Saturated hydraulic conductivity (Ks) usually varies at multiple scales in space, as affected by different soil and environmental processes operating at diverse scales. Identifying spatial process relationships can be challenging due to overlapping of underlying processes at different scales. The objective of this study was to evaluate noise-assisted multivariate empirical mode decomposition (NA-MEMD) for characterizing Ks variability and depicting its scale-dependent relationships with different soil properties and environmental factors. At an interval of 10 km along an 860-km south-north transect across the Loess Plateau of China, Ks, bulk density, soil organic carbon content, sand and clay contents at three depths of 0 to 10, 10 to 20 and 20 to 40 cm were investigated as well as four environmental factors of elevation, slope gradient, annual precipitation and temperature. Decomposed into different intrinsic mode functions (IMFs) and residues by NA-MEMD, Ks at all depths were found to vary at the smallest scale of 29 km mainly, corresponding to IMF1s, which manifested 33.0 to 48.1% of the total Ks variance. The small-scale variations of Ks reflected not only in IMF1s but also in IMF2s and IMF3s were dominated by soil properties especially bulk density, and the large-scale variations corresponding to IMF4s and IMF5s were controlled by environmental factors in general. For each depth, Ks at the scale of investigation was estimated by adding all the IMFs and residue derived from the factor components at equivalent scales using multiple linear regression (MLR). Such Ks estimations after NA-MEMD evidently outperformed the MLR before NA-MEMD by explaining additional 9.4 to 18.7% of the total Ks variance, but underperformed the artificial neural network and state-space approach also implemented on undecomposed spatial series of Ks and its underlying factors. NA-MEMD serves as a useful tool for Ks characterization and its incorporation with nonlinear functions or spatial interactions with impact factors is suggested for the estimation of Ks and other soil processes.
|Number of pages||13|
|Journal||Soil Science Society of America Journal|
|State||Published - Apr 11 2019|
Bibliographical noteFunding Information:
The study was funded by the National Natural Science Foundation of China (No. 41571130082 and 41601277) and the Project supported by State Key Laboratory of Earth Surface Processes and Resource Ecology (No. 2017-ZY-09). Third author (O.W.) is indebted to thank for the support by the USDA National Institute of Food and Agriculture, Multistate Project KY006093.
© 2019 The Author(s).
ASJC Scopus subject areas
- Soil Science