塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测  

Bayesian geostatistical prediction of soil organic carbon contents of solonchak soils in northern Tarim Basin,Xinjiang,China

作　　者：伍维模[1,2] 王家强[2] 曹琦[2] 吴嘉平[3] WU Wei-mo WANG Jia-qiang CAO Qi WU Jia- ping(College of Environmental and Resources Sciences, Zhefiang University, Hangzhou 310058, China College of Plant Science, Tarim University, Alar 843300, Xinjiang, China Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan 316021, Zhejiang, China）.)

机构地区：[1]浙江大学环境与资源学院,杭州310058 [2]塔里木大学植物科学学院,新疆阿拉尔843300 [3]浙江大学海岛海岸带研究所,浙江舟山316021

出　　处：《应用生态学报》2017年第2期439-448,共10页Chinese Journal of Applied Ecology

基　　金：国家自然科学基金项目(40961028)资助~~

摘　　要：准确预测土壤有机碳的空间分布,对于土壤资源开发和保护、应对气候变化和生态系统健康都具有重要意义.本文以塔里木盆地北缘盐土1300 m×1700 m样地为试验区,采集5~10 cm深度土壤样品144个,构建土壤有机碳含量的贝叶斯地统计空间预测模型,并以普通克里格、序惯高斯模拟和逆距离加权方法为对照,评价贝叶斯地统计对土壤有机碳含量的预测性能.结果表明:研究区土壤有机碳含量处于1.59~9.30 g·kg^(-1),平均值为4.36 g·kg^(-1),标准偏差为1.62 g·kg^(-1);半方差函数符合指数模型,空间结构比参数值为0.57;利用贝叶斯地统计方法,获得了土壤有机碳含量的空间分布图以及评价预测不确定性的预测方差、上95%分位数、下95%分位数分布图;与普通克里格、序惯高斯模拟和逆距离加权方法相比,贝叶斯地统计方法具有更高的土壤有机碳含量空间预测精度,显示出该方法对土壤有机碳含量预测的优越性.Accurate prediction of soil utilization and conservation, climate c organic carbon （SOC） distribution is crucial for soil resources hange adaptation, and ecosystem health. In this study, we se- lected a 1300 mxl700 m solonchak sampling area in northern Tarim Basin, Xinjiang, China, and collected a total of 144 soil samples （5-10 cm）. The objectives of this study were to build a Baye- sian geostatistical model to predict SOC content, and to assess the performance of the Bayesian model for the prediction of SOC content by comparing with other three geostatistical approaches [ ordinary kriging （OK） , sequential Gaussian simulation （SGS） , and inverse distance weighting （IDW） ]. In the study area, soil organic carbon contents ranged from 1.59 to 9.30 g · kg^-1 with a mean of 4.36 g · kg^-1and a standard deviation of 1.62 g · kg^-1. Sample semivariogram was best fitted by an exponential model with the ratio of nugget to sill being 0.57. By using the Bayesian geostatistical approach, we generated the SOC content map, and obtained the prediction variance, upper 95% and lower 95% of SOC contents, which were then used to evaluate the prediction uncertainty. Bayesian geostatistical approach performed better than that of the OK, SGS and IDW, demonstrating the ad- vantages of Bayesian approach in SOC prediction.

关 键 词：土壤有机碳 空间预测 贝叶斯地统计 盐土 

分 类 号：S153.6[农业科学—土壤学]