黄河三角洲土壤水分遥感数据同化方法研究
米素娟
学位类型硕士
导师唐家奎
2012-05-21
学位授予单位中国科学院研究生院
学位授予地点北京
学位专业环境科学
关键词黄河三角洲 土壤水分 数据同化 Beps
其他摘要土壤水分是地气相互作用、水文循环等研究的关键变量,对气候、区域环境产生重要影响,故受到大气科学、土壤学、农业工程、环境工程和地下水动力学等领域的高度重视。土壤水分的准确估计对于研究和理解地球表层生物物理过程起着重要作用。土壤水分可以通过野外实地观测、陆面水文模式模拟和遥感反演来估计。为克服三种方法的缺点和不足,利用不同来源、不同空间和时间分辨率的观测数据,并将它们有机组合,结合陆面过程模式发展同化算法以提高土壤水分的测量精度是发展方向。目前数据同化研究以顺序同化为主要的研究方法,顺序同化的缺点是只能更新有观测数据时刻的土壤水分值。模型在运行过程中,模型中的一些参数随时间和空间发生变化,而顺序同化并没有改变参数值,导致模型本身的模拟精度产生较大的误差。前人提出两阶段的数据同化,不仅优化有观测数据的时刻的土壤水分值,而且对模型的参数进行优化,使整个同化时期的土壤水分值得到优化。本文中,基于以上提出的模型模拟、顺序同化、两阶段同化进行研究,得到如下的结果:1)顺序同化和两阶段同化得到的土壤水分同化值与遥感反演结果有一定的差异,因此本文中,根据顺序同化、两阶段同化两种方法,并结合BEPS模型,提出二次同化和迭代同化两种新的同化方法。2)利用BEPS模型,并根据其要求的输入数据,完成黄河三角洲地区2009年第75天至139天之间的土壤水分模拟。利用SPSI算法,得到5个时期黄河三角洲地区土壤水分反演结果。并将BEPS算法和遥感反演结果分别输入到顺序同化、两阶段同化、二次同化和迭代同化四种方法中,得到四种方法在该时期的土壤水分数据同化结果值。3利用单点观察四种同化结果分别和遥感反演结果之间的差异,差异从小到大依次是迭代同化、二次同化、两阶段同化和顺序同化。并对四种方法的预测能力进行分析,迭代同化的预测能力最强,二次迭代次之,两阶段同化和顺序同化预测能力最差。4)利用一致性指数的平均值观察整个区域四种同化结果与遥感反演结果的一致性,一致性从小到大依次是顺序同化、两阶段同化、二次同化和迭代同化。5 利用VB 6.0软件和V C++建立遥感土壤水分数据同化系统。该系统中,包括BEPS模拟、顺序同化、两阶段同化、二次同化和迭代同化五个模块,实现基于BEPS模型的模拟和土壤水分同化的系统化。; Soil moisture is a key parameter of some research fields, such as hydrographic circulation and interactions between earth and atmosphere, which has important influences on atmosphere and regional environment. Accurate estimates of soil moisture plays an important role in research and understand biological physical process of the earth surface system. Soil moisture can be obtained in three ways: 1) it may be obtained by traditional filed surveys; 2) it may be derived by running a land surface model; 3) it may be retrieved from Remote Sensing data. Based on the advantages and disadvantages of three methods, observation data from multiple sources and variable spatial and temporal resolutions are combined and also with the help of land surface data assimilation system to promote the accuracy of soil moisture.Nowadays, the sequential data assimilation (SDA) is used as the main method of data assimilation. The disadvantages of the SDA are that soil moisture can be optimized only when observation data can be obtained. During the running process, parameters change temporally and spatially, the SDA doesn’t consider the changes and the errors between observations and simulations accumulated. Two Stage Data Assimilation model for soil moisture (TSDA), which not only optimizes soil moisture, but also optimizes the parameters of the process model by data assimilation.In this paper, based on the process model, SDA and TSDA, some researches are carried out as follows:1)    During the experiment in the area of the yellow river delta, there are some discrepancies between data assimilation values and remote sensing inversion values. So, new two data assimilation systems based on the SDA and TSDA system are presented ,which named as SMDAT—EnKF and Iterative DA, respectively.2)    With the help of the Boreal Ecosystem Productivity Simulator (BEPS) model, soil moisture simulate data in the yellow river delta from the 75th day to 139th day in 2010 were obtained. By using the SPSI method and MODIS images, five soil moisture images of this period were inverted. After orderly imputing the inverted result to the four data assimilation systems, data assimilation results were obtained.3)    The discrepancies between data assimilation results and remote sensing inversed results are compared using one point. The Iterative DA result has less discrepancy than the other three. The prediction capability analyses of the four methods are compared and the Iterative DA result has better ability than others.4)    The Agreement Index is used to observe the agreement between the data assimilation results and the remote sensing inversed results of the whole research area. After the experiment, it can be concluded that the Iterative DA has higher values than other methods.5)    A remote sense soil moisture data assimilation system was created with the help of the VB 6.0 software and the VC++ software. In this system, five submodules, which are simulation of the BEPS model, SDA, TSDA and SMDAT—EnKF, are concluded.
语种中文
文献类型学位论文
条目标识符http://ir.yic.ac.cn/handle/133337/5628
专题中国科学院烟台海岸带研究所知识产出_学位论文
推荐引用方式
GB/T 7714
米素娟. 黄河三角洲土壤水分遥感数据同化方法研究[D]. 北京. 中国科学院研究生院,2012.
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