Abstract
Sampling and testing are conducted on groundwater depth and vegetation coverage in the 670 km2 of the Sangong River Basin and semi-variance function analysis is made afterwards on the data obtained by the application of geo-statistics. Results showed that the variance curve of the groundwater depth and vegetation coverage displays an exponential model. Analysis of sampling data in 2003 indicates that the groundwater depth and vegetation coverage change similarly in space in this area. The Sangong River Basin is composed of upper oasis, middle ecotone and lower sand dune. In oasis and ecotone, influenced by irrigation of the adjoining oasis, groundwater level has been raised and soil water content also increased compared with sand dune nearby, vegetation developed well. But in the lower reaches of the Sangong River Basin, because of descending of groundwater level, soil water content decreased and vegetation degenerated. From oasis to abandoned land and desert grassland, vegetation coverage and groundwater level changed greatly with significant difference respectively in spatial variation. Distinct but similar spatial variability exists among the groundwater depth and vegetation coverage in the study area, namely, the vegetation coverage decreasing (increasing) as the groundwater depth increases (decreases). This illustrates the great dependence of vegetation coverage on groundwater depth in arid regions and further implies that among the great number of factors affecting vegetation coverage in arid regions, groundwater depth turns out to be the most determinant one.
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References
Bloschl G, Sivapalan M, 1998. Scale issues in hydrological modeling: a review.Hydrol. Proc., 9(3–4): 251–290.
Burgess, Webster R, 1980. Optimal interpolation and isarithm mapping of soil properties.Soil Science, 31: 315–331.
Cambardella C A, Moorman T B, Novak J Met al., 1994. Field-scale variability of soil properties in central Iowa soils.Soil Science, 58: 1501–1511.
Clark Isobel, 1979. Practical Geo-statistics. London: Applied Science Publishers Ltd.
Fethi B J, 1994. Multivariate geo-statistical design of groundwater monitoring networks.Water Resources Planning and Management, 120(4): 505.
Goovaerts P, 1999. Geo-statistics in soil science: state-of-the-art and perspectives.Geoderm, 89: 1–45.
Hodgson D I F, 1978. The use of multiple linear regressions in simulating ground-water level responses.Ground Water, 28: 1694–1719.
Hou Jingru, 1998. Utility Geo-statistics. Beijing: Geological Press. (in Chinese)
John Harte, Ann Kinzig, Jessica Green, 1999. Self-similarity in the distribution and abundance of species.Science, 284(9): 334–336.
Li Habin, 1998. Theory and methodology of spatial heterogeneity quantification.Chinese Journal of Applied Ecology, 9(6): 651–657. (in Chinese)
Matheron G, 1963. Principles of geostatistics.Economic Geology, 58: 1246–1266.
Qin Yaodong, 1998. Semi-variogram problems in soil spatial variability.Chinese Journal of Agricultural Engineering, 12(4): 42–47. (in Chinese)
Sun Hongquan, 1990. Geo-statistics and Its Application. Xuzhou: Chinese Mining University Press. (in Chinese)
Trangmar B B, Yost R S, Uehara G, 1985. Application of geo-statistics to spatial studies of soil properties.Advances in Agronomy, 38: 44–94.
Wang Zhengquan, 1999. Geo-statistics and Its Application in Ecology. Beijing: Science Press, 162–192. (in Chinese)
Willian E Kunin, 1998. Extrapolating species abundance across spatial scales.Science, 281(4): 1513–1515.
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Danyar, S., Yudong, S. & Jumakeld, M. Influence of groundwater level change on vegetation coverage and their spatial variation in arid regions. J. Geogr. Sci. 14, 323–329 (2004). https://doi.org/10.1007/BF02837413
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DOI: https://doi.org/10.1007/BF02837413