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新疆天山山区TRMM降水数据的校正与降尺度研究
李慧
学位类型硕士
导师杨涛 ; 赵成义
2016
学位授予单位中国科学院大学
学位授予地点北京
学位专业自然地理学
关键词天山山区 Trmm3b43 Ndvi 校正 降尺度
摘要降水是全球水分和能量循环中关键的气候要素。降水量是水分循环、水资源评价、气候分析、水文模型等计算和研究中非常重要的输入参数。因此获得高分辨率、高精度的降水数据具有重要的意义。在我国西部地区,尤其是地势复杂,海拔较高的天山山区,水文气象观测站点稀少,站网密度远远不能满足流域水文模型的应用需求。目前,遥感技术可以提供在空间上连续分布的降水资料,其中TRMM遥感数据应用广泛,已成为研究降雨变化的一种重要手段。但其空间分辨率较低,数据精度相对不高,对遥感降水数据进行降尺度和校正,能够有效解决该问题。 本研究以天山山区作为研究对象,首先利用研究区内8个气象站点的实测降水数据对TRMM3B43降水数据在天山山区的适用性进行评估;然后基于不同方法(ANN和CART)对TRMM3B43数据进行校正,得到高精度的降水数据;最后根据TRMM3B43降水与植被NDVI、坡度、坡向、高程、经纬度的相关关系,通过CART算法建立回归模型进行降尺度,并二次校正后得到高分辨率、高精度的降水数据,以此分析天山山区降水空间分布的特征。主要结论如下: (1)TRMM3B43降水数据在天山山区有一定的适用性。在研究区尺度上,TRMM3B43和GPM月降水数据与站点实测降水数据的决定系数(R2)分别为0.76和0.87,存在显著的线性相关关系,说明TRMM3B43和GPM降水数据与站点实测降水在整体上有较好的一致性,可以反映出研究区大致的降水情况。在单个站点上,TRMM3B43和GPM月降水数据与站点实测降水数据相关性不高,且误差较大。其中,GPM降水数据的精度高于TRMM3B43。 (2)从ANN法和CART算法的校正结果可知:无论整体还是单个站点综合考虑地理因子和NDVI数据校正后的TRMM降水数据与实测值之间的R2的平均值都在0.7以上,δ和RMSE也都在可接受范围之内,说明ANN和CART两种方法对TRMM数据的校正效果都很好。其中,CART算法校正后的TRMM降水数据与实测值之间的拟合度更高,误差更小,比ANN法校正后的TRMM降水数据更接近实测值。由此可以判断CART算法的校正效果优于ANN法,且CART计算过程更简便,效率更高。 (3) 基于CART算法综合考虑NDVI、坡度、坡向、高程和经纬度建立回归模型对TRMM3B43降水数据进行降尺度的方法可行。通过降尺度和校正的方法,得到了天山山区分辨率为1km的年降水数据,并通过比例系数法分解得到月降水数据。经过实测站点的检验,降尺度过程提高了空间分辨率,但与实测值之间存在一定误差,因此使用加法修正法进行校正。校正后的降尺度多年平均降水数据与实测降水数据的决定系数(R2)为0.85,δ和RMSE分别为15.20%和53.72mm,相比未校正的R2提高了0.05,δ和RMSE分别减小了5.42%和10.58mm。由此可以说明,经过校正后的降尺度降水数据精度更高,且校正过程有效地减小了降尺度和站点实测降水数据之间的误差。因此,通过本文降尺度和校正的方法能够得到天山山区高分辨率、高精度的降水数据。 (4)利用校正后的降尺度降水数据,在月、季、年三个时间尺度上对天山山区的降水进行分析,从空间分布情况来看:天山西部一年四季均是降水最多的地区,东部地区相对而言降水较少。夏季降水量主要集中在天山中部,冬季天山西部地区的降水明显多于东部,北坡的降水量普遍略高于南坡。从时间变化情况来看:天山山区降水季节性明显,夏季降水量大,冬季降水量小。其中7月降水量最大,2月最少。
其他摘要Precipitation is a key climate variable of global water and energy circulation. And it also is an indispensable input parameter of water calculation, water resources assessment, climatic analysis, hydrological models research. Thus, there is great importance of high resolution and precision precipitation data. At present, in the Western China, the hydrometeorology observation stations are so spare that not satisfy the application requirements of watershed hydrological model, especially in the Tien mountainous with complicated topography and high altitude. Currently, the remote sensing technology could provide continuous distribution of precipitation data in space. And the TRMM data has already been widely used, which become an important method to study the precipitation variability. However, the low resolution and data accuracy problem of TRMM seriously restricted its wide application. While downscaling and correction technology to the remote sensing precipitation data can effectively solve the problem. This study is taking Tianshan mountainous as the research object. First, we validated the applicability of TRMM3B43 precipitation data with measured data of eight meteorological stations in Tianshan mountainous; Then, corrected TRMM3B43 data based on different methods (ANN and CART) to get the high-precision precipitation data; Finally, according to the relation between TRMM3B43 precipitation and vegetation NDVI, slope, aspect, elevation, latitude and longitude, established downscaling regression model by CART algorithm. Using high resolution and precision precipitation data by secondary correction to analyze the characteristics of the spatial distribution of precipitation in Tianshan mountainous. Main conclusions are listed as follows: (1) TRMM3B43 precipitation data has the basic applicability in Tianshan mountainous. In the study area, the determination coefficient (R2) between monthly precipitation data of TRMM3B43 and GPM and observed station were 0.76 and 0.87. There is a significant linear correlation relationship, illustrating that the precipitation data between TRMM3B43, GPM and observed station have good consistency on the whole. The precipitation data of TRMM3B43 and GPM can reflect the approximate precipitation in the study area. While, in a single station, the correlation between monthly precipitation data of TRMM 3B43, GPM and observed station is not high, and the error is large. (2) According to the results of ANN and CART correlation algorithm, we know that: Whether on the whole or single station, the average of R2 between TRMM and observed precipitation data is more than 0.7 after correlation by comprehensively considering geographical factor and NDVI data. δ and RMSE are within the acceptable range. Illustrating that it’s a well effect that adjust TRMM data by two methods of ANN and CART. The fitting degree between the precipitation data of TRMM after correlation by CART algorithm and the measured values is higher than ANN. Compare with ANN, the precipitation data of TRMM after correlation by CART algorithm is closer to the measured values. Which can determine the effect of correction of CART algorithm is better than the ANN method, and CART calculation process is more simple and efficient. (3) The method that based on CART algorithm to establish a regression model which considered NDVI, gradient, slope direction, vertical and longitude and latitude to downscale TRMM precipitation data is feasible. We can obtain annual precipitation data of resolution of 1 km of Tianshan mountainous through the downscaling and correction. Through the inspection of: Downscaling process improves the resolution of spatial, but with a certain error between the precipitation data of observed station. The monthly precipitation data is obtained by proportional coefficient method of decomposition. The determination coefficient (R2) between the downscaling of years of average precipitation data and measured precipitation data is 0.85, δ and RMSE were 15.20% and 53.72mm respectively. Compared with the unadjusted R2 increased 0.05, δ and RMSE reduced 5.42% and 10.58 mm respectively. Thus: the precision of downscaling precipitation data after correction is higher, and the method of correction reduced the error between downscaling and observed precipitation data effectively. Therefore, this research can get the precipitation data of high resolution and precision of Tianshan mountainous by the method of downscaling and correction. (4) Use the downscaling precipitation data after correction to analyze the precipitation in three time scales of month, season and year in Tianshan mountainous. The spatial distribution shows: The most precipitation in Tianshan mountainous all the year is located in western, and the precipitation decreased gradually from the west to the east in Tianshan mountainous. The precipitation mainly concentrated in the central Tianshan mountainous in summer. The precipitation in the western area is more than the east in winter, and the precipitation in northslope of Tianshan mountainous is slightly higher than southslope. From the change of the time view: The precipitation is large in summer and small in winter in Tianshan mountainous, which seasonal is apparent. Among them, July is the largest and February is the least.
学科领域自然地理学
语种中文
文献类型学位论文
条目标识符http://ir.xjlas.org/handle/365004/14758
专题研究系统_荒漠环境研究室
作者单位中科院新疆生态与地理研究所
推荐引用方式
GB/T 7714
李慧. 新疆天山山区TRMM降水数据的校正与降尺度研究[D]. 北京. 中国科学院大学,2016.
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