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Multiscale Influences of Soil Properties on Soil Water Content Distribution in a Watershed on the Chinese Loess Plateau

She, Dongli1,2; Liu, Dongdong2; Peng, Shizhang1; Shao, Ming’an3

doi: 10.1097/SS.0000000000000021
Technical Article

Abstract: The characterization of hydrological and biological processes requires information on the scaling properties of soil water content (SWC). In this regard, accurate estimation of nonstationary and nonlinear SWC distribution for various scales is a challenge. In this study, multivariate empirical mode decomposition (MEMD) was applied to reveal the multiscale influences of soil properties on SWC distribution in the Loess Plateau landscapes. Seven data sets analyzed in this study were SWC of 0 to 100 cm measured at seven different periods from two transects with obvious differences in five soil properties, that is, soil organic matter, clay, silt, sand, and bulk density. Soil water content and soil properties were separated into different numbers (four in Transect 1 and three in Transect 2) of intrinsic mode functions (IMF) and residue representing different “common” scales by MEMD. Scale-specific relationships between SWC and soil properties varied with scales and measurement periods. The influence of soil properties on SWC was more deterministic at greater scales. Soil water content at each IMF (specific scale) or residue was predicted from the scale-specific controlling factors and the summing up of all the predicted IMF, and residue simulated well the SWC distribution at the measurement scale. Soil organic matter and soil particle composition were the main explanatory variables for the overall SWC estimation, respectively, for the two transects. The overall SWC prediction using MEMD outperformed the SWC predictions using the traditional method based on the original data.

1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China.

2College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China.

3Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China.

Address for correspondence: Dr. Dongli She, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China. E-mail: shedongli@gmail.com

Financial Disclosures/Conflicts of Interest: This study was funded by the National Nature Science Foundation of China (Grant No. 51109063) and by the China Postdoctoral Science Foundation (Grant No. 2012T50433).

Received June 28, 2013.

Accepted for publication October 30, 2013.

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