新疆青海地区现代地表沉积物颜色指标与气候参数关系

干旱区地理 ›› 2017, Vol. 40 ›› Issue (2): 355-364.

新疆青海地区现代地表沉积物颜色指标与气候参数关系

严永耀^1,2, 安聪荣³, 苗运法¹, 宋友桂⁴, 杨胜利⁵, 蔡晓敏^1,2

1. 沙漠与沙漠化重点实验室中国科学院西北生态环境资源研究院, 甘肃兰州 730000;
2. 中国科学院大学, 北京 100049;
3. 中国石油大学(华东) 地球科学与技术学院, 山东青岛 257061;
4. 黄土与第四纪地质国家重点实验室中国科学院地球环境研究所, 陕西西安 710061;
5. 兰州大学资源环境学院, 甘肃兰州 730000

收稿日期:2016-09-21 修回日期:2016-12-25 出版日期:2017-03-25
通讯作者: 苗运法,男,博士,副研究员.Email:miaoyunfa@lzb.ac.cn
作者简介:严永耀(1992-),女,河南驻马店人,硕士研究生,研究方向为自然地理学与古气候变化.Email:yanyongyao@lzb.ac.cn
基金资助:
黄土与第四纪地质国家重点实验室开放基金（SKLLQG1515，SKLLQG1301）；中国科学院盐湖资源与化学重点实验室开放基金（KLSLRC-KF-13-DX-1）

Relationship between color index of modern surface sediment and climate parameters in the region of Xinjiang and Qinghai

YAN Yong-yao^1,2, AN Cong-rong³, MIAO Yun-fa¹, SONG You-gui⁴, YANG Shen-li⁵, CAI Xiao-min^1,2

1. Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. School of Geosciences, China University of Petroleum(East), Qingdao 257061, Shandong, China;
4. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, Shanxi, China;
5. Northwest Institute of Eco-Environment and Resources, Lanzhou University, Lanzhou 730000, Gansu, China

Received:2016-09-21 Revised:2016-12-25 Online:2017-03-25

摘要/Abstract

摘要： 为更好理解现代沉积物颜色指标（亮度L^*、红度a^*和黄度b^*）与气候参数（年平均温度和年平均降水量）关系，采集新疆和青海地区大空间尺度上104 个地表沉积物样品，运用空间分析与传统统计方法相结合探讨了颜色指标与气候参数的空间趋势特征及局部空间关系，并分析二者之间的相关性。研究发现无论是年平均温度和L^*、a^*和b^*的趋势分析还是空间关联分析，年平均温度和L^*、a^*和b^*在研究区域的不同地区具有不同的关系特征. 年平均降水量和L^*、a^*和b^*的趋势分析在研究区域的不同地区呈现负相关，空间关联分析中在东南部和中部年平均降水量与L^*、a^*和b^*呈现负相关的关系特征.该地区现代表土沉积物颜色指标（L^*、a^*和b^*）与年平均降水量呈负相关关系，相关系数分别为-0.26、-0.35 和-0.29（P=0.01），与年平均温度相关性不显著，可能与年平均温度和颜色指标在研究区域的不同地区具有不同的相关性有关。现代地表沉积物颜色指标与气候参数的相关性高低，与采样点的数量、分布密度、跨度、地带性土壤类型、相关性分析方法等有关. 本研究对新疆和青海地区地表沉积物颜色指标与气候参数之间关系的探讨，为深入探讨该地区地层颜色指标与古气候环境关系提供参考依据。

关键词: 颜色指标, 气候参数, 地表沉积物, 相关性, 新疆, 青海

Abstract: In order to better understand the relationship between color index of modern sediment(brightness L^*, redness a^* and yellowness b^*)and climate parameters(the annual average temperature and annual average precipitation), 104 surface sediment samples were collected at a large scale in the region of Xinjiang and Qinghai. These samples were analyzed by combining the spatial econometric and traditional statistical approachs to explore their trend and local spatial relations and correlation. The results show that there are different characteristics of the annual average temperature and L^*, a^* and b^* in different regions of the study area based on analysis of the general trend and spatial correlation. Besides, annual average precipitation and L^*, a^* and b^* in the study area all presents negative correlation in the general trend. According to the spatial relation analysis, they also have inverse correlation in the southeast and central region of the study area. The color index(L^*, a^* and b^* )are negatively related with annual average precipitation, of which the correlation coefficients are 0.26, 0.35 and 0.29 respectively(P=0.01), but the color index has no significant correlation with annual average temperature, which may relate with the difference of their relationships in different regions. The correlation of modern sediment color index and climate parameters links with some factors including the number of sampling points, distribution density, the span of space, zonal soil and different correlation analysis methods. This study can provide more references for further exploring the relationship between color index of stratum and paleoclimate.

Key words: color index, climate parameters, modern surface sediment, correlation, Xinjiang, Qinghai

中图分类号:

P512.2

严永耀, 安聪荣, 苗运法, 宋友桂, 杨胜利, 蔡晓敏. 新疆青海地区现代地表沉积物颜色指标与气候参数关系[J]. 干旱区地理, 2017, 40(2): 355-364.

YAN Yong-yao, AN Cong-rong, MIAO Yun-fa, SONG You-gui, YANG Shen-li, CAI Xiao-min. Relationship between color index of modern surface sediment and climate parameters in the region of Xinjiang and Qinghai[J]. , 2017, 40(2): 355-364.

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