A multifractal approach to characterize cumulative rainfall and tillage effects on soil surface micro-topography and to predict depression storage

摘要

Most of the indices currently employed for assessing soilsurface micro-topography, such as random roughness (RR), are merelydescriptors of its vertical component. Recently, multifractal analysisprovided a new insight for describing the spatial configuration of soilsurface roughness. The main objective of this study was to test the abilityof multifractal parameters to assess in field conditions the decay ofinitial surface roughness induced by natural rainfall under different soiltillage systems. In addition, we evaluated the potential of the joint use ofmultifractal indices plus RR to improve predictions of water storage indepressions of the soil surface (MDS). Field experiments were performed onan Oxisol at Campinas, S?o Paulo State (Brazil). Six tillage treatments,namely, disc harrow, disc plough, chisel plough, disc harrow + disc level,disc plough + disc level and chisel plough + disc level were tested. In eachtreatment soil surface micro-topography was measured four times, withincreasing amounts of natural rainfall, using a pin meter. The samplingscheme was a square grid with 25 × 25 mm point spacing and the plot size was1350 × 1350 mm (≈1.8 m2), so that each data set consisted of3025 individual elevation points. Duplicated measurements were taken pertreatment and date, yielding a total of 48 experimental data sets. MDS wasestimated from grid elevation data with a depression-filling algorithm.Multifractal analysis was performed for experimental data sets as well asfor oriented and random surface conditions obtained from the former byremoving slope and slope plus tillage marks, respectively. All theinvestigated microplots exhibited multifractal behaviour, irrespective ofsurface condition, but the degree of multifractality showed wide differencesbetween them. Multifractal parameters provided valuable information forcharacterizing the spatial features of soil micro-topography as they wereable to discriminate data sets with similar values for the verticalcomponent of roughness. Conversely, both, rough and smooth soil surfaces,with high and low roughness values, respectively, can display similar levelsof spectral complexity. Although in most of the studied cases trend removalproduces increasing homogeneity in the spatial configuration of heightreadings, spectral complexity of individual data sets may increase ordecrease, when slope or slope plus tillage tool marks are filtered.Increased cumulative rainfall had significant effects on various parametersfrom the generalized dimension, D_q, and singularity spectrum,f(α). Overall, micro-topography decay by rainfall was reflected on a shift ofthe singularity spectra, f(α) from the left side(q0) to the right side (q0) and also on a shift of the generalizeddimension spectra from the right side (q0) to the left side(q0). The use of an exponential model of vertical roughness indices,RR, and multifractal parameters accounting for the spatial configurationsuch as D₁ or D₅ improved estimation of water stored in surfacedepressions.