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

  1. M. Francos 1
  2. P. Pereira 2
  3. X. Úbeda 1
  1. 1 Universitat de Barcelona

    Universitat de Barcelona

    Barcelona, España


  2. 2 Mykolas Romeris University

    Mykolas Romeris University

    Vilna, Lituania


Cuadernos de investigación geográfica: Geographical Research Letters
  1. Lasanta Martínez, Teodoro (ed. lit.)

ISSN: 0211-6820 1697-9540

Year of publication: 2019

Volume: 45

Issue: 2

Pages: 601-621

Type: Article

DOI: 10.18172/CIG.3516 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Cuadernos de investigación geográfica: Geographical Research Letters


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.

Funding information

This study was supported by the POSTFIRE Project (CGL2013-47862-C2-1 and 2-R) and the POSTFIRE_CARE Project (CGL2016-75178-C2-2-R) sponsored by the Spanish Ministry of Economy and Competitiveness and AEI/FEDER, EU. Support was also received from the FPU Program (FPU 014/00037), sponsored by the Ministry of Education, Culture and Sports; EST16/00183 – a short-stay grant at MykolasRomeris University (Vilnius, Lithuania), provided by the Ministry of Education, Culture and Sports; and the Program2017SGR1344 of the Generalitat de Catalunya.


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