Long-range correlations of soil water content time series under tillage and different cover crops in a semi-arid vineyard

We explored the suitability of multifractal detrended fluctuation analysis (MFDFA) to characterize the temporal dynamics of soil water content (SWC), we analysed the origin of the multifractality of time series of the dynamic of SWC and we investigated how this dynamic was affected by different soil treatments and depths, through the characterization of long-range correlations. Temporal data of SWC from conventional tillage and two different cover crops at three different depths in a Mediterranean vineyard were examined. The dataset spanned 18 months in 30-min intervals. We investigated if SWC dynamics, as reflected by long-range dependencies, can be detected and quantified by MFDFA in the form of scaling laws. We analysed the randomly shuffled series associated with temporal data of SWC to investigate if the detected multifractality of the data reflected either a broad density function of the values of the series or different long-range correlations for small and large fluctuations in the dataset. Finally, the impact of treatment and depth on the variation of long-range dependencies was studied. Our results suggest that the time records of soil moisture in a Mediterranean vineyard show a multifractal behaviour compatible with complex patterns of long-range correlations. Moreover, this complex structure is affected by depth, as depicted by the statistically significant variation of the generalized Hurst exponenth(0), which controls the logarithmic mean of the fluctuation of SWC over a period of time. No significant interactions between treatments and depth was found. This suggests that soil properties that influence soil water dynamics at each depth are not affected by cover crop treatments. This could be due to the fact that the experiment lasted less than 2 years and no long-term interactions were present at this time interval. Highlights Multifractal detrended fluctuation analysis (MFDFA) characterizes soil water content dynamics. MFDFA detects the influence of soil depth on long-range correlations. Time series of soil moisture are multifractals compatible with complex patterns of long-range correlations. Long-range dependencies are affected by depth but no interaction has been found between soil treatment and depth.

Automated summary

The study found that MFDFA can be used to characterize the dynamics of soil water content, and discovered that long-range correlations are influenced by soil depth. The time series of soil moisture exhibit multifractal behavior, with depth playing a significant role, though no interaction between treatment and depth was observed.