遥感估算降水在西藏高原中的应用研究

采用遥感估算降水模型RFE 2.0(Rainfall Estimation Algorithm Version 2)模拟了2009年西藏高原的区域降水,并结合该地区气象站降水观测资料分别从日、月、年尺度上评价了该模型在西藏高原降水估算中的适用性,最后通过系数校正分析了2009年8月西藏高原降水量和年降水量的分布格局。结果表明,RFE2.0模型日降水量模拟值与观测值的相关系数在0.40以上的测站占46%,变化趋势较一致,但在日降水量较小时(接近零)模拟结果不稳定,在降水量较大时(>15mm)模拟结果一般会偏低;月平均降水量模拟结果与观测结果的相关系数在0.80以上的测站占62%,模拟结果较好地反映了观测结果的变化趋势,但个别月份的模拟结果会出现偏差。雨季降水量的模拟结果明显好于干季,为进一步提高模拟精度,确定雨季校正系数为1.133,干季校正系数为1.265;年尺度上降水量的模拟值与观测值的相关系数为0.368(P=0.026)。整体来看,遥感估算降水模型(RFE2.0)模拟的西藏高原降水结果较好,可为西藏高原降水模拟提供借鉴和参考。     

近20年来济南泉水补给区景观格局及其功能变化分析

本文以济南市泉水补给区为研究对象,利用GIS空间分析方法,在其景观变化分析的基础上,参照中国陆地生态系统的生态服务价值系数,估算了泉水补给区生态服务功能价值变化,重点分析景观变化对研究区生态服务功能的影响,进而为区域可持续发展和城市建设提供理论支持。结果表明:(1)20世纪90年代以来,泉水补给区耕地和草地景观面积呈减少趋势,林地、园地和建设用地景观面积大幅增加,其中,以林地面积增加最大;从景观类型转化看,耕地与其他景观类型相互转化较为密切,特别是与建设用地和园地之间的转化;林地面积的增加主要来源于草地和耕地;(2)泉水补给区生态服务功能价值主要由林地和耕地景观生态服务价值构成,其变化对该研究区生态服务价值变化起决定作用;从生态服务价值构成类型来看,该地区土壤形成与保护服务功能单项价值所占比重最大,约占总服务功能价值的20%;(3)90年代以来,该地区总生态服务价值呈增加趋势;从单项服务价值变化来看,水源涵养、废物处理和食物生产服务功能价值则呈现出减小趋势,其中,以水源涵养减幅最大,为4.01%,主要是由该区建设用地增加,地面硬化面积增多而引起的。研究认为,济南市南部山区作为重要的泉水补给区,其生态服务功能尤为重要,特别是水源涵养功能,因此,研究区水源涵养服务功能价值的降低应在今后南部山区开发过程中得到足够的重视与保护,逐步增加泉水补给区的整体生态效益。     

30年来呼伦贝尔地区草地植被对气候变化的响应(英文)

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is...     

近30 年来呼伦贝尔地区草地植被变化对气候变化的响应

基于1981-2006 年的GIMMS NDVI数据和2000-2009 年的MODIS NDVI数据反演呼伦贝尔地区草地变化,结合1981-2009 年该地区7 个气象站点的气温和降水数据,分别从年际变化、季节变化和月变化角度分析该地区草地变化对气候变化的响应。结果表明,从年际变化来看,降水是驱动草地植被年际变化的主要因素;从季节变化来看,草地植被生长在不同季节对水热条件变化的敏感性不同,春季草地植被生长对气温变化的敏感性较降水变化高,夏季和秋季草地植被的生长对降水变化的敏感性则高于对气温变化的敏感性,其中以夏季最为显著;从月变化来看,4 月和5 月草地植被变化受气温变化影响较明显;5-8 月与前一月降水变化关系密切,说明植被生长对降水变化具有一定的滞后性;4 月正值草本植物萌芽期,而4 月份草地生长与年气温变化关系最为密切,一定程度上说明4 月份表征植被生长的NDVI值增加可能是由于气候变暖引起的草地植被生长季提前产生的。综上所述,通过植被与气候要素月变化的关系可以具体地揭示气温和降水对草地植被生长的季节韵律控制。     

基于生态地理分区的青藏高原植被覆被变化及其对气候变化的响应

基于1982~2006年GIMMS NDVI数据集和地面气象台站观测数据,分析了青藏高原整个区域及各生态地理分区年均NDVI的变化趋势,并通过偏相关分析研究不同生态地理分区植被覆被变化对气温和降水响应的空间分异特征。研究表明:(1)近25年来,高原植被覆盖变化整体上趋于改善;高原东北部、东中部以及西南部湿润半湿润及部分半干旱地区植被趋于改善,植被覆盖较差的北部、西部半干旱和干旱地区呈现退化趋势;(2)高原植被变化与气温变化的相关性明显高于与降水变化的相关性,说明高原植被年际变化对温度变化更为敏感;(3)高原植被年际变化与气温和降水的相关性具有明显的区域差异,植被覆盖中等区域全年月NDVI与气温和降水的相关性最强,相关性由草甸向草原、针叶林逐步减弱,荒漠区相关性最弱。生长季植被覆盖变化与气温的相关性和全年相关性较一致,降水则不同,生长季期间高原大部分地区植被变化与降水相关性不显著。     

30年来呼伦贝尔地区草地植被对气候变化的响应(英文)

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal-interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.