集合卡尔曼滤波数据同化方法改进土壤水分模拟效果

被引：13

作者：

陈鹤 ^{[1
]}

杨大文 ^{[2
]}

刘钰 ^{[1
]}

张宝忠 ^{[1
]}

机构：

[1] 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

[2] 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

来源：

农业工程学报 | 2016年 / 32卷 / 02期

关键词：

土壤; 遥感; 温度; 数据同化; 陆面过程模型; 土壤含水量;

D O I：

暂无

中图分类号：

S152.7 [土壤水分];

学科分类号：

摘要：

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。

引用

页码：99 / 104

页数：6

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