Mapping impact of intense rainfall on a high-severity burned area using principal component analysis

High-severity wildfires have a major impact on soil properties. Moreover, recently burned areas are highly sensitive to intense rainfall events. However, little is known about the impact of extreme rainfall on the relationship between soil properties and their spatial distribution. The objective of...

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Detalles Bibliográficos
Autores: Francos, Marcos, Pereira, Paulo Alexandre da Silva, Úbeda, Xavier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/164849
Acceso en línea:https://hdl.handle.net/2445/164849
Access Level:acceso abierto
Palabra clave:Incendis forestals
Precipitacions (Meteorologia)
Pluviometria
Forest fires
Precipitations (Meteorology)
Pluviometry
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spelling Mapping impact of intense rainfall on a high-severity burned area using principal component analysisFrancos, MarcosPereira, Paulo Alexandre da SilvaÚbeda, XavierIncendis forestalsPrecipitacions (Meteorologia)PluviometriaForest firesPrecipitations (Meteorology)PluviometryHigh-severity wildfires have a major impact on soil properties. Moreover, recently burned areas are highly sensitive to intense rainfall events. However, little is known about the impact of extreme rainfall on the relationship between soil properties and their spatial distribution. The objective of this study is to examine the effects of an intense rainfall event on soil properties and their spatial distribution in a small area using principal component analysis (PCA). The variables studied were aggregate stability (AS), total nitrogen (TN), soil organic matter (SOM), inorganic carbon (IC), C/N ratio, calcium carbonates (CaCO3), pH, electrical conductivity (EC), available phosphorus (P), extractable calcium (Ca), extractable magnesium (Mg), extractable sodium (Na) and extractable potassium (K). Each PCA (before and after intense rainfall event) allowed us to extract five factors. Factor 1 in the pre-intense rainfall event PCA explained the variance of EC, available P, extractable Mg and K; factor 2 accounted for TN, SOM (high loadings), IC and CaCO3 (low loadings); factor 3 explained AS, extractable Ca and Na; and, factors 4 and 5 accounted for C/N and pH, respectively. Factor 1 in the after intense rainfall event PCA explained the variance of TN, SOM, EC, available P, extractable Mg and K (high loadings) and pH (low loading); factor 2 accounted for IC and CaCO3; factor 3 explained extractable Ca and Na; factor 4 accounted for AS; and, factor 5 for C/N. The results showed that the intense rainfall event changed the relationship between the variables, strengthening the correlation between them, especially in the case of TN, SOM, EC, available P, extractable Mg and extractable Ca with AS. In the case of the pre-intense rainfall event PCA, the best- fit variogram models were: factors 1 and 2 - the linear model; factors 3 and 4 - the pure nugget effect; and, factor 5 - the spherical model. In the case of the factors after intense rainfall event PCA, with the exception of factor 5 (spherical model), the best fit model was the linear model. The PCA score maps illustrated a marked change in the spatial distribution of the variables before and after the intense rainfall event. Important differences were detected in AS, pH, C/N IC, CaCO3, extractable Ca and Na.Universidad de La Rioja2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/164849Articles publicats en revistes (Geografia)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.18172/cig.3516Cuadernos de Investigación Geográfica, 2019, vol. 45, num. 2, p. 601-621https://doi.org/10.18172/cig.3516cc-by-nc-nd (c) Universidad de La Rioja, 2019http://creativecommons.org/licenses/by-nc-nd/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1648492026-05-27T06:46:51Z
dc.title.none.fl_str_mv Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
title Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
spellingShingle Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
Francos, Marcos
Incendis forestals
Precipitacions (Meteorologia)
Pluviometria
Forest fires
Precipitations (Meteorology)
Pluviometry
title_short Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
title_full Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
title_fullStr Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
title_full_unstemmed Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
title_sort Mapping impact of intense rainfall on a high-severity burned area using principal component analysis
dc.creator.none.fl_str_mv Francos, Marcos
Pereira, Paulo Alexandre da Silva
Úbeda, Xavier
author Francos, Marcos
author_facet Francos, Marcos
Pereira, Paulo Alexandre da Silva
Úbeda, Xavier
author_role author
author2 Pereira, Paulo Alexandre da Silva
Úbeda, Xavier
author2_role author
author
dc.subject.none.fl_str_mv Incendis forestals
Precipitacions (Meteorologia)
Pluviometria
Forest fires
Precipitations (Meteorology)
Pluviometry
topic Incendis forestals
Precipitacions (Meteorologia)
Pluviometria
Forest fires
Precipitations (Meteorology)
Pluviometry
description High-severity wildfires have a major impact on soil properties. Moreover, recently burned areas are highly sensitive to intense rainfall events. However, little is known about the impact of extreme rainfall on the relationship between soil properties and their spatial distribution. The objective of this study is to examine the effects of an intense rainfall event on soil properties and their spatial distribution in a small area using principal component analysis (PCA). The variables studied were aggregate stability (AS), total nitrogen (TN), soil organic matter (SOM), inorganic carbon (IC), C/N ratio, calcium carbonates (CaCO3), pH, electrical conductivity (EC), available phosphorus (P), extractable calcium (Ca), extractable magnesium (Mg), extractable sodium (Na) and extractable potassium (K). Each PCA (before and after intense rainfall event) allowed us to extract five factors. Factor 1 in the pre-intense rainfall event PCA explained the variance of EC, available P, extractable Mg and K; factor 2 accounted for TN, SOM (high loadings), IC and CaCO3 (low loadings); factor 3 explained AS, extractable Ca and Na; and, factors 4 and 5 accounted for C/N and pH, respectively. Factor 1 in the after intense rainfall event PCA explained the variance of TN, SOM, EC, available P, extractable Mg and K (high loadings) and pH (low loading); factor 2 accounted for IC and CaCO3; factor 3 explained extractable Ca and Na; factor 4 accounted for AS; and, factor 5 for C/N. The results showed that the intense rainfall event changed the relationship between the variables, strengthening the correlation between them, especially in the case of TN, SOM, EC, available P, extractable Mg and extractable Ca with AS. In the case of the pre-intense rainfall event PCA, the best- fit variogram models were: factors 1 and 2 - the linear model; factors 3 and 4 - the pure nugget effect; and, factor 5 - the spherical model. In the case of the factors after intense rainfall event PCA, with the exception of factor 5 (spherical model), the best fit model was the linear model. The PCA score maps illustrated a marked change in the spatial distribution of the variables before and after the intense rainfall event. Important differences were detected in AS, pH, C/N IC, CaCO3, extractable Ca and Na.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/164849
url https://hdl.handle.net/2445/164849
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.18172/cig.3516
Cuadernos de Investigación Geográfica, 2019, vol. 45, num. 2, p. 601-621
https://doi.org/10.18172/cig.3516
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Universidad de La Rioja, 2019
http://creativecommons.org/licenses/by-nc-nd/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Universidad de La Rioja, 2019
http://creativecommons.org/licenses/by-nc-nd/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad de La Rioja
publisher.none.fl_str_mv Universidad de La Rioja
dc.source.none.fl_str_mv Articles publicats en revistes (Geografia)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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