| 引用本文: | 刘星廷，乔江波，杨涛，马雅莉，田涵洋，韩晓阳，朱元骏.2024.毛乌素沙地不同土地利用方式下土壤饱和导水率垂向分布特征[J].地球环境学报,15(4):665-674 |
| LIU Xingting, QIAO Jiangbo, YANG Tao, MA Yali, TIAN Hanyang, HAN Xiaoyang, ZHU Yuanjun.2024.Vertical distribution characteristics of soil saturated hydraulic conductivity under different land use patterns in the Mu Us sandy land[J].Journal of Earth Environment,15(4):665-674 |
|
| |
|
|
| 本文已被：浏览 3121次 下载 3008次 |
码上扫一扫！ |
|
|
| 毛乌素沙地不同土地利用方式下土壤饱和导水率垂向分布特征 |
|
刘星廷，乔江波，杨涛，马雅莉，田涵洋，韩晓阳，朱元骏
|
1. 西北农林科技大学水土保持研究所，杨凌 712100
2. 陕西省林业科学院，西安 710082
3. 国家林业和草原局长柄扁桃工程技术研究中心，榆林 719000
|
|
| 摘要: |
| 土壤饱和导水率（Ks）是土壤水文循环中的关键参数，然而当前对毛乌素沙地Ks的特征及影响因素还认识不足。通过测定毛乌素沙地4种土地利用方式下（农田、乔木、灌木和草地）0—100 cm土层的Ks和土壤基本理化性质（有机质、容重及颗粒组成），分析Ks的垂向变异性及其影响因素、建立估算Ks的传递函数。研究表明：农田、乔木和灌木地的Ks随着土层深度增加而逐渐增大，草地无明显变化。4种土地利用方式下的Ks均为中等程度的垂向变异性，平均值排序为草地（1.38 mm·min−1）＜乔木（1.76 mm·min−1）＜农田（1.82 mm·min−1）＜灌木（3.30 mm·min−1），其中灌木地的Ks显著高于其他土地利用方式（P＜0.05）。Ks与有机质、容重、土壤颗粒组成存在显著相关性，通过提取Ks的主导因素，用逐步多元回归方法建立了估算Ks的传递函数。相关研究有助于毛乌素沙地Ks参数的获取和预测。 |
| 关键词: 毛乌素沙地 土壤饱和导水率 垂直分布 传递函数 |
| DOI：10.7515/JEE222052 |
| CSTR： |
| 分类号: |
| 基金项目:陕西省科学技术厅青年项目（2021JQ-168）；中国科学院战略性先导科技专项（XDB40020303）；国家自然科学基金项目（41530854）；中国科学院西部青年学者B类（XAB2019B11） |
|
| Vertical distribution characteristics of soil saturated hydraulic conductivity under different land use patterns in the Mu Us sandy land |
|
LIU Xingting, QIAO Jiangbo, YANG Tao, MA Yali, TIAN Hanyang, HAN Xiaoyang, ZHU Yuanjun
|
1. Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
2. Shaanxi Academy of Forestry, Xi’an 710082, China
3. Engineering Technology Research Center for Amygdalus Pedunculata, National Forestry and Grassland Administration, Yulin 719000, China
|
| Abstract: |
| Background, aim, and scope Soil saturated hydraulic conductivity (Ks) is a key parameter in the hydrological cycle of soil; however, we have very limited understanding of Ks characteristics and the factors that influence this key parameter in the Mu Us sandy land (MUSL). Quantifying the impact of changes in land use in the Mu Us sandy land on Ks will provide a key foundation for understanding the regional water cycle, but will also provide a scientific basis for the governance of the MUSL. Materials and methods In this study, we determined Ks and the basic physical and chemical properties of soil (i.e., organic matter, bulk density, and soil particle composition) within the first 100 cm layer of four different land use patterns (farmland, tree, shrub, and grassland) in the MUSL. The vertical variation of Ks and the factors that influence this key parameter were analyzed and a transfer function for estimating Ks was established based on a multiple stepwise regression model. Results The Ks of farmland, tree, and shrub increased gradually with soil depth while that of grassland remained unchanged. The Ks of the four patterns of land use were moderately variable; mean Ks values were ranked as follows: grassland (1.38 mm·min−1) ＜ tree (1.76 mm·min−1) ＜ farmland (1.82 mm·min−1) ＜ shrub (3.30 mm·min−1). The correlation between Ks and organic matter, bulk density, and soil particle composition, varied across different land use patterns. A multiple stepwise regression model showed that silt, coarse sand, bulk density, and organic matter, were key predictive factors for the Ks of farmland, tree, shrub, and grassland, in the MUSL. Discussion The vertical distribution trend for Ks in farmland is known to be predominantly influenced by cultivation, fertilization, and other factors. The general aim is to improve the water-holding capacity of shallow soil on farmland (0—30 cm in depth) to conserve water and nutrients; research has shown that the Ks of farmland increases with soil depth. The root growth of tree and shrub in sandy land exerts mechanical force on the soil due to biophysical processes involving rhizospheres, thus leading to a significant change in Ks. We found that shallow high-density fine roots increased the volume of soil pores and eliminated large pores, thus resulting in a reduction in shallow Ks. Therefore, the Ks of tree and shrub increased with soil depth. Analysis also showed that the Ks of grassland did not change significantly and exhibited the lowest mean value when compared to other land use patterns. This finding was predominantly due to the shallow root system of grasslands and because this land use pattern is not subject to human activities such as cultivation and fertilization; consequently, there was no significant change in Ks with depth; grassland also had the lowest mean Ks. We also established a transfer function for Ks for different land use patterns in the MUSL. However, the predictive factors for Ks in different land use patterns are known to be affected by soil cultivation methods, vegetation restoration modes, the distribution of soil moisture, and other factors, thus resulting in key differences. Therefore, when using the transfer function to predict Ks in other areas, it will be necessary to perform parameter calibration and further verification. Conclusions In the MUSL, the Ks of farmland, tree, and shrub gradually increased with soil depth; however, the Ks of grassland showed no significant variation in terms of vertical distribution. The mean K s values of different land use patterns were ranked as follows: shrub ＞ farmland ＞ tree ＞ grassland; all land use patterns showed moderate levels of variability. The Ks for different land use patterns exhibited differing degrees of correlation with soil physical and chemical properties; of these, clay, silt, sand, bulk density, and organic matter, were identified as important variables for predicting Ks in farmland, tree, shrub, and grassland, respectively. Recommendations and perspectives In this study, we used a stepwise multiple regression model to establish a transfer function prediction model for Ks for different land use patterns; this model possessed high estimation accuracy. The ability to predict Ks in the MUSL is very important in terms of the conservation of water and nutrients. |
| Key words: Mu Us sandy land saturated hydraulic conductivity vertical distribution transfer function |
|
|
|
|
