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dc.date.available
2023-06-13T15:56:56Z
dc.identifier.citation
Tapia Baldis, Carla Cintia; (2023): Permafrost model for the Argentinian Andes - Calibration data set. Consejo Nacional de Investigaciones Científicas y Técnicas. (dataset). http://hdl.handle.net/11336/200479
dc.identifier.uri
http://hdl.handle.net/11336/200479
dc.description.abstract
Supplementary information to the following publication: Tapia Baldis C, Trombotto Liaudat D. 2020. Permafrost debris-model in Central Andes of Argentina (28°-33° S). Cuadernos de Investigación Geográfica 46, http://doi.org/10.18172/cig.3802 ------------------------------------------------------------------------------------------------------------------------- To predict regional-scale spatial patterns of permafrost occurrence, especially over remote environments with limited data, empiric-statistical models are widely used. This kind of approach correlates permafrost occurrence with topo-climatic factors (altitude, geographic position, slope, aspect, air temperature, ground temperature, solar radiation, etc.) easily available, in some cases. Different combinations of empiric-statistical models were tested to evaluate the permafrost spatial distribution in the study area. The study area (28° to 33°S and 70°30’ to 69°W) comprises the middle portion of the South American (Argentinian side) Central Andes (17°30’ to 35°S), named Dry Andes. The landscape is expressed as mountain ranges and valleys with 50% of the terrain surface above 3000 m a.s.l. The highest elevations are represented by mountain peaks such us Mercedario (6850 m a.s.l.) or La Ramada (6400 m a.s.l.). The Dry Andes could be further separated into Desert Andes (17°30’ to 31°S) and Central Andes (31° to 35°S), according to precipitation rates and landscape geomorphological characteristics. Models were trained in a calibration area to evaluate the correlation between geomorphological permafrost indicators (named explanatory variable) and the topoclimatic parameters (predictive variable). A logistic regression model with a logit link function was chosen as a mathematical approach. Data for model calibration was obtained from the Bramadero river basin, located at 31°50’ S and 70°00’ W in the Central Andes. From a geomorphological point of view, the landscape of the Dry Andes is characterized by the interdigitation of glacial, periglacial, alluvial, fluvial, and gravitational processes. The Bramadero river basin was largely glaciated during the LGM, even today it is possible to recognize erosive forms and glacial deposits all over the main valley and subordinated creeks. Even though Quaternary glacial stages modeled the landscape; periglacial features prevail today. Currently, periglacial processes are active in elevations exceeding 2700 m a.s.l. (lowest limit of seasonal freezing), however, a wide variety of periglacial deposits and permafrost indicating cryoforms occur between 3400 and >4500 m a.s.l. (permafrost periglacial belt). The complete geomorphological characterization of the Bramadero river basin and the geomorphometric data extracted from every kind of landform were used to set up the permafrost predictive categories. The first predictive category (presence) includes geoforms that indicate current permafrost, such as; active rock glaciers, inactive rock glaciers, protalus lobes, cryoplanation surfaces, and perennial snow patches. The second category (absence) includes geoforms without current permafrost (relict or fossil rock glaciers, bedrock outcrops, glacial abrasion surfaces, debris/mud flows, and Andean wetlands/peatlands types). It also includes geoforms where the presence of permafrost could not be certainly assessed such us: frozen and unfrozen talus slopes, glaciers and covered glaciers, moraines and morainic complexes, debris/snow avalanches, rock avalanches, and rock slides.
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.title
Permafrost model for the Argentinian Andes - Calibration data set
dc.type
dataset
dc.date.updated
2023-01-18T15:26:47Z
dc.description.fil
Fil: Tapia Baldis, Carla Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
dc.datacite.PublicationYear
2023
dc.datacite.Creator
Tapia Baldis, Carla Cintia
dc.datacite.affiliation
Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales
dc.datacite.publisher
Consejo Nacional de Investigaciones Científicas y Técnicas
dc.datacite.subject
Otras Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.datacite.subject
Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.datacite.subject
CIENCIAS NATURALES Y EXACTAS
dc.datacite.date
27/03/2018
dc.datacite.DateType
Creado
dc.datacite.language
eng
dc.datacite.AlternateIdentifierType
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.5281/zenodo.7229569
dc.datacite.version
1.0
dc.datacite.FundingReference
PIP 12222015-01000913
dc.datacite.FunderName
Consejo Nacional de Investigaciones Científicas y Técnicas
dc.relationtype.isSourceOf
http://hdl.handle.net/11336/200476
dc.subject.keyword
Permafrost
dc.subject.keyword
Periglacial
dc.subject.keyword
Suelos congelados
dc.subject.keyword
Andes
dc.datacite.resourceTypeGeneral
dataset
dc.conicet.datoinvestigacionid
2745
dc.datacite.awardTitle
Caracterización del sistema criosférico y determinación de permafrost andino en los Andes Centrales de San Juan (Argentina)
dc.datacite.geolocation
Calibration area: -33, -69; -28, -71; -33, -71; -28, -69;
dc.datacite.formatedDate
2018
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