Multivariate and geostatistical analysis of wetland soil salinity in nested areas of the Yellow River Delta
M. Yang A , S. L. Liu A C , Z. F. Yang A , T. Sun A and Robert Beazley BA School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China.
B Department of Natural Resources, College of Agriculture and Life Sciences, Fernow Hall 302, Cornell University, Ithaca, NY 14853, USA.
C Corresponding author. Email: shiliangliu@bnu.edu.cn
Australian Journal of Soil Research 47(5) 486-497 https://doi.org/10.1071/SR08211
Submitted: 22 September 2008 Accepted: 30 April 2009 Published: 18 August 2009
Abstract
This study investigated scale dependency of certain soil salinity ions in topsoil horizons in the Yellow River Delta in north-east Shandong province, China. Factorial kriging analysis (FKA) was used to analyse spatial variability of soil salinity ions (Na+, K+, Mg2+, Ca2+, Cl–, SO42–) sampled at 3 nested areas over a geologically contrasting region. Correlation analysis and principal component analysis (PCA) were performed on the logarithmic variables, then multivariate geostatistics was used to investigate scale dependency of soil salinity spatial variability and auto- and cross-variograms exhibited by 3 spatial structures: nugget effect, short-range, and long-range structures. Statistical analysis showed that NaCl was the main salinity type over the 3 nested sample areas. In addition, the variables were random and regional, which implied that a linear model of coregionalisation was feasible for the analysis of their spatial variability. The coefficients of the coregionalisation matrix showed that the short-range structures of auto- and cross-correlation for soil salinity were dominant at the large and middle-sized sample areas, while the long-range structure dominated at the small area. The resulting structural correlation coefficients showed strong correlations between variables changing as a function of spatial scale. These relationships between soil salinity variables at different spatial structures were not acquired by the linear correlation coefficients.
PCA was then performed on the coregionalisation matrices at each sample area to summarise the relationships among variables at different spatial structures. From the synthetic analysis of coregionalisation matrices, correlation matrices, and principal components, we concluded that soil genesis and parent material may act on short-range variation of soil salinity, while climate and topography may influence long-range structure at the large sample area. At the middle-sized sample area, variations were mostly affected by mineral fertilisation at the short-range structure, while human activities such as irrigation and drainage in wetland restorations influenced the soil salinity spatial variability at the long-range scale. Vegetation and groundwater table may also be important factors influencing the spatial variability of soil salinity at different spatial structures at the small sample areas.
Additional keywords: spatial variability, salinity, factory kriging, PCA, coregionalisation.
Acknowledgements
This study was supported by the National Key and Important Program for Basic Research of China (No. 2006CB403303) and the National Natural Sciences Foundation of China (No. 40871237; No. 40501067). The authors would like to thank instructor Liu R M for guiding in operation of multivariate geostatistical techniques.
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