Multi-scale correlations between soil hydraulic properties and associated factors along a Brazilian watershed transect

Abstract

Soil hydraulic properties are important to the understanding and modeling of hydrological processes at the watershed scale. These properties are presumed to be correlated with associated topographic attributes and other soil factors operating at different intensities and scales. Using multivariate empirical mode decomposition (MEMD), this study examined the multi-scale correlations among two soil hydraulic properties and two topographic attributes and three soil factors along a transect in the Pelotas River Watershed (PRW) situated in Southern Brazil. Soil water content at field capacity (θFC) and saturated hydraulic conductivity (KS) were determined for 100 soil samples taken at 250-m intervals along a 25-km transect that cut through the PRW. The five selected factors were elevation (Ele), slope (Slo), sand content (Sand), bulk density (Bd), and soil organic carbon (SOC). The topographic attributes were derived from a digital elevation model, while those related to soils were determined for the collected samples. The multivariate data series of the two soil hydraulic properties and five associated factors were decomposed into six intrinsic mode functions (IMFs). For θFC, 52.4% of the total variance was separated at IMF1 (scale of 728m) and IMF2 (1,113m), while for ln KS, 35.9% of the variance was separated at IMF1 (728m) and IMF6 (scale of 11,877m). It was found that scale-specific relationships between soil hydraulic properties and the associated factors varied with scale. The associated factors exerted influence on the soil hydraulic properties at their own dominant scale(s). The θFC and ln KS values at each IMF (specific scale) and residue were predicted from the scale-specific associated factors at the same IMF or residue. The total of all the predicted IMFs including the residue was then used to predict the θFC and ln KS at the measurement scale. Sand had the greatest relative importance to the measurement-scale θFC prediction model, while topographic properties were clearly the dominant explanatory factors for the overall ln KS prediction. The overall ln KS predictions using the MEMD outperformed those based on the original data.