Knowledge Resource Center for Ecological Environment in Arid Area
| 祁连山区降水空间分布研究 | |
| 其他题名 | Study on Spatial Distribution of Precipitation in Qilian Mountain Area |
| 董慧慧 | |
| 出版年 | 2016 |
| 学位类型 | 硕士 |
| 导师 | 肖洪浪 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 祁连山位于我国青藏高原东北边缘，由于地形抬升及季风环流影响，山区降水要高于周边地区。疏勒河、石羊河、黑河等河流均发源于此，祁连山区降水是抚育河西走廊及柴达木盆地绿洲的主要水资源。研究山区的降水分布特征，获取高精度的降水空间分布数据仍是该领域研究的重点方向之一。受山区地形等自然因素的限制，气象观测台站往往分布稀疏且不均匀，利用常规插值方法很难获得高精度的降水数据，尤其山区的地理地形特征对降水量的分布有重要的影响作用。如何利用有限的站点资料获取更高精度的山区降水分布数据是许多学者仍在关注的重点方向。同时，近些年来，卫星降水产品以其时间分辨率高、覆盖范围广、不受地形限制等优点得到广泛的应用，也成为获取降水资料的重要途径。本文利用祁连山区2010-2012年的气象站点及水文站点月降水资料，将地理及地形因素对降水的影响加以考虑，建立年降水量与地理地形因子间的多元线性回归模型，并选择偏最小二乘回归法、逐步回归法、主成分分析法对模型进行解算，同时与反距离加权插值、径向基函数插值、普通克里金插值、考虑了地形因素的协同克里金插值等插值方法进行精度对比。然后，基于精度最好的插值方法获取祁连山区的年、季、月尺度上的降水产品，并将年降水产品与常用的几种卫星产品 TRMM (Tropical Rainfall Measured Mission)3B43、CMORPH(CPC Morphing Technique)、中国区域高时空分辨率地面气象要素驱动数据集中的降水产品ITPCAS（Institute of Tibetan Plateau Research Chinese Academy of Science）在祁连山区的降水模拟精度进行对比，研究结果表明：（1）利用交叉验证法对7种插值方法的精度进行评估，结果表明多元线性统计回归模型的插值结果较传统插值方法好，其中，具有能够消除影响因子间的多重相关性、利用主成分回归原理、可以定性分析影响因子的重要性等优点的偏最小二乘法插值精度最好。而考虑了地形影响的协同克里金插值结果要比普通克里金插值精度高，说明了地形因素对降水具有重要的影响。（2）基于偏最小二乘（PLS）回归模型的降水量插值结果显示，祁连山年降水量分布从西北向东南逐渐递增，最大降水量在700mm以上，出现在山区东南部。主体降水量为300-600mm之间，主要分布在山区中东部地区，占据祁连山区2/3的区域。季降水量中，夏季降水量最为丰富，冬季降水量稀少，春夏秋季降水量均呈现于年降水大体一致的分布趋势从西向东逐渐增加，而冬季降水量的趋势表现为从南向北逐渐增加。（3）利用PLS模型标准化回归系数分析表明，祁连山区年降水及春、夏、秋季降水主要受经度（季风）及海拔的影响，其中降水量最为丰富的夏季降水量受海拔影响最为显著。由于冬季降水较少，各因子对冬季降水量的影响都并不很大。坡度坡向对降水量的整体影响不显著，但也是不可忽视的因素，是PLS模型精度较其他插值方法高的保证。（4）考虑到山区降水资料分布不均、影响因子的选择等因素都会对模型插值结果产生一定的影响，无法保证插值的降水分布产品一定是精度最好的。本文同时选择了近些年在山区应用较为广泛的三种卫星降水资料进行处理，获取祁连山区降水分布数据。并将四种数据产品进行分析和评估，为后续祁连山区的水资源研究及山区降水产品的选择提供参考。结果表明，中国科学院青藏高原研究所开发的中国区域高时空分辨率气象要素集中的ITPCAS降水产品在祁连山区的精度最好，其次为PLS插值产品和TRMM产品，CMORPH产品的精度最差。 |
| 英文摘要 | Qilian Mountains is located in the northeastern margin of the Tibetan Plateau, which have higher precipitation than the surrounding areas because of the influence of the terrain uplift and the monsoon circulation. Shule River, Shiyang River, Heihe River originate from here. The precipitation of Qilian Mountain is also the main water resources that fostering the Gansu Corridor and oasis in the Qaidam Basin. It is still one of the important research directions in the field of studying the distribution of precipitation in the mountain area and obtaining higher spatial accuracy. Restricted by the mountainous terrain and other natural factors, meteorological observation stations are sparse and uneven. That make it’s difficult to obtain high accuracy of precipitation data using conventional interpolation methods. Especially, geography factors play an important role in the precipitation distribution. How to use the limited observation data to obtain the more accurate data of precipitation in mountainous areas is still the focus for many scholars. At the same time, in recent years, satellite precipitation products get a wide range of applications because of their high time resolution, wide coverage and not limited by the topography. In this paper, the Qilian Mountains 2010-2012 meteorological stations and hydrological stations data, geographic and topographic factors are taken into consideration to establish a multiple linear regression model between precipitation and the geographical and topographical factors. And partial least squares regression, stepwise regression, principal component analysis method are used for model calculation, and the conventional interpolation method such as the inverse distance weighted interpolation, radial basis function interpolation, ordinary Kriging interpolation, CO Kriging which considering the topographic factors are also used to compare their accuracy. The results show that the accuracy of the PLS interpolation products have the best accuracy .Then let PLS products compared with the TRMM, CMORPH, ITPCAS in the Qilian mountain area to get the best precipitation product. The results show that t:(1) Via the cross validation method to assess these seven kinds of interpolation methods and find that, multivariate linear statistical regression model interpolation have better accuracy than traditional interpolation method. The PLS method have the best interpolation precision with its advantages that can eliminate the multiple correlation between influence factors, using principal component regression principle, and also can qualitatively analyses the importance of geography factors for precipitation. The CO-kriging interpolation method that taking the effect of the terrain into account is more accurate than the ordinary Kriging interpolation. It shows that the geography factors have an important influence on precipitation.(2) The precipitation interpolation results based on the PLS regression model show that the precipitation distribution of Qilian Mountains gradually increase from the northwest to the southeast with the maximum precipitation above the 700mm in the southeast of the mountain. Main precipitation is 300-600mm in the eastern part of the mountain area, occupying the area of 2/3 in Qilian mountain area. For seasonal precipitation, the most abundant precipitation is in summer, however, scarce in winter. The spring/ summer and autumn precipitation showed a broadly consistent trend with annual precipitation which the distribution of precipitation have a gradually increased from west to east, while the winter precipitation trend for increased gradually from the south to the north. (3) The results derived from the standard regression coefficient of PLS model showed that, the annual precipitation and spring, summer and autumn precipitation in Qilian mountain area are mainly affected by the longitude and altitude. Slope and aspect to the overall impact of the precipitation is not significant, but they are also important factor that cannot be ignored, which are the assurance of the PLS model have higher accuracy than other interpolation methods .Due to the winter precipitation is little, the impact of geography factors on winter rainfall is not very notable. (4) Considering the uneven distribution of precipitation data, the choice of factors and other factors will have certain impacts on the results of the interpolation results. We do not assure how accuracy our product is. Consequently, three kinds of satellite precipitation products widely used in the mountainous area in recent years are selected to compare with our results in this paper. These four kinds of data products are analyzed and evaluated in order to provide reference for the study of water resources in Qilian mountain area and the selection of precipitation products in mountainous areas. The results show that the ITPCAS product of the Chinese Academy of Sciences have the highest precision, followed by PLS interpolation products and TRMM products. |
| 中文关键词 | 祁连山 ; 地理地形因子 ; 偏最小二乘回归模型 ; 卫星降水产品 |
| 英文关键词 | Qilian Mountains geography factors partial least squares regression satellite precipitation product |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287720 |
| 推荐引用方式 GB/T 7714 | 董慧慧. 祁连山区降水空间分布研究[D]. 中国科学院大学,2016. |
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