Knowledge Resource Center for Ecological Environment in Arid Area
| 科尔沁沙地土壤水时空格局及植被耗水策略研究 | |
| 其他题名 | Spatio-temporal variability of soil moisture and its impact on plant water use in Horqin Sandy Land |
| 刘凯 | |
| 出版年 | 2015 |
| 学位类型 | 硕士 |
| 导师 | 彭新华 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 土壤水分是半干旱区科尔沁沙地植物生长发育的主要限制因子，决定土壤植被承载力。充分认识和把握科尔沁沙地土壤水分的时空变异特征及分布规律，有助于制定科学合理的固沙植被布局及水分管理措施。本研究选取典型科尔沁沙地丘间地，采用经典统计、地统计学及小波相干的方法，分析了2013年不同植被类型下0-160cm剖面土壤水分的时空变异特征及海拔对土壤水分分布的影响；利用Spearman秩相关系数和小波分析技术探索了区域土壤水分预测的新方法。基于稳定氢氧同位素技术，采用四种方法量化了科尔沁沙地先锋灌木小叶锦鸡儿2014年不同时期的水分利用策略。以期为科尔沁沙地土壤水资源管理和植被恢复重建提供科学依据。本论文主要研究结果如下： 1. 科尔沁沙地土壤水分时空变异特征 草本植被区和灌木丛区的土壤含水量的差异不显著，但显著低于裸沙区。土壤含水量在剖面上的变异系数在18％-177％之间，属于中等或强变异水平；土壤水分状况影响其变异性，湿季（7-8月）的变异性低于干季（9-10月）。草本植被和灌木通过截留降雨和根系吸水，加剧了土壤水分在空间上的变异。相同深度的空间自相关性为湿季大于干季。在湿季各层土壤含水量具有较好的空间连续性，而干季各层土壤水分变化均较为剧烈，土壤水分的破碎化程度较高。0-20cm深度土壤水分分布由降雨、海拔等多种因素共同影响，土壤水分与海拔存在中小尺度（18-72m）依赖性。在40-160cm深度，海拔与土壤体积含水量呈显著的负相关关系，土壤水分与海拔存在大尺度（72-150m）依赖性，海拔是影响该深度土壤水分分布的最重要因素。 2. 土壤水分时间稳定性的应用及其区域土壤水分预测 土壤水分具有强烈的时空变异特征，但是在不同时间，空间格局具有相似性。利用土壤水分时间稳定性概念，通过小波相干和Spearman秩相关分析，发现不同土层土壤含水量存在明显的正相关，其相关性既有空间结构的相似性，也具有不同尺度依赖性特征。通过筛选高相关性的土层，发现0-20 cm的土壤水分可以有效地预测20-40cm的土壤水分，而40-80 cm的土壤水分有效地预测80-160 cm、0-160 cm的土壤水分状况。结合Bootstrap重采样方法，可以进一步提高土壤水分预测精度。 3. 先锋固沙植物小叶锦鸡儿的水分利用策略 根据0-120 cm土层和小叶锦鸡儿植株体内的氢氧同位素分布特征，采用直观判断法、Iso-Source模型、正态分布模型和SIAR模型等四种方法对小叶锦鸡儿不同时期吸水策略进行定量化研究。结果表明小叶锦鸡儿在不同水分条件下采取不同的水分利用策略。以7月份为代表的湿季，小叶锦鸡儿的吸水深度在40cm左右，主要吸水区间为20-40cm；在干湿交替的9月份，小叶锦鸡儿的吸水深度下探至80cm左右，吸水区间主要位于80-120cm之间；而在以10月份为代表的干季，小叶锦鸡儿的吸水深度上移至40cm左右，吸水量以来自40-60cm土层居多。这四种方法的结果相似，但是各有优缺点。直观法能够大体判断小叶锦鸡儿根系吸水的变动区间，但无法量化；正态分布模型能够计算小叶锦鸡儿的平均吸水深度，但参数设置主观性较大；IsoSource模型可以定量化不同土层的水分贡献率，所需输入的参数较少（平均值），但该模型解的组合仅具有统计学意义而未必具有生态学的合理性；基于贝叶斯框架的SIAR模型既综合了前两种模型的优点，其模型结构本身又体现了小叶锦鸡儿根系吸水的生理结构特征，是一种更加合理的水源解析方法。 |
| 英文摘要 | The spatio-temporal variation and temporal stability are ubiquitous features of natural ecosystems, especially in semi-arid regions of Horqin. The purpose of this study was to 1) characterize variations in moisture content in the 0–160 cm soil layers in situ by means of intensive sampling in both space and time; 2) seek for a new method of predicting mean soil water content of a soil profile at a point scale, with the analysis of Spearman’s rank correlation coefficient and wavelet coherency; 3) determine potential plant water sources using observation and the models of Normal distribution、IsoSource and SIAR. Our main results were obtained as follows: 1. The spatio-temporal variation of soil water in Horqin sandy land Soil moisture content in the 0–160 cm soil layers exhibited a high degree of spatial and temporal variability. The degree of variability was dependent on water content. The CV of mean water content in the 0–160 cm soil layers spanned from 18% to 177%, indicating a level of middle or high variability. The result also suggested that CV was dependent more on soil moisture level than on soil depth. A large increase in CV for the soil layers was observed in dry condition. The spatial and temporal variations of the water content were related to vegetation types and soil moisture. At a fairly definite range, the heterogeneity of water content caused by spatial autocorrelation was evidently higher in wet season (July-August) than in dry season (September-October). The distance of spatial autocorrelation was different in different seasons and depths. It was more consecutive for spatial trend in wet season than in dry season. Meanwhile, it was identified that the topography and rainfall control the change in soil water variability for the 0-20cm soil depth, with the significant coherencies of small and medium scales. There were also significant coherencies at large scale between relative elevation and soil moisture for the 40-160cm soil layers, which indicating that the relative elevation had major control on soil moisture. 2. A new method to estimate the soil moisture of research site Spearman’s rank correlation analysis and wavelet coherency were used to measure the similarities between the overall spatial patterns at different depths and identify the scales of correlation in soil water series at any two different depths, which indicating that the shallow depths such as 0-20cm and 40-80cm were reliable to be used to predict soil moisture of deeper soil profiles. For a certain depth, the accuracy for predicting soil moisture was increased with the approach of Bootstrap Resampling. 3. The contribution of water sources in Caragana microphylla by stable isotopes of hydrogen and oxygen Both direct inference of hydrogen and oxygen isotopes between stem water and the soil water profiles, and three models of partitioning sources were applied to estimate the main depths of water uptake of Caragana microphylla in different growing seasons. According to both 18O and 2H signatures, shrubs in July used the shallow water from 20 to 40cm layer. In September, shrubs tended to shift their water uptake to deeper depths from 80 to 120cm layer. In October, shrubs harvested soil water from 40 to 60cm layer, which was similar to the uptaking patterns of July. Each stable isotope analytical approach above has distinct strengths and weaknesses. Interpretation of source of water uptake by direct inference for shrubs is less clear due to the more irregular isotopic gradient for the soil water. That is, the output, generated by direct inference, merely reflects a range of depths from which most water is being extracted instead of being quantified. The mean depth of water uptake by shrubs can be obtained using the Normal model. However, the parameter of this model is set arbitrarily. One of the main advantages of IsoSource model is the relatively limited amount of input data required (average isotope values of the stem and soil water). But perhaps |
| 中文关键词 | 科尔沁半干旱沙区 ; 水分时空变异 ; 土壤水分预测 ; 小叶锦鸡儿 ; 水分利用策略 |
| 英文关键词 | Horqin sandy land spatio-temporal variability soil moisture prediction Caragana microphylla plant water use strategies |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 土壤学 |
| 来源机构 | 中国科学院南京土壤研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287563 |
| 推荐引用方式 GB/T 7714 | 刘凯. 科尔沁沙地土壤水时空格局及植被耗水策略研究[D]. 中国科学院大学,2015. |
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