Changes of glacier facies on Hornsund glaciers (Svalbard) during the decade 2007–2017

Barzycka, Barbara; Grabiec, Mariusz; Błaszczyk, Małgorzata; Ignatiuk, Dariusz; Laska, Michał; Hagen, Jon Ove; Jania, Jacek

doi:10.1016/j.rse.2020.112060

Cited by 13 publications

(29 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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] .…”

Section: Background and Summarymentioning

confidence: 99%

Hansbreen Snowpit Dataset – over 30-year of detailed snow research on an Arctic glacier

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Hansbreen Snowpit Dataset – over 30-year of detailed snow research on an Arctic glacier

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…and outflows of cold and fresh water from the fjords (Figure 1; Bensi et al 2019). Along the western Spitsbergen coast, there are four major fjords, Hornsund, Bellsund, Isfjorden, and Kongsfjorden (Figure 1), that are characterized by the presence of tidewater glaciers in the inner part (e.g., Strzelecki et al 2018;Halbach et al 2019;Barzycka et al 2020;Szeligowska et al 2021). Also, Storfjorden, which is occupied by glacier systems, strongly impacts the oceanography of Svalbard (Skogseth et al 2008).…”

Section: Study Areamentioning

confidence: 99%

Multiproxy investigation of the last 2,000 years BP marine paleoenvironmental record along the western Spitsbergen margin

Torricella

Sojo

Gariboldi

et al. 2022

Arctic, Antarctic, and Alpine Research

View full text Add to dashboard Cite

A reconstruction of the last 2,000 years BP of environmental and oceanographic changes on the western margin of Spitsbergen was performed using a multidisciplinary approach including the fossil assemblages of diatoms, planktic and benthic foraminifera and calcareous nannofossils and the use of geochemistry (X-ray fluorescence spectroscopy, X-ray diffraction). We identified two warm periods (2,000-1,600 years BP and 1,300-700 years BP) that were associated with the Roman Warm Period and the Medieval Warm Period that alternate with colder oceanic conditions and sea ice coverage occurred during the Dark Ages (1,600-1,300 years BP) and the beginning of the Little Ice Age. During the Medieval Warm Period the occurrence of ice-rafted debris and Aulocoseira spp., a specific diatom genus commonly associated with continental freshwater, suggests significant runoff of meltwaters from local glaciers.

show abstract

“…Some successful studies on glacier zone detection and monitoring in polar regions have been carried out, using analysis of sigma0 and comparison to terrestrial data (e.g., [21][22][23][24][25]). These studies are limited to only a few glaciers and to a single-or dual-pol SAR mode of C-band SAR data.…”

Section: Introductionmentioning

confidence: 99%

“…These studies are limited to only a few glaciers and to a single-or dual-pol SAR mode of C-band SAR data. Barzycka et al [25], in a study of changes in glacier zones on Hornsund glaciers (Spitsbergen, Svalbard), recognized limitations in the K-means unsupervised classification of sigma0 of both single-and dual-polarization SAR images in distinguishing glacier zones. The limitations are related to the thickness of glacier facies, heterogeneity of the ice body, and the local character of a structure.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Changes of glacier facies on Hornsund glaciers (Svalbard) during the decade 2007–2017

Cited by 13 publications

References 84 publications

Hansbreen Snowpit Dataset – over 30-year of detailed snow research on an Arctic glacier

Hansbreen Snowpit Dataset – over 30-year of detailed snow research on an Arctic glacier

Multiproxy investigation of the last 2,000 years BP marine paleoenvironmental record along the western Spitsbergen margin

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

Contact Info

Product

Resources

About