沙坡头沙地人工植被的群落学特征

本文论述了包兰铁路沙坡头地段铁路北沙地人工植被的种类组成、结构及动态。根据预测, 如果人工植被试验区象目前这样继续封禁下去, 再过20年, 地面固定较好, 人工栽植地段的花棒、柠条以及目前能够天然下种进行更新的油蒿, 都会先后从人工植被试验区退出。作者对进一步深入研究这一工作还提出了几点建议。     

江河源区NDVI时空变化及其与气候因子的关系(英文)

The source regions of the Yangtze and Yellow rivers are important water conservation areas of China. In recent years, ecological deterioration trend of the source regions caused by global climate change and unreasonable resource development increased gradually. In this paper, the spatial distribution and dynamic change of vegetation cover in the source regions of the Yangtze and Yellow rivers are analyzed in recent 10 years based on 1-km resolution multitemporal SPOTVGT-DN data from 1998 to 2007. Meanwhile, the correlation relationships between air temperature, precipitation, shallow ground temperature and NDVI, which is 3×3 pixel at the center of Wudaoliang, Tuotuohe, Qumalai, Maduo, and Dari meteorological stations were analyzed. The results show that the NDVI values in these two source regions are increasing in recent 10 years. Spatial distribution of NDVI which was consistent with hydrothermal condition decreased from southeast to northwest of the source regions. NDVI with a value over 0.54 was mainly distributed in the southeastern source region of the Yellow River, and most NDVI values in the northwestern source region of the Yangtze River were less than 0.22. Spatial changing trend of NDVI has great difference and most parts in the source regions of the Yangtze and Yellow rivers witnessed indistinct change. The regions with marked increasing trend were mainly distributed on the south side of the Tongtian River, some part of Keqianqu, Tongtian, Chumaer, and Tuotuo rivers in the source region of the Yangtze River and Xingsuhai, and southern Dari county in the source region of the Yellow River. The regions with very marked increasing tendency were mainly distributed on the south side of Tongtian Rriver and sporadically distributed in hinterland of the source region of the Yangtze River. The north side of Tangula Range in the source region of the Yangtze River and Dari and Maduo counties in the source region of the Yellow River were areas in which NDVI changed with marked decreasing tendency. The NDVI change was980 Journal of Geographical Sciences positively correlated with average temperature, precipitation and shallow ground temperature. Shallow ground temperature had the greatest effect on NDVI change, and the second greatest factor influencing NDVI was average temperature. The correlation between NDVI and shallow ground temperature in the source regions of the Yangtze and Yellow rivers increased significantly with the depth of soil layer.     

西藏砂生槐种群结构与点格局分析

以雅鲁藏布江中游砂生槐种群为研究对象,采用相邻格子法分别在半固定沙地和固定沙地设立20 m×30 m的样方,通过对不同生境条件下种群年龄结构、空间分布格局以及不同径级空间关联等方面的研究,探讨了砂生槐种群结构与空间格局及其影响因素。结果表明,半固定沙地和固定沙地砂生槐种群年龄结构与种群（总和）特征基本一致,均呈基部宽、顶部狭,随径级增大个体数减少的趋势。砂生槐种群中幼龄体的空间分布呈明显的集群分布,随着年龄等级的增加,由中幼龄体不同尺度下的集群和随机分布交替出现过渡到大龄等级下的随机分布。半固定沙地砂生槐种群不同径级的空间关系有正相关和不相关两种方式,变化趋势是随着径级差距的加大,幼体与其他径级砂生槐的空间正相关性逐渐减弱;固定沙地砂生槐种群不同径级的空间关系均呈正相关。     

西藏高原气候变化及其差异性

利用西藏高原38个气象站点1961-2010年逐月气象资料,采用气候倾向率、累积距平及CIS空间插值方法,研究了近50 a来西藏及各分区气象要素的时空演变规律,揭示了西藏不同区域气候变化的差异性。结果表明:近50a来,西藏总体呈现出日照略增、温度升高、降水增加、风速减小、平均相对湿度略有上升趋势,年平均日照时数倾向率3.04 h/10 a,年平均气温倾向率0.58℃/10 a,多年平均降水量倾向率12.5 mm/10 a,年平均风速倾向率-0.13 m·s~(-1)·(10 a)~(-1)、年平均相对湿度倾向率0.1%/10a。根据地貌分区的各区域气象要素分布及变化程度差异较大,藏北高原区日照时数最长,但呈减少趋势,藏东高山深谷区日照时数最短,增加趋势明显;藏南山原湖盆谷地区年平均气温最高,升温幅度较小,藏北高原区年平均气温最低,升温幅度最大;藏东高山深谷区年平均降水量最多,藏北高原区最少,藏南山原湖盆谷地区降水量增加较明显;藏北高原区平均风速最大,藏东高山深谷区最小,所有区域平均风速均呈先增加后减小趋势;喜马拉雅高山区、藏东高山深谷区相对湿度较大,藏北高原区、藏南山原湖盆谷地区较小,西藏各区平均湿度均有所上升。     

近30年西藏雪深时空变化及其对气候变化的响应

积雪深度是表征积雪特征的重要参数,也是气候变化区域响应敏感因素。利用1979—2010年逐日雪深被动微波遥感数据以及同期气象资料,对西藏雪深时空变化特征及其与气候因子的响应关系进行了分析。结果表明:32 a来,西藏雪深呈显著增加趋势,气候倾向率为0.26 cm/10 a;1999年以后,雪深则表现为下降趋势,气候倾向率为-0.35 cm/10 a。四季平均雪深中,春季雪深的变化对年平均贡献最大,二者相关系数高达0.88。高原雪深异常偏多年份主要在20世纪90年代,但并未发生气候突变。周期分析表明,西藏雪深存在准6~7 a振荡的显著周期。西藏雪深呈四周山地雪深大,中部腹地雪深小的空间格局,且受海拔影响有明显的陡坎效应,绝大部分地区雪深变化趋势倾向率在-0.08~0.08 cm/a,百分比达到74.6%;逐像元回归分析表明,雪深呈增加趋势的像元数占全区像元总数的76.9%,有减少趋势的仅占23.1%。西藏雪深与气温、降水、风速和日照时数存在明显的统计和空间相关性,整体表现为雪深与气温、风速、日照时数呈负相关,而与降水呈正相关。多元回归分析表明,春秋季雪深模拟值与实测值的相关系数均达0.6以上,通过了0.01的显著性检验;夏冬季雪深回归模型的复相关系数只有0.4~0.5,且未通过0.05显著性检验。