Characterizing scale-specific environmental factors affecting soil organic carbon along two landscape transects
Soil organic carbon (SOC) is one of the most important soil properties affecting many other soil and environmental properties and processes. In order to understand and manage SOC effectively, it is important to identify the scale-specific main factors affecting SOC distributions, which in this study occurred in a watershed on the Loess Plateau. Two transects were selected that passed along the upper slopes on each side of the main gully of the Liudaogou watershed. Transect 1 (3411-m length) had 27 sampling sites at 131-m intervals; transect 2 (3597 m length) had 30 sampling sites at 124-m intervals. The two transects were chosen in order to compare landscape patterns of differing complexity that were in close proximity, which reduced the effects of factors that would be caused by different locations. The landscape of transect 1 was more complex due to the greater diversity in cultivation. Multivariate empirical mode decomposition (MEMD) decomposed the total variation in SOC and five selected environmental factors into four intrinsic mode functions (IMFs) and a residual according to the scale of occurrence. Scale-specific correlation analysis was used to identify significant relationships between SOC and the environmental factors. The dominant scales were those that were the largest contributors to the total SOC variance; for transect 1, this was the IMF 1 (scale of 403 m), whereas for transect 2, it was the medium scale of the IMF 2 (scale of 688 m). For both transects, vegetation properties (vegetation cover and aboveground biomass) were the main factors affecting SOC distributions at their respective dominant scales. At each scale, the main effective factors could be identified although at the larger scales, their contributions to the overall variance were almost negligible. The distributions of SOC and the factors affecting it were found to be scale dependent. The results of this study highlighted the suitability of the MEMD method in revealing the main scale-specific factors that affect SOC distributions, which is necessary in understanding and managing this important soil property.
Application of multivariate empirical mode decomposition for revealing scale-and season-specific time stability of soil water storage
Abstract Spatial patterns of soil water storage (SWS), the total amount of water stored in soil at a given depth interval, tend to be similar if we measure at different times. This is characterized as time stability and can be used to optimize sampling design. The objective of this study was to examine the scale- and season-specific time stability of SWS spatial patterns at seven depth intervals (at every 20 cm down to 140 cm) in a hummocky landscape. Soil water content was measured 20 times using time domain reflectometry and a neutron probe along a transect of 128 points over a four-year period and converted to SWS by multiplying by the depth intervals. Multivariate empirical mode decomposition (MEMD) was used to decompose the spatial series of SWS into six or seven (depending on depth) components known as intrinsic mode functions (IMFs). Each IMF represents a specific scale of SWS. Spearman's rank correlation coefficients between IMFs from different measurement dates were used to characterize the time stability of SWS at different scales. The variance of each IMF and its ratio to the variance of the original SWS series was calculated. The dominant scale, which has the maximum ratio of variance to the original variances, was 104–128 m (IMF5) for the depth intervals of 0–20 cm to 60–80 cm and 315–412 m (IMF7) for the depth intervals of 80–100 cm to 120–140 cm. Time stability gradually increased with spatial scales and was the strongest at the dominant scale. At any scale, time stability was the strongest within the same season and the weakest between different seasons. This study indicates that MEMD combined with Spearman's rank correlation analysis has great potential for revealing the scale specific time stability of SWS.
Influence of the spatial extent and resolution of input data on soil carbon models in Florida, USA
(R 2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R 2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs.