ISWC OpenIR  > 水保所知识产出(1956---)
黑河中游绿洲土壤物理性质 的 时空变异
李丹凤
Subtype博士
Thesis Advisor邵明安
2014-05
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword土壤质地 土壤水分 土壤有机碳 时空变异 随机模拟
Abstract

认识区域尺度土壤物理属性基本特征及其时间和空间变异是开展相关科学研究
的基础。在自然和人为因素的共同影响下,西北内陆黑河中游地区的景观格局复杂,
土壤属性在水平和垂直方向均存在明显分异。针对黑河中游地区绿洲和荒漠镶嵌分
布且相互影响的景观格局特征,本论文以绿洲和绿洲——荒漠过渡带的主要景观单
元(农田、湿地和荒漠)为研究对象,开展土壤含水率的连续定期监测、剖面不同
深度土壤水力特性指标、有机碳浓度以及土壤机械组成测定。借助经典统计学和马
尔可夫链理论,分析了研究区内土壤质地层次在空间不同方向的变异特征和转移规
律,实现对土壤质地剖面构型的三维模拟;估算了荒漠、农田和湿地三种主要景观
单元的剖面土壤有机碳储量,并分析其空间变异和分布格局;探讨了不同景观单元
剖面土壤储水量的时间稳定性特征以及鉴定代表性样点的简便方法;分析了夏玉米
生育期内土壤水量平衡组分的时间变化,并尝试对区域平均土壤储水量和深层渗漏
量进行估算;分析了不同景观单元土壤水力特性的空间变异性。获得了以下主要研
究成果:
(1) 研究区内土壤剖面共出现了七种质地类型,分别为砂土、壤质砂土、砂质壤
土、壤土、粘质壤土、粉粘壤土和粉粘土。粉粘壤土从未在表层 0-10 cm 土
壤中出现,而其余六种质地类型以不同的概率在表层土壤中出现,砂土的出
现概率最高。七种质地类型的层次厚度均服从对数正态分布,砂土和粉粘壤
土的层次厚度值较大,而砂质壤土和粘质壤土的层次较薄。研究区剖面某层
土壤之下多出现与该层土壤质地类型相似的两种土壤层次。壤质砂土的形成
很大程度上依赖于下层质地类型,而粘质壤土对上层质地类型的形成有至关
重要的影响。壤质砂土和壤土拥有相同上层(砂质壤土)的概率较高,而粉
粘壤土同时作为粘质壤土和粉粘壤土的上层的概率也较高。七种质地类型拥
有完全不同的上下质地层次组合。剖面相邻质地层之间的转移具有明显的马
尔可夫链特征,且马氏链是平稳的。一维嵌入型马尔可夫链模型能够较好地描述研究区土壤质地层次的垂向变化。剖面主要的质地层次组合为:砂—壤、
壤—砂、壤—粘、粘—壤。
(2) 土壤质地类型在空间 x,y 和 z 三个主方向转移的一维连续型马尔可夫链模型
可以较好地反映土壤质地类型在该方向的空间变异。模型的特征参数:分布
比例、平均长度和体现毗邻转移趋势的熵值系数可以很好地定量描述质地类
型的主要分布特征和转换规律。土壤质地类型在研究区任一方向的分布并非
完全随机,而是有一定的毗邻转移趋势。通过假定平均长度之比,根据垂直
方向质地类型的平均长度可以求算水平方向的平均长度,进而计算水平方向
的转移强度矩阵并建立一维连续型马尔可夫链模型,一定程度上解决了水平
方向数据稀疏的问题。将空间三个主方向的一维连续型马尔可夫链进行耦合
所建立的三维马尔可夫链模型可以较好地再现研究区土壤质地剖面构型,体
现了不同质地类型之间的交叉协相关关系。基于马尔可夫链的地统计模型在
模拟过程中方便地融入了地质统计信息,从而反映了质地类型分布的空间连
续性、不对称性和各向异性。
(3) 荒漠剖面土壤有机碳储量较低且分布均一,而灌溉农田和湿地的土壤有机碳
储量的垂直分布可以用对数函数拟合。土壤有机碳储量呈中等空间变异性,
具有较强的空间依赖性。湿地的土壤有机碳储量最高,荒漠最低。荒漠、灌
溉农田和湿地 3 m 深剖面中土壤有机碳的总储量分别为 59.4,149.6 和 174.4
Mg hm -2 ,其中,1-3 m 土层的有机碳储量分别占 67.0%,52.7%和 58.0%。粘
粒+粉粒含量是影响土壤有机碳储量的主导因子。由粘粒+粉粒含量解释的土
壤有机碳储量变异随荒漠和灌溉农田土层深度增加而增加,随湿地土层深度
增加而减少。本研究中未能解释的土壤有机碳储量变异可能由其他土壤属性
或者环境因素导致,例如:地形、植被和风力侵蚀强度等等。土壤样品有机
碳含量和机械组成测定中的试验误差可能导致根据所建立的传递函数计算土
壤容重的不确定性增加,从而影响土壤有机碳储量估算的准确性。由自然荒
漠开垦转变为灌溉农田并经历不足 40 年的耕种后,表层 0-0.3 m 土壤有机碳
含量增加 196.3%,而湿地开垦为灌溉农田并经过大约 30 年的耕种,导致表
层 0-0.3 m 土层的有机碳含量降低了 5.3%。绿洲土壤演化过程较缓慢,土地
利用方式转变在短期内不足以显著改变荒漠和湿地深层土壤有机碳含量。
(4) 斯皮尔曼秩相关系数和相对偏差分析均表明荒漠、农田和湿地剖面土壤储水
量具有较好的时间稳定性,且时间稳定性随土层深度的增加而增强。平均储水量的代表性样点为相对偏差标准差最小的点。没有一个代表性样点可以同
时准确估计剖面不同深度的土壤储水量。土壤质地和有机碳含量能够解释较
多的土壤储水量时间变异。荒漠 0-1,1-2 和 2-3 m 土层储水量代表性样点的
粘粒、粉粒、砂粒和有机碳含量的累积概率分布分别为:<0.25,<0.25,>0.75
和介于 0.5 至 0.75 之间。灌溉农田 0-1,1-2 和 2-3 m 土层储水量代表性样点
的粘粒、粉粒、砂粒和有机碳含量的累积概率分布分别为:>0.75,介于 0.5
至 0.75 之间,介于 0.25 至 0.5 之间,和介于 0.5 至 0.75 之间。土壤粘粒、粉
粒、砂粒和有机碳含量的累积概率分布可以作为间接途径来鉴别潜在的土壤
储水量代表性样点,通过后续的连续监测及分析,从而识别“真正”的代表
性样点。
(5) 尽管研究区内北部和南部农田夏玉米生育期相差 15 天,且灌溉次数和灌溉量
差异较大,北部和南部农田剖面 0-1 和 1-2 m 土层储水量均具有较好的时间
稳定性。相对偏差标准差最小的点可以准确估计平均土壤储水量(R 2 >0.91,
预测精度 PE>0.76,相对误差绝对值均值 MARE=0)。自 5 月 21 日至 9 月末
夏玉米收获后,北部和南部农田分别有大约 39%和 22%的降雨和灌溉水通过
深层渗漏损失。在夏玉米生育期内,北部和南部农田 0-1 m 土层平均储水量
代表性样点的 0-2 m 深层渗漏量可以大致估计区域平均深层渗漏量。
(6) 对于 0-10、10-20 和 20-30 cm 土层,饱和质量含水率呈中等程度空间变异性,
而饱和导水率呈强变异。湿地的饱和质量含水率最高,荒漠最低。荒漠土壤
的饱和导水率最大。饱和质量含水率、容重和饱和导水率均呈现中等程度的
空间结构性。饱和质量含水率的变程最大,而饱和导水率的变程最小。van
Genuchten 水分特征曲线公式对三种景观单元剖面实测水分特征曲线的拟合
效果均较好。荒漠土壤水分特征曲线呈现出高吸力时陡直、急剧下降,一定
吸力以下曲线缓平的特点,湿地土壤水分特征曲线较平缓。通过标定,可以
将景观单元内部各点变异的土壤水分特征曲线转换为各点均适用的、统一的
土壤水分特征曲线。
了解和明晰研究区土壤质地的剖面构型,分析土壤水力特性的空间变异性,可
以为准确获取土壤水分运动参数,从而模拟层状土中水分运动和溶质迁移过程奠定
基础。估算土壤有机碳储量并分析其空间变异及分布格局,有助于中游地区生态恢
复,在对不同景观单元的土壤碳固存潜力做出较准确评估的基础上,通过科学合理
规划土地利用,发挥干旱区潜在的土壤碳汇功能。分析不同景观单元剖面土壤水分的时空变异,估算绿洲农田土壤水量平衡并分析其组分的时间变化特征,能够为有
效调控农田土壤水分利用提供依据。本研究结果可以为黑河中游绿洲生态系统土壤
——植被——大气连续体水过程的科学研究提供基础数据,为提高中游地区农业灌
溉水利用效率,促进绿洲生态系统可持续发展提供参考。
关键词:土壤质地;土壤水分;土壤有机碳;时空变异;随机模拟

Other Abstract

Understanding the spatial heterogeneity and distribution patterns of soil physical
properties are fundenmental for various corresponding researches. Under the natural and
anthropogenic influences, landscape pattern is complex in the middle basin of the Heihe
River. Soil properties differ significantly both vertically and horizontally. Considering the
complex landscape pattern characterized by the coexistence and interaction of desert and
oasis, this study focused on the oasis and oasis-desert ecotone. Soil water contents to a
depth of 3 m in the 120 sampling points in the 100 km 2 study area were measured
regularly in 2011 and 2012. Soil organic carbon concentration, hydraulic parameters and
mechanical composition in the respective soil layers were measured. By using the
classical statistics and the one dimensional Markov chain theory, the transition
characteristics of different textural layers in the vertical and horizontal directions were
analyzed, and the three-dimensional visualization of soil textural profiles was realized.
Soil organic carbon stocks in the 0-3 m profiles in different landscapes were estimated,
and the spatial variability and distribution were analyzed. The temporal stability of soil
water storage in the profile of different landscapes was analyzed and an a priori approach
to identify the representative locations was proposed. The temporal variation of
components of soil water balance was analyzed in the irrigated cropland during the the
growing seasons of summer maize. Efforts were made to accurately estimate the spatial
mean soil water storage and spatial mean deep percolation. The spatial variability of
hydraulic parameters was also analyzed. The main results obtained were listed as follows:
(1) There were seven textural types in the study area, namely, sand, loamy sand,
sandy loam, loam, clay loam, silty clay loam and silt clay, respectively. Compared  with the non-occurrence of silty clay layers in the surface soil, another six types
of textural layers all occurred in the surface soil, while sand layers occurred with
a much higher probability than others. The layer thickness of each textural type
could be characterized as a lognormal distribution, with relatively thicker sand
and silty clay loam layers, and relatively thinner sandy loam and clay loam layers.
For a certain soil type in profile, layers occurred beneath it were mainly the two
whose textural types were similar to it, especially the one consisting of more fine
particles. The formation of loamy sand layers was much strongly dependent on
the lower layers, whereas clay loam layers had a key effect on the formation of
the upper layers. Loamy sand and loam layers had relatively high probability to
own sandy loam layers as upper layers, while silty clay loam layers had relatively
high probability to occur as upper layers of both clay loam and silty clay layers.
None of the seven textural types had the same combinations of upper and lower
layers simultaneously. Markov characteristic and the stability of the vertical
change of adjoined textural layers were verified. One-dimensional embedded
Markov chain model could accurately describe the vertical change of soil textural
layers. The main combinations of textural layers were sand-loam, loam-sand,
loam-clay, and clay-loam.
(2) The one-dimensional continuous Markov chain model constructed based on the
transition probability matrix in each of the principal x, y and z directions reflected
the spatial variability of soil textural layers in each direction of the study area. The
mean lens length, mean proportion and the entropy factors quantitively described
the characteristics of the distribution of, and transition among different textural
layers. The distribution of soil textural layers were not completely random, but
had the juxtapositioning tendencies. The mean lens length ratio was used to obtain
the lens length in the horizontal direction according to the lens length in the
vertical direction. Then the transition rate matrix was calculated and the
one-dimensional continuous Markov chain model was constructed in the
horizontal direction. This procedure solved the scarcity of data in the horizontal
direction to some extent. The one-dimensional continuous Markov chain models  in the the principal directions were interpolated into a three-dimensional Markov
chain model in an arbitrary direction, which reflected the cross co-correlations
among different textural layers, and reproduced the soil textural profiles in the
study area. By integrating the geostatistical information in the simulation, the
Markov chain model reflected the continuity in space, the asymmetry and the
anisotropy of the distribution of soil textural layers in the study area.
(3) Soil organic carbon (SOC) density was low and remained homogeneous in the
profiles of desert soil. The vertical distributions of SOC density in cropland and
wetland could be well described by logarithmic functions. Soil organic carbon
density presented moderate spatial variability and strong spatial dependence
across all depths. Wetland and desert could be easily recognized by the highest
and lowest SOC densities in the study area, respectively. Soil organic carbon
densities in the 3-m profiles were 59.4, 149.6 and 174.4 Mg ha -1 for desert,
cropland and wetland, respectively, among which, about 67.0, 52.7 and 58.0%
were stored in the 1-3 m layer, respectively. Clay plus silt (clay+silt) particles
were the major determinant of SOC in the study area. The variability in SOC
density explained by clay+silt content, increased with depth in desert and
cropland, but decreased with depth in wetland. The remaining SOC density
variability could be attributed to factors not included in this study, such as
geography, vegetation and the degree of erosion. Errors in the measurement of
SOC concentration and the distribution of soil-particle size, however, may
introduce uncertainty in the determination of soil bulk density and thus the
estimation of SOC density. The concentration of SOC in the 0-0.3 m layer
increased by 196.3% after the reclamation of native desert for less than 40 years
and decreased by 5.3% after the cultivation of wetland as cropland for less than
30 years. Short-term cultivation was insufficient to significantly alter SOC
concentration in the deeper layers of desert and wetland soils.
(4) Spearman’s rank correlation coefficients and the relative difference analysis both
indicated the temporal stability of soil water storage (SWS) in profiles of desert,
cropland and wetland, respectively. The temporal stability of SWS spatial pattern
increased with depth for desert and wetland. The representative location was  identified as the one with the smallest standard deviation of relative differences.
No single location could represent the spatial mean SWS of the three layers
simultaneously for each landscape. Soil texture and soil organic carbon content
could explain much of the temporal variability of SWS spatial pattern. At the
representative locations in the three layers of desert, the cumulative probabilities
for clay, silt, sand and soil organic carbon contents were < 0.25, < 0.25, > 0.75
and between 0.5 and 0.75, respectively, and the respective values in the cropland
were > 0.75, between 0.5 and 0.75, between 0.25 and 0.5 and between 0.5 and
0.75. An a priori approach was then proposed to select the potential representative
locations more conveniently in larger areas of the desert and cropland, from which
actual representative locations can be identified after long period measurement.
(5) Although the growing stages of summer maize, the amount and times of irrigation
all differed in the northern and southern croplands in the study area, soil water
storages in the northern and southern croplands were temporally stable. The
location with the lowest standard deviation of relative differences could
accurately estimate the spatial mean SWS with high coefficient of determination
(R 2 >0.91, P<0.001) and prediction accuracy (PE>0.76) and near-zero mean
absolute relative error (MARE=0). From May 21 to late September in the two
years, about 39% and 22% of the irrigation and rainfall lose as deep percolation in
the northern and southern croplands, respectively. Deep percolation at the
representative location of the 0-1 m spatial mean SWS could generally estimate
the spatial mean deep percolation in the 2 m soil profiles of the northern and
southern croplands.
(6) In the 0-10, 10-20 and 20-30 cm soil layers, saturated water content exhibitd
moderately spatial variability, and hydraulic conductivity and bulk density
showed strong variability over space. Wetland and desert had the highest and the
lowest saturated water content, respectively. Hydraulic conductivity in desert was
the largest. Saturated water content, bulk density and hydraulic conductivity all
showed moderatedly spatial dependence. Saturated water content and hydraulic
conductivity had the largest and smallest ranges over space, respectively. The  ormula of soil water characteristic curve proposed by van Genuchten fitted the
measured curves with high coefficinets of determination. In desert, the volumetric
soil water content drastically declined when the soil water sunction was high, and
kept nearly constant when the sunction was lower than a certain value. Soil water
retension curves in wetland were relatively gentle. The measured soil water
retension curves at various locations could be represented by a uniformed curve
for each landscape after scaling.
Insight into the soil textural profiles and the spatial variability of hydraulic
parameters can benefit studies on the processes of water movement and solute transfer in
stratified soils. Knowledge about soil organic carbon stock, its spatial variability and
distribution, and the influencing factors may help to make measures to restore the
desertified land. Basedon the accurate estimation of the carbon stock potentialities of
different landscapes, land use and landscape layout can be planed and managed
reasonably in the hope of playing the potential carbon sink role of arid region. Analyzing
the temporal variation of soil moisture in different landscapes and estimating the
temporal variations of soil water balance components in irrigated cropland during the
growing season of summer maize are fundemental for further study on the soil water
processes in the SPAC system of the oasis ecosystem in the middle basin of the Heihe
River, and favour for the improvement of irrigation water use efficiency and the
sustainability of oasis ecosystem in inner arid region, northwestern China.
Keywords: soil texture; soil moisture; soil organic carbon; temporal-spatial variability;
stochastic simulation

Language中文
Document Type学位论文
Identifierhttp://ir.iswc.ac.cn/handle/361005/9021
Collection水保所知识产出(1956---)
Recommended Citation
GB/T 7714
李丹凤. 黑河中游绿洲土壤物理性质 的 时空变异[D]. 北京. 中国科学院研究生院,2014.
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