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100.1.#.a: Pineda-martínez, Luis F.; Carbajal, Noel

524.#.#.a: Pineda-martínez, Luis F., et al. (2017). Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis. Atmósfera; Vol. 30 No. 3, 2017; 233-242. Recuperado de https://repositorio.unam.mx/contenidos/11302

245.1.0.a: Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

264.#.0.c: 2017

264.#.1.c: 2017-06-30

653.#.#.a: Hierarchical clustering analysis; principal component analysis; climate of Mexico; vegetation distribution

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx

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041.#.7.h: eng

520.3.#.a: The climate regions of Mexico are delimitated using hierarchical clustering analysis (HCA). The data used consists of monthly means of maximum and minimum temperatures and monthly-accumulated precipitation. The dataset was obtained from heterogeneously distributed climatic stations in Mexico for the period from 1961 to 2004. This cluster method assigns precipitation and temperature variables to groups of clusters based on similar statistical characteristics. We carried out a principal components analysis to obtain a standardized reduced matrix to be used in HCA. By applying two clustering criteria (K-means and Ward´s method) it was possible to define statistically groups of stations that delimit regions of similar climate. In addition, the applied methodology describes the dominant vegetation distribution for each climate region. This analysis may contribute to the generation of new climate scenarios, where the dynamics of land vegetation cover could be included as a biomarker of climate.

773.1.#.t: Atmósfera; Vol. 30 No. 3 (2017); 233-242

773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index

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022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

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300.#.#.a: Páginas: 233-242

264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

doi: https://doi.org/10.20937/ATM.2017.30.03.04

handle: 08117a6dcb1d089d

harvesting_date: 2023-06-20 16:00:00.0

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No entro en nada

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Artículo

Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis

Pineda-martínez, Luis F.; Carbajal, Noel

Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM, publicado en Atmósfera, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
Revista
Repositorio
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Pineda-martínez, Luis F., et al. (2017). Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis. Atmósfera; Vol. 30 No. 3, 2017; 233-242. Recuperado de https://repositorio.unam.mx/contenidos/11302

Descripción del recurso

Autor(es)
Pineda-martínez, Luis F.; Carbajal, Noel
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis
Fecha
2017-06-30
Resumen
The climate regions of Mexico are delimitated using hierarchical clustering analysis (HCA). The data used consists of monthly means of maximum and minimum temperatures and monthly-accumulated precipitation. The dataset was obtained from heterogeneously distributed climatic stations in Mexico for the period from 1961 to 2004. This cluster method assigns precipitation and temperature variables to groups of clusters based on similar statistical characteristics. We carried out a principal components analysis to obtain a standardized reduced matrix to be used in HCA. By applying two clustering criteria (K-means and Ward´s method) it was possible to define statistically groups of stations that delimit regions of similar climate. In addition, the applied methodology describes the dominant vegetation distribution for each climate region. This analysis may contribute to the generation of new climate scenarios, where the dynamics of land vegetation cover could be included as a biomarker of climate.
Tema
Hierarchical clustering analysis; principal component analysis; climate of Mexico; vegetation distribution
Idioma
eng
ISSN
ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

Enlaces