Using MODIS MOD17A3 Net Primary Productivity (NPP) data and meteorological data, we analyzed the temporal and spatial variation of NPP, inter-annual changes of climatic factors and the correlative relationship between NPP and climatic factors in Xilingol grassland from 2000 to 2010. The NPP of the Xilingol grassland varied from 0 to 0.5 kg C·m -2 which decreased from east to west. From 2000 to 2010, the NPP fluctuated annual that increased in generaland evenly distributed. The variations of climatic factors in typical and desert grassland were similar. The cumulative average temperature, average relative humility and cumulative sunshine duration in April to August had a downward trend, while the average wind speed had an increasing trend. The cumulative precipitation hadan increasing trend in desert grassland, but had a slight decreasing trend in typical grassland. For most climatic factors, they had more dramatic fluctuations in typical grassland than in desert grassland. For the two types of grassland in the study area, the grassland NPP has very significantly negative correlation ( P <0.001) with cumulative average temperature and average daily minimum temperature in April to August, and had slight negative correlation with cumulative sunshine duration and average wind speed, while had strong positive correlation ( P <0.001) with cumulative precipitation and average relative humility. In conclusion, the typical grassland had stronger response ability to the changes of climatic factors than the desert grassland.

利用MODIS MOD17A3植被初级生产力数据产品及地面气象观测数据,研究分析了2000-2010年锡林郭勒盟草原NPP的时空变化特征、各气候因子的年际变化特征及NPP与气候因子的相关关系。结果表明,锡林郭勒盟草原NPP的分布由东向西逐渐减小,值大多分布在0~0.5 kg C·m -2 ;2001-2010年NPP呈现波动变化,整体呈增加趋势,空间分布上更加趋于均匀分布;2000-2010年,研究区内典型草原和荒漠草原各气候因子的变化趋势大致相同,4-9月累计平均气温、平均相对湿度和累计日照时数呈减小趋势,平均风速呈增加趋势,累计降水量典型草原呈轻微减少趋势,荒漠草原呈增加趋势,趋势斜率分别为-0.026和1.044 5,典型草原大部分气候因子的波动程度大于荒漠草原;研究区内两种类型的草原的NPP均与4-9月累计平均气温、平均日最低气温有较强的负相关关系(显著水平分别为 P< 0 . 001和 P <0.01),与累计降水量、平均相对湿度有较强的正相关关系(显著水平为 P <0.01),与4-9月累计日照时数、平均风速呈负相关关系但相关关系较弱,荒漠草原对气候因子变化的响应总体不如典型草原敏感。

摘　要： 导致青藏高原草地退化的因子很多，主要有气候、野生动物和人类活动等。在气候因素中以气温和降水的影响为主，短期内气候的变化不会成为草地退化的主导因素，从长期来看，气候变化与草地退化之间的相互作用可引起草地生态系统的退化；野生动物因素中主要以植食性小哺乳动物的影响为主，其危害程度取决于其种群数量的高低，同时大型野生草食动物对草地退化的影响也不容忽视；人类活动因素中主要以家畜过度放牧为主，在一定程度上，家畜放牧强度的高低直接决定草地的退化程度；草地退化是多种因素综合作用的结果。不同地区导致草地退化的主要因素不尽相同，导致青藏高原草地退化最主要的因子是过度放牧和植食性小哺乳动物种群爆发。针对退化的原因，提出了青藏高原退化草地恢复与管理过程中应注意的事项。

以新疆具有完整资料的90个气象站点中的75个站点的基本地理信息和1995-2008年平均降水数据资料为基础,以ArcGIS为数据处理平台,分别采用反距离加权法、径向基函数法、普通克里格法、协克里格法对新疆降水空间分布进行插值,并通过没有参与插值的15个气象站点的实际降水量与插值结果进行相对误差分析.研究结果表明:在年降水量插值的4种算法中,普通克里格法(Kriging)的计算精度最高.利用最优的插值方法的栅格降水空间数据库建立的多年降水资源信息系统,可快速计算研究区内任一地域单元中降水的总量及其空间变化,对降水资源模拟、工程规划、发展战略及决策管理均具有重要的意义.

For improving the effect of annual precipitation spatial interpolation in Xinjiang area, the spatial interpolation of annual precipitation had been performed and the precision was comparatively validated by using of deterministic function and geostatistical interpolation methods, through adding some simulative meteorological stations as a supplement of spatial variability information of fact station data. The results showed that the precipitation spatial variability information and interpolation precision had effectively enhanced by establishing simulative weather stations through finding weather stations influence zone by investigating appropriate sampling interval distance of multiyear average precipitation with geostatistical analysis, and founding multiple regression equation between precipitation and longitudelatitude, elevation, slope, aspect and NDVI(normalized difference vegetation index). MRBF was the best and most appropriate method for spatial interpolation of precipitation among three interpolation methods of Inverse Distance Weighted (IDW), Multiquadratic Radial Basis Functions (MRBF) and Ordinary Kriging (OK) in Xinjiang area. By using stepwise and integrated methods, namely establishing simulative meteorological stations through multiple regression and conducted spatial interpolation of precipitation by MRBF method, considering significant factors and spatial variability, removing boundary effect of multiple regression, enhancing precipitation spatial geostatistical information, the annual precipitation spatial distribution dataset of this area with higher precision had been obtained.〖JP〗

为提高新疆地区年降水量的空间插值效果，通过增设模拟气象站点、补充实测站点数据空间变异性信息，采用确定性函数和地统计学的综合插值方法进行新疆地区年降水量的空间插值与比较验证分析。结果表明：（1）利用地统计方法分析多年平均降水量的最佳抽样间距，确定气象站点影响区，建立降水量与经纬度、高程、坡度、坡向、NDVI间的多元回归方程，增设模拟气象站点，可以有效增加降水量空间变异信息，显著提高降水量空间插值效果。（2）通过对比发现，反距离加权法（IDW）、复二次径向基函数法（MRBF）和普通克里格法（OK）3种空间插值方法中，MRBF的插值效果最好，是该地区降水空间分布的最适插值方法。（3）在新疆地区采用逐步综合的方法，即以多元回归方法增设模拟气象站点和MRBF进行年降水量空间插值，全面考虑影响因子与空间变异性，去除了多元回归带来的“边界效应”，弥补了实测降水量空间信息，得到了具有较高精度的年降水量数据集。

The spatial interpolation methods of inverse distance weight,trend surface,Kriging,Co-kriging and combined method are utilized for the comparison study on spatial interpolation of January mean air temperatures from 1961 to 1990 in the Qinghai-Xizang Plateau(QXP).The results of the former four methods display a nearly similar spatial structure.Moreover,the center of low temperature in northwestern QXP can not be revealed due to uneven spatial distribution of meteorological stations and limited obsevations.In the combined method,monthly mean air temperature is divided into structural and stochastic components,and,with the help of Digital Elevation Model(DEM),the structural component is simulated by the lapse rate of different latitude and altitude zones in the QXP,the stochastic component is treated by the Kriging method.The results of combined method correctly represent spatial distribution of monthly mean air temperatures in the QXP.Its errors are far less than ordinary Kriging method that depends only on samples.Furthermore,the errors have been averaged,and they are evenly distributed in the study area.The comparing study of these interpolation methods on the monthly mean air temperature in the QXP indicates that the best spatial interpolation method does not exist,the best interpolation method only exist in given conditions.The best results of data interpolation can only be achieved by exploratory spatial data analysis and various experiments.The interpolation results showed that spatial distribution of monthly mean air temperatures in the QXP is controlled by altitudinal and latitudinal zonations,displaying a structure of high temperature in eastern and southern parts and low temperature in western and northern parts.The January mean air temperature in the QXP is lower than -13℃ and increases southeastward.The biggest center of low temperature is located in northwestern QXP north of 34°N and west of 93°E,with January mean air temperature lower than -16℃ and the lowest interpolated value of -25℃;another lower temperature area is in Qilian Mountains.Secondary level low temperature centers with smaller areal extent have been found in the regions around Wudaoliang,Tuotuohe and Maduo in the interior of the QXP,with January mean air temperature lower than -15℃.High temperature center of January is located in southeastern QXP,the high temperature ridge extends northward to Changdu and westward to Lhasa and Xigaza along the 29°N(Yaluzangbu River).The air temperature increases southeastward from -3℃ to 6℃.

使用反距离平方、趋势面、Kriging插值、Cokriging插值和综合方法对青藏高原1961-1990年30年平均1月气温进行空间插值比较研究,其中后两种方法能够把影响青藏高原气温分布的关键因素——高程置于插值算法之中。反距离平方和趋势面插值的结果都与实际情况相差较远;普通Kriging插值能够反映出青藏高原气温分布的一定的空间结构,但结果依然不好;由于考虑了高度变量,Cokriging插值表现出一定程度的性能改进,但因为台站海拔高度偏低,插值结果依然不理想。前4种方法效果不好的原因主要是因为青藏高原上气象台站稀少且高原西北地区无气象台站。综合方法把气温分解为结构化分量和随机分量,使用直减率把气温订正到同一海拔高度后,再对它们做Kriging插值分析,其结果较为正确地反映了青藏高原气温空间分布的特征,误差远小于其它方法。研究结果表明:样本本身的空间分布是影响插值精度的重要因素,合理的采样设计是必要的前提;对于台站稀少的地区,必须把随机插值方法和确定性方法结合起来估计气候变量的空间分布。同时给出了青藏高原1月的气温空间分布状况。

Spatial grid metrological data is an essential environmental factor for variousgeo model and climate model, and the interpolation software is a tool to makethe data space dependent. As a specially designed interpolation packagefor meteorological data, ANUSPLIN has advantages of its solid theory of thin plate spline function, high interpolation accuracy by the incorporation of parametric linear sub model, in addition to the independent spline variables. Furthermore,it is more suitable for time series of meteorological data by processing one more surface layer one time. So many contents of ANUSPLIN, such as the interpolation theory, main data flows, parameters setting, model selection, statistic analyses and input output formats are introduced through an example. It is expected that this paper is helpful for the researchers to use the ANUSPLIN more easily.

空间化的气候数据作为环境因子参数是区域气候模型和地学模型的基础，而插值软件是实现气候观测点数据空间化的工具。ANUSPLIN基于薄盘样条函数理论，引入多个影响因子作为协变量进行气象要素空间插值，大大提高插值精度，且能同时进行多个表面的空间插值，对时间序列的气象要素更加适合。

以获取三江源地区1971～2000年的各月降水及平均气温的栅格数据为目的,选取青海省及其周边共58个基准气象站点的气候数据,结合100 m×100 m分辨率的数字高程模型(DEM)数据,采用协同克里格(Co-Kriging,COK)和薄盘光滑样条函数(Thin plate smoothing splines, TPS)2种方法进行气温及降水的空间插值.用广义交叉验证(General cross validation, GCV)的最小统计误差筛选最佳模型,用均方根预测误差(RMSE)和平均绝对误差(MAE)比较2种方法的精度.结果表明,①不论是COK还是TPS,引入协变量后模型插值结果的精度更优;②TPS的插值效果显著优于COK:对月均温模型, TPS的RMSE值比COK提高了69.48%,MAE验证指标提高了70.56%;而对于月降水模型,TPS的RMSE值比COK提高了28.07%,MAE值提高了29.06%.

Annual average temperature data, annual rainfall and annual accumulated temperature data from 2114 meteorological stations in China and surrounding countries from 1961 to 1990 were interpolated by using inverse distance weighing (IDW), ordinary kriging (OK) and spline with ArcMap. Crossvalidation was applied to evaluate the three interpolation methods. The result indicated that the precision of interpolation results were varied significantly along with the number of selected meteorological stations for interpolating. For the three climate factors, ordinary Kriging shown the best precision. For annual average temperature and annual rainfall, spline shown better result than IDW, but for annual accumulated temperature, IDW was better than spline. The research indicated that the spatial distribution of moisture and temperature in China shown an obvious westeast, southnorth line as same with the HU population line. In the west of this line, the moisture and temperature conditions were poor and in the east of this line, the conditions were good.

基于地理信息系统软件ArcMap的统计分析模块，对我国及周边地区2 114个气象站点1961—1990年的年均温度、年降水量以及年积温等数据，分别使用反距离权重法、样条函数法和普通克里格法，选取不同的气象站点进行了空间插值，并利用交叉检验方法对插值精度进行了评估，结果表明：对于同一种插值方法，参与插值的气象站点数目不同，插值结果也不同。对3种气象要素的插值结果进行验证发现，普通克里格法均具有最好的插值精度。对年均温度和年降水量来说，样条函数法的插值精度优于反距离权重法，而对年积温，反距离权重法的插值精度优于样条函数法。通过对原始数据进行一定的处理，可以有效提高最终插值结果的精度。研究表明，我国水热的空间分布呈现明显的东西、南北分界，与胡焕庸线大致相一致，此线以东，水热条件充分，此线以西水热条件较差。

利用新疆1959～1998年的春季温度降水资料,分析了天山山区近40年来春季气候变化的基本特征,并与南疆、北疆进行了比较.所得的主要结果如下:(1)天山山区在春季温度的冷暖变化阶段上1981年前与北疆的相似,1981年后与南疆相似.(2)天山山区在春季降水量干湿变化阶段上与北疆的相似性强于南疆.(3)春季温度空间分布的同步变化性以北疆为最好,南疆最差,天山山区居中,而春季温度空间分布的反向变化性,以南疆为最大,北疆最小,天山山区居中.春季降水空间分布的同步变化性北疆较好,天山山区和南疆较差,而春季降水空间分布的反向变化性,以天山山区为最大,北疆最小,南疆居中.(4)三大区域的春季温度均表现为20世纪60与90年代偏高,70和80年代偏低.天山山区与北疆从60年代到90年代,春季降水均表现出了持续的增加的趋势,南疆春季降水除60年代外,不断增多.90年代是新疆三大区域春季降水最多的年代.⑸北疆和南疆近40年的春季最低温度存在着显著的增温趋势,增温率北疆大于南疆.

According to the rate of precipitation changing with altitude and the principle that a subzone must be contiguous, the 120 meteorological stations was divided into two types with six precipitation subzones in Xinjiang Region. There are three subzones below 2500 m a.s.l., of which precipitation varies linearly with the altitude. There are three subzones above 2500 m a.s.l., of which precipitation varies approximately as quadratic equation of altitude. When the variation of precipitation with altitude is linear, the maximum precipitation takes place in the mountaintop. Otherwise, the maximum precipitation takes place in the mountainside. What’s more, the maximum precipitation altitude changes with season and drought degree. The maximum precipitation altitude in summer is higher than that in winter; and the drier the climate, the higher the altitude. The maximum precipitation altitude and precipitation amount has significant difference among the six precipitation subzones.  The maximum precipitation altitude in Subzone 1 is 3190 m a.s.l., with the mean annual precipitation of 226.9 mm; the maximum precipitation altitude in Subzone 4 is 3332 m a.s.l., with the mean annual precipitation of 86.9 mm; the maximum precipitation altitude in Subzone 6 is 3840 m a.s.l., with the mean annual precipitation of 36.4 mm.

依据降水随高度变化率和分区连片的原则，将新疆102个气象站划为两种类型6个降水分区. 海拔小于2500m的3个区，降水随海拔变化近似呈线性；海拔大于2500m的3个区，降水随海拔变化呈近似二次曲线类型. 线性分布类型最大降水高度带在山顶，二次曲线类型最大降水高度带在“山腰”，它随季节和气候干旱程度发生变化：夏季高，冬季低；气候越干旱，最大降水高度越高，反之亦然. 最大降水高度的分布依气候的干湿程度和季节变化，各区的最大降水高度及降水量具有明显差异，1区最大降水高度带约为3190m，年平均降水量226.9mm；4区最大降水高度带约为海拔3332 m，年平均降水量为86.9mm，6区最大降水高度带约为海拔3840 m，年平均降水量为36.4mm. 降水量越少（干旱）的地区，最大降水高度越高.

The Xinjiang Uygur Autonomous Region is located in the hinterland of the Eurasian continent and away from the sea.Precipitation in the region is scarce and its temporal and spatial temporal distributions are uneven.The mean annual precipitation is 2588×10 8 m 3 ,which is equivalent to 157.4 mm water deep.The characteristics of geographical distribution of precipitation are as follows: precipitation in the north is more than that in the south;precipitation in the northwest is more than that in the southeast;precipitation in the windward slopes is more than that in the leeward slopes;precipitation in the mountain areas is more than that in the plains and basins.Zonal vertical distribution of precipitation is obvious.The changing range of annual precipitation in the north is less than that in the south.The coefficients of variation of annual precipitation at more than 90% of the stations are between 0.2 and 0.7.From May to August there is a high consecutive rainfall period.The intensity of precipitation is small in north but great in the south.The relative intensity at higher altitude is generally greater that that in lower altitude.The longest continuous dry season is 3~5 months in the south slopes of Altay Mountains,the mountains west of the Junggar Basin and the western Tianshan Mountains,5~7 months along the north slopes of Tianshan Mountains and 6~8 months in the south region,east region and the Junggar Basin.;The atmospheric precipitation,the river runoff and the surface water resources in Xinjiang Region are related well.For example,the correlation coefficient of precipitation and surface water resources is 0.8290.The flow coefficient of average precipitation is 0.34 in all Xinjiang Region,and the areas with abundant precipitation are larger than those with short precipitation.Sparse rainfall results in extreme vulnerability of the ecological environment in the region.The coverage rate of forest is only 1.5%,the total oasis area is less than 9% and the total desert area is 25.7% in the region.The precipitation factor is 0.25 and is the largest one among the weight factors of vulnerability assessment of ecological environment in Xinjiang Region.;Simulation results of global climate models show that Xinjiang Region will still be arid,semi-arid areas in the future.The water resources of atmospheric precipitation are very limited,so it is unrealistic to rely on rainfall to ease the shortage of water resources in the region.In order to economic and social sustainable development,the problem of water resources of Xinjiang Region must be dealt with by other means,such as rational allocation and utilization of water resources,using various types of water-saving techniques and enhancing the public’s awareness of water-saving etc.

新疆位于欧亚大陆腹地,空中水汽来源少,水资源匮乏,生态环境脆弱,降水有其独特的时空分布特征,且对水资源形成和生态环境有着十分重要的影响和作用.利用全疆106处水文、气象站1956-2005年的降水资料以及相关分析研究成果,从水资源的角度对新疆降水的形成条件、时空分布特征和对水资源、生态环境的影响进行了分析.结果表明:多年平均年降水总量为2588×10 8 m 3 ,折合降水深157.4 mm;90%以上的站点年降水量变差系数在0.2～0.7之间;连续最大4个月降水量在40%以上,平均降水产流系数为34%.新疆降水的稀少导致了其生态环境的极端脆弱,绿洲面积仅占全疆总面积的9%,而沙漠面积却达全疆的25.7%.未来新疆地区仍然是干旱、半干旱地区,大气降水资源有限,必须通过其它途径来解决新疆水资源短缺问题.

Precipitation plays a major role in maintaining the water supply in Mountainous area, which directly impacts the development of ecology environment and economic society in the mid and down streams of arid regions. Due to the technical and geographical limitations, one difficult problem in precipitation study is to accurately observe the meteorological factors. The entropy is used in this study to investigate the spatio-temporal variations of precipitation in annual, seasonal and monthly time series from 31 stations located in the Tianshan Mountain during 1960-2008. The results showed that there was an obvious spatial-temporal variability of precipitation in the Tianshan Mountain. The variability of precipitation of annual entropy was less than that of the seasonal, which is in turn less than the monthly. The decreasing trend of annual entropy was significant. In seasons, the variability of entropy was very significant in winter, but not in summer. The variability of entropy exhibited difference between precipitation and elevation for seasons. There was an increasing trend in the precipitation in northern slope with the elevation increase, while decreasing in southern slope of Tianshan Mountain in spring, summer and fall. The precipitation increased from April to September, and decrease from October to March of next year. The variability of monthly entropy and precipitation were antithetical. The spatial variability of entropy was more radical in southern and eastern slope than northern and western slope. The variability of entropy was minimal in Ili River valley. Obviously, the spatio-temporal variations of precipitation can be depicted by entropy, and supply a scientific basis to the rational development of water resources in Mountain area.

降水是山区气候中最重要的气象要素之一,其大小及变化直接关系到干旱山区中、下游的生态环境与经济社会的发展,但是,由于技术和地理条件的限制,是难以精确观测的气象要素。本研究采用天山山区31个台站和1960-2008年间的实测降水资料,应用信息熵方法对天山山区年、季和月的降水变化特征进行分析。结果表明,天山山区降水在时间和空间上均存在变异性,时间变异性表现为信息熵的无序指数(DI)年际变化小于季节变化,季节变化小于月变化。DI多年变化特征呈现下降趋势;冬季DI变化最为显著,夏季最小;冬季北坡降水随高程的增加DI变化呈显著增加趋势( P <0.05),春、夏和秋季南坡降水随高程的增加DI变化呈显著减少趋势( P <0.05)。山区月降水4-9月逐渐增加,10月至次年3月降水逐渐减少,月降水DI的变化与降水变化呈反相特征。在空间上DI变化与降水的分布相反,表现为南强北弱,东强西弱,伊犁河谷降水DI变化最小。结果证明,DI方法能够有效地分析山区降水的时空变化特征,为合理开发山区的水资源提供科学依据。

Based on seasonal precipitation data of 172 weather stations from 1961 to 2004 in Northeastern China, three circumstances including the same quantity of modeling stations but different quantity of testing stations, the same quantity of testing stations but different quantity of modeling stations, and different quantity of both modeling stations and testing stations, Inverse distance weighted(methodⅠ), three dimension-second order trend surface analysis(methodⅡ), Anusplin software(method Ⅲ) and spatial climatic value integrating with multi-annual deviation from normal interpolation methods(method Ⅳ) are used to interpolate rainy and rainless and annual precipitation data with different temporal scale ranging from multi-year and single-year to compare precision among them. The conclusions are as follows: firstly, the interpolation precision of method Ⅲ is the best among the other methods for both multi-year and single-year bases. At the same time, method Ⅲ is convenient to operate and able to run with a huge amount of data; secondly, to every interpolation method, spatial interpolation error of summer and annual precipitation is smaller than that of winter at different time scale. It is not evident for difference of interpolation precision between the summer season and the year. For different seasons, the interpolation precision is evidently higher in rainy period than in rainless period; thirdly, under circumstance of the same testing stations, the interpolation precision of precipitation data is not always elevated with increasing of number of modeling stations. Moreover, with increasing number of testing stations, the interpolation precision won’t be evidently affected on the circumstance with same number of modeling stations.

以1961年～2004年东北3省44年172个观测站的四季降水资料为数据源，采用反距离权重(IDW)(方法1)、趋势面模拟+残差内插(方法2)、ANUSPLIN软件插值(方法3)、空间化气候值+年际距平值(方法4)4种空间插值方法，以相同建模站不同检验站、相同检验站不同建模站以及不同检验站不同建模站3种形式分别对多年平均降水，单年丰水年和少水年的冬、夏、年降水数据进行空间插值精度比较，最终得出以下结论：无论是多年还是单年数据，方法３对降水数据空间插值相对误差都是最小的，是一种操作方便，易于批量运算的最优方法。对于每种插值方法，不同时间尺度夏季和年降水空间插值相对误差小于冬季，而夏季和年差异不大，不同季节降水空间插值相对误差丰水年明显小于少水年。在检验站相同情况下，建模站增加并不一定会使降水要素空间插值精度提高；由于空间分布的差异，在建模站相同情况下，检验站数目增加对空间插值的精度影响不大。