青海湖地区潜在蒸散发变化特征及影响因子分析

蒸散发是陆面水循环的关键环节,在高寒、干旱生态环境中表现更加复杂。由于青海湖地区蒸发皿实测值存在数值偏大、长期观测器皿不统一的问题,本文采用Penman-Monteith(P-M)模型研究了青海湖地区天峻、刚察气象站潜在蒸散发的时空变化特征及影响因子。结果表明:(1)1967-2010年青海湖地区潜在蒸散发呈下降趋势,天峻站的平均下降速率为0.807 mm·a^(-1),刚察站为0.499 mm·a^(-1)。(2)青海湖地区存在“蒸发悖论”现象,潜在蒸散发的主要影响因子存在地域差异:天峻为风速主导;刚察则由气温和风速共同主导。(3)与蒸发皿实测值的潜在机理相比,P-M模型更强调水汽输送条件的影响作用;在能量供给条件上,P-M模型与气温关系更密切,而蒸发皿实测值与日照百分率相关性更高。

高寒草甸植物物候对温度变化的响应

植物物候是植物为适应其生长环境而呈现的规律性变化,是气候变化的指示器。为了解高寒植物物候对温度变化的响应,利用 1997 2010 年青海湖海北高寒草原生态监测站群落优势种矮嵩草物候观测资料和同时段的气象资料,应用偏最小二乘(PLS)回归定量分析了植物物候期变化特征、趋势及其与气温间的相互关系。结果表明:①1997-2010年青海湖地区年均温度总体上升,倾向率为 0.5℃/10a,其中年均最高温度和最低温度呈现出非对称型变化,最低温度显著升高且高于年均温升幅,倾向率为0.7℃/10a(P<0.05),而年均最高温度无明显变化。②1997 2010年间,矮嵩草平均返青期和枯黄期分别为4月18日和 10 月 2 日,矮嵩草返青期推迟,枯黄期提前,生长季长度缩短。③影响矮嵩草返青的关键时期为每年的 1 月和 3-4 月,1 月温度升高影响植物休眠进程进而延迟返青,而 3-4 月温度升高有利于热量积累使返青提前;影响矮嵩草枯黄的关键时期为7 月上中旬和 8 月,期间温度升高使枯黄期提前。④根据 PLS 分析和相关分析,最低温度在各关键时期内显著影响植物物候,而最高温度仅在 8 月对枯黄期影响通过显著性检验,因此最低温度是影响高寒草地矮嵩草物候期的关键因子。

陕北风沙过渡带植被净初级生产力变化特征及原因

基于光能利用率原理,采用CASA(Carnegie-Ames-Stanford-Approach)模型,实现了2000—2014年陕北风沙过渡带地区植被净初级生产力(NPP)估算,对该地区NPP时空变化以及驱动机制进行了定量化分析。结果表明:(1)2000—2014年陕北风沙过渡带NPP为6.71×1012gC·a^(-1),单位面积值为202.57gC·m^(-2)·a^(-1),受地貌和气候特征影响,植被空间异质性强,黄土区植被明显优于风沙区;(2)近十几年来该区域植被得到明显改善,NPP总体增速为10.98gC·m^(-2)·a^(-1)(R=0.85,P<0.01),植被增速存在空间差异,东南部的黄土区植被增长较快,西北风沙区植被增长较慢;(3)2000—2014年,降水、气温和辐射与NPP的相关系数分别为0.54(P<0.05)、-0.25、0.35,三者对植被增长的贡献量分别为3.95、0.71、2.75gC·m^(-2)·a^(-1)。这说明降水是气候因素中影响陕北风沙过渡带植被变化的主要因素;(4)近15年的植被恢复过程中,气候和人类活动都是重要的驱动因素,气候因子对植被增长的贡献更大,相对作用达到67.49%。区域内部,不同地区植被的主要驱动源存在差异,东部地区植被受气候因子主导,西部地区植被受人类活动主导。

Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data

Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations（temperature and precipitation） on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows：（1） Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons（i.e. spring and summer） exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only.（2） The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences.（3） Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator, and then, limits the plant growth of next spring.