sensing analysis include unsupervised classification of Synthetic Aperture Radar (SAR) data from both decommissioned (ENVISAT ASAR) and modern satellite missions (RADARSAT-2, Sentinel-1). The results of the SAR classification are compared to the information on glacier zones retrieved from terrestrial data, i.e. shallow cores and visual interpretation of 800 MHz Ground Penetrating Radar (GPR) profiles. In addition, a novel application of the Internal Reflection Power (IRP) coefficient as an objective method of distinguishing glacier zones based on GPR data is discussed. Changes in glacier facies areas over time are analysed, as well as their correlation to Hansbreen's mass balance. The main finding of the study is that firn and SI of Hansbreen, Storbreen and Hornbreen significantly decreased over the study period. For example, due to continuous negative mass balance between 2010 and 2017, the contribution of firn area to Hansbreen's total area decreased ca. 14% (cumulative firn area loss during that time: ~45%) whereas since 2012 SI has not been distinguished as a vast area on this glacier. In addition, an east–west gradient of firn area loss was observed as a result of differences in local climate conditions. Therefore, for the common time span (i.e. 2012–2017) Hansbreen recorded a ca. 12% loss of firn contribution to glacier area whereas Hornbreen recorded ca. 9%. Finally, application of the IRP coefficient as an objective method of glacier zones discrimination by GPR data gave very good results, so the method is recommended for future analysis of glacier zones instead, or as a support, to popular visual interpretation of the GPR profiles.
