• 研究报告 •

### 祁连山地甘肃臭草斑块土壤水分的空间自相关分析

1. (西北师范大学地理与环境科学学院， 甘肃省湿地资源保护与产业发展工程研究中心， 兰州 730070)
• 出版日期:2014-03-10 发布日期:2014-03-10

### Spatial autocorrelation analysis on soil moisture of Melica przewalskyi patch in a degraded alpine grassland of Qilian Mountains, Northwest China.

YANG Quan, ZHAO Cheng-zhang**, SHI Li-li, DANG Jing-jing, ZHA Gao-de

1. (Research Center of Wetland Resources Protection and Industrial Development Engineering of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)
• Online:2014-03-10 Published:2014-03-10

Abstract: A prerequisite in using conventional statistical methods, such as regression models in investigating spatial distribution of soil moisture, is that the data regarding soil moisture should be statistically independent and identically distributed. However, soil moisture generally exists with spatial autocorrelation to some degree, which contains some useful information. In this paper, the spatial autocorrelation analysis of soil moisture in Melica przewalskyi patch was investigated based on Moran’s I index on the north slope of the Qilian Mountains. Moran’s I was applied to describe spatial autocorrelation of soil moisture, and analyze the scales of spatial autocorrelation. Meanwhile, standard multiple linear regression model and spatial autoregressive model of soil moisture were constructed. The results showed that distribution of surface soil moisture all displayed spatial autocorrelation characteristics. In addition, the spatial aggregation characteristics of the 20-30 cm depth were higher than that of the 0-10 and 10-20 cm depths. It was found that the Moran’s I decreased with the increase of the scale of spatial analysis. The spatial autocorrelation of surface soil moisture resulted from different soil depths. At the 10-20 cm depth, the community height and Melica przewalskyi coverage had significant effects on the spatial autocorrelation, while at the 20-30 cm depth, the Stipa krylovii coverage and community height significantly affected the spatial autocorrelation. Our analysis showed that spatial autoregressive model was better than the standard multiple linear regression model due to the spatial autocorrelation exerting more impact on the latter one.