1. 河海大学农业科学与工程学院,南京,210098
2. 中国科学院教育部水土保持与生态环境研究中心, 黄土高原土壤侵蚀与旱地农业国家重点实验室, 陕西 杨凌,712100
[ "杨震(1996-),男,硕士研究生,主要从事水土保持研究。E-mail:865845142@qq.com" ]
纸质出版:2020
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杨震, 黄萱, 佘冬立. 晋西北黄土丘陵区土壤饱和导水率的空间分布特征及影响因素[J]. 水土保持学报, 2020,34(6):178-184.
YANG Zhen, HUANG Xuan, SHE Dongli. Spatial Distribution Characteristics and Influencing Factors of Soil Saturated Hydraulic Conductivity in the Loess Hilly Region of Northwestern Shanxi[J]. 2020, 34(6): 178-184.
杨震, 黄萱, 佘冬立. 晋西北黄土丘陵区土壤饱和导水率的空间分布特征及影响因素[J]. 水土保持学报, 2020,34(6):178-184. DOI: 10.13870/j.cnki.stbcxb.2020.06.026.
YANG Zhen, HUANG Xuan, SHE Dongli. Spatial Distribution Characteristics and Influencing Factors of Soil Saturated Hydraulic Conductivity in the Loess Hilly Region of Northwestern Shanxi[J]. 2020, 34(6): 178-184. DOI: 10.13870/j.cnki.stbcxb.2020.06.026.
土壤饱和导水率(Ks)是反映土壤入渗性能与土壤持水能力的重要参数,为探究流域尺度下土壤Ks的空间分布特征及影响因素,更好地掌握土壤水文过程与调节机理,选取晋西北黄土丘陵区朱家川流域横向梯度(上游、中游、下游)不同土地利用方式下的土壤(70个样点)为研究对象,采用定水头法测定土壤Ks,并获取样点地形因子和其他土壤理化性质,通过建立土壤Ks偏最小二乘回归模型(PLSR),分析影响土壤Ks空间分布格局的主要因素。结果表明:(1)除土壤容重和砂粒含量为弱变异外,区域土壤理化性质其余因子均为中等变异;土壤Ks在横向梯度下表现为上游 > 中游 > 下游;(2)不同土地利用方式下土壤Ks差异显著(P<0.05),由高到低顺序为林地 > 农地 > 草地;(3)林地(VIP=1.997)与草地(VIP=1.710)利用方式、土壤容重(VIP=1.548)、土壤有机质(VIP=1.323)、大团聚体(VIP=1.266)、粉粒含量(VIP=1.062)和黏粒含量(VIP=1.049)是土壤Ks变化的主要因素,林地利用方式影响程度最大。土地利用、土壤性质、地形因子均是影响黄土丘陵区土壤Ks空间分布的主要因素,是用来模拟预测土壤Ks空间分布的重要因子。
Soil saturated hydraulic conductivity (Ks) is an important parameter reflecting soil infiltration performance and soil water holding capacity. Understanding the spatial distribution characteristics and influencing factors of soil Ks at the watershed scale is helpful to better comprehend soil hydrological processes and regulation mechanisms. Totally 70 soil samples were collected from different land use patterns in the horizontal gradients (upstream
midstream
downstream) of the Zhujiachuan watershed in the Loess Hilly Region of Northwestern Shanxi. The soil Ks was determined by constant-head method
the topographical factors and physical and chemical properties of the sample points were also measured to establish partial least-squares regression model of soil Ks
and then the main factors affecting the spatial distribution pattern of soil Ks were analyzed. The study showed that: (1) Soil bulk density and sand content showed a weak variation
but the other physical and chemical properties showed the moderate variations. The soil Ks performance in the horizontal gradients was upstream > midstream > downstream; (2) Soil Ks varied greatly with different land uses (P<0.05)
the sequence was forest land > agricultural land > grassland; (3) Forest land (VIP=1.997)
grassland (VIP=1.710)
soil bulk density (VIP=1.548)
soil organic content (VIP=1.323)
macro-aggregates (VIP=1.266)
silt content (VIP=1.062) and clay content (VIP=1.049) were the main factors influencing soil Ks
among which the use of forest land gave the greatest impact. Land use
soil properties and topographical factors were the main factors affecting the spatial distribution of soil Ks in the Loess Hilly Region and could be used to simulate and predict the spatial distribution of soil Ks.
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