Abstract
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.
Highlights
Wildfires may affect soil nutrients directly or indirectly (Smithwick et al, 2005)
With the exceptions of AS and total nitrogen (TN), the intense rainfall reduced the coefficient of variation (CV) of all the variables
The intense rainfall event changed the relationship between the soil variables, causing some correlations to increase and others to fall
Summary
Wildfires may affect soil nutrients directly (e.g. heating) or indirectly (e.g. ash deposition) (Smithwick et al, 2005) These effects are heterogeneous, determined by varying topographies, fuel amounts, environmental conditions, and present considerable variability at the small scale (Outeiro et al, 2008; Romero-Ruiz et al, 2010). This spatial variability undergoes marked changes depending on the erosion agents present and the incorporation of ash into the soil profile (Outeiro et al., 2008) This irregular distribution of soil nutrients has a significant impact on the degree of vegetation recovery and landscape restoration after high-severity wildfires (Francos et al, 2016a). Pereira et al (2015) investigated the spatial distribution of extractable aluminium and zinc, zero, two, five, seven and nine months after a lowseverity grassland wildfire, and Rodríguez-Martín et al (2013) observed the spatial pattern of several nitrogen and phosphorous (P) forms before and after a wildfire
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