集合卡尔曼滤波数据同化方法改进土壤水分模拟效果
陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度。本研究构建集合卡尔曼滤波（En KF,ensemble Kalman filter）数据同化方法,将其集成到水文强化陆面过程模型HELP（hydrologically-enhanced land process）中,对模型中土壤水分及表面温度等状态变量进行优化。模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值。结果表明,数据同化后表层、根层、深层土壤水...
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| Published in | 农业工程学报 Vol. 32; no. 2; pp. 99 - 104 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国水利水电科学研究院,流域水循环模拟与调控国家重点实验室,北京 100048%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2016.02.015 |
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| Abstract | 陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度。本研究构建集合卡尔曼滤波（En KF,ensemble Kalman filter）数据同化方法,将其集成到水文强化陆面过程模型HELP（hydrologically-enhanced land process）中,对模型中土壤水分及表面温度等状态变量进行优化。模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值。结果表明,数据同化后表层、根层、深层土壤水分模拟结果相比基准值均有提高,土壤含水量均方根误差减小30%-50%,证明采用数据同化方法能够有效提高土壤水分的模拟结果。 |
|---|---|
| AbstractList | 陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度。本研究构建集合卡尔曼滤波（En KF,ensemble Kalman filter）数据同化方法,将其集成到水文强化陆面过程模型HELP（hydrologically-enhanced land process）中,对模型中土壤水分及表面温度等状态变量进行优化。模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值。结果表明,数据同化后表层、根层、深层土壤水分模拟结果相比基准值均有提高,土壤含水量均方根误差减小30%-50%,证明采用数据同化方法能够有效提高土壤水分的模拟结果。 S152.7; 陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累.数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度.本研究构建集合卡尔曼滤波(EnKF,ensemble Kalman filter)数据同化方法,将其集成到水文强化陆面过程模型HELP(hydrologically-enhanced land process)中,对模型中土壤水分及表面温度等状态变量进行优化.模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值.结果表明,数据同化后表层、根层、深层土壤水分模拟结果相比基准值均有提高,土壤含水量均方根误差减小30％～50％,证明采用数据同化方法能够有效提高土壤水分的模拟结果. |
| Author | 陈鹤 杨大文 刘钰 张宝忠 |
| AuthorAffiliation | 中国水利水电科学研究院,流域水循环模拟与调控国家重点实验室,北京100048 清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京100084 |
| AuthorAffiliation_xml | – name: 中国水利水电科学研究院,流域水循环模拟与调控国家重点实验室,北京 100048%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084 |
| Author_FL | Zhang Baozhong Liu Yu Chen He Yang Dawen |
| Author_FL_xml | – sequence: 1 fullname: Chen He – sequence: 2 fullname: Yang Dawen – sequence: 3 fullname: Liu Yu – sequence: 4 fullname: Zhang Baozhong |
| Author_xml | – sequence: 1 fullname: 陈鹤 杨大文 刘钰 张宝忠 |
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| DOI | 10.11975/j.issn.1002-6819.2016.02.015 |
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| DocumentTitleAlternate | Data assimilation technique based on ensemble Kalman filter for improving soil water content estimation |
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| Keywords | 温度 土壤含水量 land surface model 数据同化 temperature remote sensing soil water content 遥感 data assimilation soils 土壤 陆面过程模型 |
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| Notes | 11-2047/S Soil moisture in unsaturated zone connects the water and energy exchange process between surface water and groundwater, which has great influence to rainfall infiltration and surface evapotranspiration and hence has important meaning to agriculture, hydrology and meteorology. In agricultural study, accurate estimation of soil water content has significant importance to agricultural water management, irrigation regime determination and agricultural output increase. Soil water content can be quantified by surface observation, model estimation and remote sensing retrieval. Due to the large heterogeneity of soil property, surface observation at small scale can be hardly extended to large scale; while spatial distribution retrieved by remote sensing data can only obtain instantaneous value at satellite over-passing time. Land surface model is treated as a powerful tool in continuous estimation of soil water content, which is continuous in spatial and temporal dimension. However, the error tends to accumul |
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| Publisher | 中国水利水电科学研究院,流域水循环模拟与调控国家重点实验室,北京 100048%清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京 100084 |
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| Snippet | 陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模... S152.7; 陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累.数据同化技术可以考虑模型不确定性,实... |
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| SubjectTerms | 土壤 土壤含水量 数据同化 温度 遥感 陆面过程模型 |
| Title | 集合卡尔曼滤波数据同化方法改进土壤水分模拟效果 |
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