Variability in wet and dry snow radar zones in the North of the Antarctic Peninsula using a cloud computing environment.


Journal

Anais da Academia Brasileira de Ciencias
ISSN: 1678-2690
Titre abrégé: An Acad Bras Cienc
Pays: Brazil
ID NLM: 7503280

Informations de publication

Date de publication:
2024
Historique:
received: 23 06 2023
accepted: 15 12 2023
medline: 17 7 2024
pubmed: 17 7 2024
entrez: 17 7 2024
Statut: epublish

Résumé

This work investigated the annual variations in dry snow (DSRZ) and wet snow radar zones (WSRZ) in the north of the Antarctic Peninsula between 2015-2023. A specific code for snow zone detection on Sentinel-1 images was created on Google Earth Engine by combining the CryoSat-2 digital elevation model and air temperature data from ERA5. Regions with backscatter coefficients (σ⁰) values exceeding -6.5 dB were considered the extent of surface melt occurrence, and the dry snow line was considered to coincide with the -11 °C isotherm of the average annual air temperature. The annual variation in WSRZ exhibited moderate correlations with annual average air temperature, total precipitation, and the sum of annual degree-days. However, statistical tests indicated low determination coefficients and no significant trend values in DSRZ behavior with atmospheric variables. The results of reducing DSRZ area for 2019/2020 and 2020/2021 compared to 2018/2018 indicated the upward in dry zone line in this AP region. The methodology demonstrated its efficacy for both quantitative and qualitative analyses of data obtained in digital processing environments, allowing for the large-scale spatial and temporal variations monitoring and for the understanding changes in glacier mass loss.

Identifiants

pubmed: 39016361
pii: S0001-37652024000401101
doi: 10.1590/0001-3765202420230704
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e20230704

Auteurs

Filipe D Idalino (FD)

Universidade Federal do Rio Grande do Sul, Instituto de Geociências, Centro Polar e Climático, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.

Kátia K DA Rosa (KKD)

Universidade Federal do Rio Grande do Sul, Instituto de Geociências, Centro Polar e Climático, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.

Fernando L Hillebrand (FL)

Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Sul, IFRS, Rodovia RS-239, Km 68, 3505, 95690-000 Rolante, RS, Brazil.

Jorge Arigony-Neto (J)

Universidade Federal do Rio Grande, Instituto de Oceanografia, Av. Itália, Km 8, 96201-900 Rio Grande, RS, Brazil.

Claudio Wilson Mendes (CW)

Universidade Federal do Rio Grande do Sul, Instituto de Geociências, Centro Polar e Climático, Departamento de Geodésia, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.

Jefferson C Simões (JC)

Universidade Federal do Rio Grande do Sul, Instituto de Geociências, Centro Polar e Climático, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.

Articles similaires

India Carbon Sequestration Environmental Monitoring Carbon Biomass
Animals Cattle Alberta Deer Seasons
Rivers Turkey Biodiversity Environmental Monitoring Animals
1.00
Iran Environmental Monitoring Seasons Ecosystem Forests

Classifications MeSH