| 黄土高原沟壑区坡地土壤水分状态空间模拟 |
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| 引用本文： | 段良霞,黄明斌,张洛丹,索立柱,张永坤.黄土高原沟壑区坡地土壤水分状态空间模拟[J].水科学进展,2015,26(5):649-659. |
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| 作者姓名： | 段良霞 黄明斌 张洛丹 索立柱 张永坤 |
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| 作者单位： | 1.西北农林科技大学资源环境学院, 陕西 杨凌 712100; |
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| 基金项目： | 国家自然科学基金资助项目(41390463) |
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| 摘 要： | 为掌握黄土高原沟壑区坡地土壤水分的空间分布特征及其影响因素,采用状态空间模型和经典线性回归方法对该区不同土层深度土壤含水率的分布进行模拟.结果表明,不同土层深度的土壤含水率呈中等程度变异,并与海拔高度、黏粒、粉粒、砂粒含量和分形维数具有显著的空间自相关和交互相关关系,可用于状态空间模拟分析.不同因素组合下的状态空间模拟效果均要优于线性回归方程,其中采用海拔高度、砂粒含量和分形维数的三因素状态空间方程模拟精度最高(R2=0.992).状态空间模拟方法可用于黄土高原坡面尺度不同土层深度土壤含水率的预测.
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| 关 键 词： | 坡面尺度 土壤水分 线性回归 状态空间模拟 黄土高原 |
| 收稿时间： | 2015-02-05 |
State-space prediction of soil water content on a hillslope in the gully region of the Loess Plateau,China |
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| DUAN Liangxia,HUANG Mingbin,ZHANG Luodan,SUO Lizhu,ZHANG Yongkun.State-space prediction of soil water content on a hillslope in the gully region of the Loess Plateau,China[J].Advances in Water Science,2015,26(5):649-659. |
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| Authors: | DUAN Liangxia HUANG Mingbin ZHANG Luodan SUO Lizhu ZHANG Yongkun |
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| Affiliation: | 1.College of Resources and Environment, Northwest A&F University, Yangling 712100, China;2.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China;3.University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Soil water content is one of the key factors affecting plant growth and eco-environment reconstruction on the Loess Plateau of China. To assess the spatial heterogeneity of soil water content and its potential influencing factors on a hillslope in the gully region of the Loess Plateau, the state-space approach and a classical linear regression approach were applied in order to identify and quantify the significant relationships between soil water content and elevation, contents of clay, silt, and sand, median soil grain size, and fractal dimension. The results showed that the soil water contents in different soil layers exhibited moderate variation, and were significantly influenced by the elevation, the contents of clay, silt, and sand, and by the fractal dimension. Autocorrelation for the six potential influencing factors were conducted, and cross-correlation functions indicated strong spatial dependences between the soil water content and the elevation, the contents of clay, silt, and sand, and the fractal dimension. The state-space approach simulated the soil water content much better than any equivalent linear regression method. The best state-space model included the elevation, the sand content, and the fractal dimension, which could explain 99％ of the variation in the soil water contents; the model accurately predicted the soil water contents along two transects. Consequently, the state-space analysis was verified to be an effective tool for estimating soil water contents in different soil layers on a hillslope on the Loess Plateau. |
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| Keywords: | slope scale soil moisture linear regression state-space model Loess Plateau |
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