2000—2020年中亚大型湖泊湖冰物候时空变化

湖冰物候变化特征是区域气候变化的敏感指示器之一。近几十年来,由于全球变暖和人为活动的影响,中亚地区的气候发生了显著变化,准确监测湖冰物候对于了解中亚地区气候变化具有重要的科学意义。通过对中亚地区7个大型湖泊(卡拉库尔湖、巴尔喀什湖、咸海、阿拉湖、斋桑泊、查蒂尔-科尔湖以及马卡科尔湖,面积>100 km²)2000—2020年的长期地表反射率数据、气象数据以及湖泊资料的综合分析,利用GIS相关技术探讨其湖冰物候特征及其影响因素。结果表明：(1)中亚地区的湖泊在9月中旬至11月上旬期间开始结冰,11月底到12月底会完全封冻,湖泊平均冻结时间为35 d;湖冰在3月底至5月中开始消融,4月上至6月上会完全消融,湖泊平均消融时间为18 d。(2)2000—2020年中亚7个湖泊中有5个湖泊开始冻结日期呈现延后的趋势,平均延后速率为4.86 d·(10a)^-1,巴尔喀什湖开始冻结日期呈现提前趋势,提前率为1.44 d·(10a)^-1。完全消融日期呈现提前的趋势,平均提前率为2.90 d·(10a)^-1。7...     

2000—2019年新疆大型湖泊湖冰物候时空变化特征

湖冰物候变化特征是全球气候变化过程的重要指示器。通过长时间序列MODIS数据、Landsat数据提取的湖泊数据集,综合分析了2000—2019年新疆大型湖泊湖冰物候的变化特征。结果表明：（1） 近20 a新疆大型湖泊的开始冻结日呈现提前和推迟2种变化趋势,开始冻结日呈现推迟趋势的湖泊分别为博斯腾湖、赛里木湖、艾比湖、吉力湖、乌伦古湖、萨利吉勒干南库勒湖和鲸鱼湖,且大部分湖泊的开始冻结日推迟趋势在0.51~1.53 d·a^-1之间;开始冻结日呈现提前趋势的湖泊有3个,分别为阿牙克库木湖（变化趋势为-1.04 d·a^-1）、阿克赛钦湖（变化趋势为-0.41 d·a^-1）、阿其克库勒湖（-0.31 d·a^-1）。（2） 湖冰完全覆盖期是重要的湖冰参数,湖冰覆盖期的延长或者缩短能够直接表示区域气候变化过程,新疆大部分湖泊湖冰覆盖期表现为缩短趋势,其中分布在新疆中北部的艾比湖、吉力湖和博斯腾湖等湖泊的湖冰覆盖期缩短较为明显,变化趋势分别为-1.76 d·a^-1、-2.13 d·a^-1和-0.81 d·a^-1;冰完全覆盖期延长的湖泊有3个,分别为阿牙克库木湖、阿其克库勒湖和鲸鱼湖,变化趋势分别为3.51 d·a^-1、1.54 d·a^-1和1.37 d·a^-1,这些湖泊均匀分布在昆仑山高原北翼。（3） 新疆大型湖泊湖冰物候变化特征是受其自身条件（湖泊形态因子、湖泊面积等）及气候变化（气温、降水量等）等多种因素共同作用的结果。本研究探讨了气候变化环境下的新疆大型湖泊湖冰物候的冻融趋势及其变化模式,同时应用不同遥感数据和研究方法识别了湖冰,证实了MODIS数据反演湖冰物候的可行性。     

1986—2017年呼伦湖湖冰物候特征变化

湖冰冰情物候特征是气候变化的敏感指示器之一。论文以呼伦湖为研究对象,基于MODIS、Landsat、GF-1、HJ-1等多源遥感影像及气象数据,利用RS和GIS技术综合分析了1986—2017年呼伦湖冰情物候特征及其对区域气候的响应。结果表明：① 呼伦湖年均开始冻结时间在10月下旬至11月上旬,从结冰开始到完全封冻的时间平均只有6.4 d;开始融冰时间在次年的4月上旬,消融期平均为32 d左右,到5月初或5月上旬湖冰完全融化。② 1986—2017年,在整个研究期呼伦湖完全封冻期呈现显著缩短趋势,平均缩短18.5 d;完全结冰时间有一定延迟现象,平均延后8.4 d;冰全部融化时间呈现提前趋势,平均提前了11.2 d。③ 湖冰冻结消融空间特征表现不同,冻结时先从湖岸形态较复杂地区结冰,然后由东岸向西岸迅速封冻,消融时先从湖泊西北岸开始,逐渐向东岸融化。④ 在影响因素方面,呼伦湖冰情特征主要受到区域气温、风速、风向等因素的影响。     

2000—2019年赛里木湖湖冰物候特征变化

基于MODIS影像、中国湖泊数据集及气象数据,综合分析了2000—2019年赛里木湖湖冰物候特征变化及影响因素.结果表明:(1)赛里木湖湖冰开始冻结和开始消融日期平均出现在11月2日和4月26日,湖冰完全封冻和完全消融日期平均出现在1月18日和5月17日,湖冰完全封冻期和湖冰冰期平均为99 d和196 d.(2)近20...     

2000-2016年青海湖湖冰物候特征变化（英文）

Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing(RS) datasets including the MOD09 GQ surface reflectance product, Landsat TM/ETM_+ images, and meteorological records to analyze spatiotemporal variations of ice phenology of Qinghai Lake between 2000 and 2016 applying both RS and GIS technology. We also identified the climatic factors that have influenced lake ice phenology over time and draw a number of conclusions. First, data show that freeze-up start(FUS), freeze-up end(FUE), break-up start(BUS), and break-up end(BUE) on Qinghai Lake usually occurred in mid-December, early January, mid-to-late March, and early April, respectively. The average freezing duration(FD, between FUE and BUE), complete freezing duration(CFD, between FUE and BUS), ice coverage duration(ICD, between FUS and BUE), and ablation duration(AD, between BUS and BUE) were 88 days, 77 days, 108 days and 10 days, respectively. Second, while the results of this analysis reveal considerable differences in ice phenology on Qinghai Lake between 2000 and 2016, there has been relatively little variation in FUS times. Data show that FUE dates had also tended to fluctuate over time, initially advancing and then being delayed, while the opposite was the case for BUS dates as these advanced between 2012 and 2016. Overall, there was a shortening trend of Qinghai Lake's FD in two periods, 2000–2005 and 2010–2016, which was shorter than those seen on other lakes within the hinterland of the Tibetan Plateau. Third, Qinghai Lake can be characterized by similar spatial patterns in both freeze-up(FU) and break-up(BU) processes, as parts of the surface which freeze earlier also start to melt first, distinctly different from some other lakes on the Tibetan Plateau. A further feature of Qinghai Lake ice phenology is that FU duration(between 18 days and 31 days) is about 10 days longer than BU duration(between 7 days and 20 days). Fourth, data show that negative temperature accumulated during the winter half year(between October and the following April) also plays a dominant role in ice phenology variations of Qinghai Lake. Precipitation and wind speed both also exert direct influences on the formation and melting of lake ice cover and also cannot be neglected.     

近20年青海湖水量变化遥感分析

青藏高原湖泊水量的变化是揭示全球气候变化及其区域水循环响应的重要信息载体。区别于常用的水文学方法,本文利用MODIS遥感影像和LEGOS高度计多年连续数据,基于湖泊水位—面积关系,探讨了湖泊水量变化的遥感分析方法,并以青藏高原面积最大的青海湖为例,揭示青海湖近20年来(2001-2016)湖泊水量年内与年际变化特征。主要结论为：青海湖湖泊面积在2001-2016年间整体扩张了187.9 km²,变化速率为11.6 km²/a;水位在2001-2014年间上升了1.15 m,变化速率为0.10 m/a。青海湖水位—面积关系表现为二次函数关系(相关系数R²=0.83)。基于水位—面积关系,进一步估算分析了青海湖水量平衡的净收支及其年内和年际变化。近20年来,青海湖水量总体呈增加趋势,其变化率约为4.5\times {10}^8m³/a。降水的增加与蒸发能力的下降是湖泊水量增加决定性的驱动因子。     

近10年来可可西里地区主要湖泊冰情时空变化

基于2000-2011年可可西里地区湖泊边界矢量数据、MODIS和Landsat TM/ETM+遥感影像和气象数据等资料,利用RS和GIS技术综合分析该地区主要湖泊冰情变化特征及其影响因素。结果表明：① 可可西里地区湖泊开始结冰和完全结冰出现在每年的10月下旬至11月上旬和11月中旬至12月上旬,湖泊由开始冻结至完全冻结持续时间约半个月;湖冰开始消融和完全消融时间较为分散,主要出现在每年的4月下旬至6月初和5月初至6月上旬,湖泊完全封冻期和封冻期为181 d和196 d。② 2000-2011年间,可可西里地区湖冰物候特征发生了显著变化,湖泊开始冻结和完全冻结时间推迟,湖冰开始消融和完全消融时间提前,湖泊完全封冻期和封冻期持续时间普遍缩短,平均变化速率分别为-2.21 d/a和-1.91 d/a。③ 湖冰物候特征及湖泊冰情演变是区域气候变化和湖泊自身条件共同作用的结果,其中气温、湖泊面积、湖水矿化度和湖泊形态是影响湖冰物候特征的主要因素,而湖泊热储量、地质构造等因素对湖冰演化的作用亦不可忽视。④ 可可西里地区湖泊冻结空间模式与消融过程相反,以湖冰由湖泊一岸扩展到另一岸的湖泊数量居多。     

2000-2013年西藏纳木错湖冰变化及其影响因素

湖冰物候事件是气候变化的敏感指示器。本文以西藏纳木错湖为研究对象,基于MODIS多光谱反射率产品数据监测了2000-2013年纳木错湖冰冻融日期,并结合多个气象站点的气象数据和实测湖面温度、湖面辐射亮温分析验证了湖冰变化的原因。纳木错湖冰变化较好地响应了区域气候变暖：开始冻结日期延迟和完全消融日期提前使湖冰存在期显著缩短(2.8 d/a)、湖冰冻结期增长、湖冰消融期缩短,其中消融期变化最为明显,平均每年缩短3.1 d。湖冰冻融日期的变化表明：2000年后纳木错湖冰冻结困难,消融加速,稳定性减弱。纳木错湖冰变化主要受湖面温度、湖面辐射亮温和气温变化的影响,它们可以作为气象因子来解释区域气候变化。     

巴丹吉林沙漠典型湖冰冻结-消融的空间模式差异

利用多源遥感数据解译、野外考察、原位观测等方法,分析了巴丹吉林沙漠腹地湖泊群湖冰冻结-消融空间模式及其差异的主要影响因素.结果表明:该沙漠存在4种冻结-消融空间模式,湖冰自湖岸蔓延至湖心、冻结早的区域融化晚;湖冰自湖岸蔓延至湖心、冻结早的区域融化早;湖冰自湖泊一岸扩展至另一岸、冻结早的区域融化晚;湖冰自湖泊一岸扩展至另...     

2000-2012年青海湖流域NPP时空分布特征

利用青海湖流域及周边地区气象资料和MODIS遥感影像等数据,结合地理信息系统技术和植被净初级生产力(NPP)估算模型(CASA模型),确定了2000-2012年青海湖流域NPP值,并评价了其时空分布特征。结果表明:2000-2012年青海湖流域年均NPP为4.77×10¹² g,空间分布以青海湖为中心,由低到高呈环带状,并呈由东南向西北递减趋势,在青海湖北侧河流中游地区年均NPP达到最高,为374.19 g·m^-2。2000-2012年NPP呈波动中逐渐增长趋势,年均增加4.81×10¹⁰ g;NPP年内变化显著,7月NPP达到全年最高值,占全年的28.77%。13年间流域内大部分地区NPP呈增长趋势,显著增长区主要分布在共和县江西沟乡、石乃亥乡和天峻县周围;青海湖北侧哈尔盖河上游、沙柳河中游地区则是主要减少区。多元回归分析表明归一化植被指数(NDVI)和降水是青海湖流域NPP的主要影响因素。     

Spatiotemporal characteristics of Qinghai Lake ice phenology between 2000 and 2016

Journal of Geographical Sciences - Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of...     

Spatial-temporal variations of lake ice phenology in the Hoh Xil region from 2000 to 2011

Lake ice phenology, i.e. the timing of freeze-up and break-up and the duration of the ice cover, is regarded as an important indicator of changes in regional climate. Based on the boundary data of lakes, some moderate-high resolution remote sensing datasets including MODIS and Landsat TM/ETM+ images and the meteorological data, the spatial-temporal variations of lake ice phenology in the Hoh Xil region during the period 2000–2011 were analyzed by using RS and GIS technology. And the factors affecting the lake ice phenology were also identified. Some conclusions can be drawn as follows. (1) The time of freeze-up start (FUS) and freeze-up end (FUE) of lake ice appeared in the late October–early November, mid-November–early December, respectively. The duration of lake ice freeze-up was about half a month. The time of break-up start (BUS) and break-up end (BUE) of lake ice were relatively dispersed, and appeared in the early February–early June, early May–early June, respectively. The average ice duration (ID) and the complete ice duration (CID) of lakes were 196 days and 181 days, respectively. (2) The phenology of lake ice in the Hoh Xil region changed dramatically in the last 10 years. Specifically, the FUS and FUE time of lake ice showed an increasingly delaying trend. In contrast, the BUS and BUE time of lake ice presented an advance. This led to the reduction of the ID and CID of lake. The average rates of ID and CID were–2.21 d/a and–1.91 d/a, respectively. (3) The variations of phenology and evolution of lake ice were a result of local and climatic factors. The temperature, lake area, salinity and shape of the shoreline were the main factors affecting the phenology of lake ice. However, the other factors such as the thermal capacity and the geological structure of lake should not be ignored as well. (4) The spatial process of lake ice freeze-up was contrary to its break-up process. The type of lake ice extending from one side of lakeshore to the opposite side was the most in the Hoh Xil region.     

2000—2018年青海湖流域气温和降水量变化趋势空间分布特征

青藏高原是全球气候变化的敏感区,气温和降水量的空间分布及变化趋势是气候变化研究的核心和基础,为开展生态环境变化评估提供基础资料。基于2000—2018年青海湖流域及其周边气象站观测数据,以高程为协变量,结合专业气象插值软件ANUSPLIN对气温和降水量进行空间插值。利用线性回归法分析了青海湖流域2000—2018年气温和降水量的变化趋势;利用双变量空间自相关分析法分析了青海湖流域气温和降水量空间匹配关系。结果表明：（1） 2000—2018年青海湖流域年平均气温呈显著增加趋势,平均增速为0.30 ℃·（10a）^-1,春季增温显著。（2） 降水量呈显著增加趋势,平均增速为73.20 mm·（10a）^-1,春夏季增速显著、秋季变化不明显、冬季趋于变干。（3） 青海湖流域气温和降水量空间匹配差异显著。从年尺度来看,气温和降水量莫兰指数（Moran’s I）为-0.66,表现为显著的负相关,面积比为67.56%,水热组合空间匹配不佳。从季节尺度来看,青海湖流域春季、夏季、秋季和冬季的气温和降水量Moran’s I分别为-0.49、-0.80、-0.32和-0.14,均为空间负相关。春夏季,流域低海拔区域气温逐渐升高,高海拔区域降水量逐渐增多,气温和降水量空间负相关面积逐渐增大,水热组合空间匹配不佳。值得强调的是青海湖巨大水体对环湖区局地气温的调节作用明显,是青海湖流域的“气候调节器”。     

基于MODIS数据的青海湖流域地表温度反演研究

地表温度是检测地球环境变化的重要指标,将地表温度与气象观测资料相结合可以更准确地检测地表履被、土壤湿度及水分含量变化。利用MODIS数据对青海湖流域地表温度进行反演,将反演结果与实测0 cm地面温度对比分析,显示反演的地表温度值与实测0 cm地面温度值平均差-1.05℃,在当前遥感反演地表温度的误差之内。结果表明地表温度和气象观测资料相结合,可以用于监测地表温度的变化,进而建立偏远地区气候资源数据库。     

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