| 基于集合卡尔曼平滑算法的土壤水分同化 |
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| 引用本文： | 褚楠,黄春林,杜培军.基于集合卡尔曼平滑算法的土壤水分同化[J].水科学进展,2015,26(2):243-249. |
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| 作者姓名： | 褚楠 黄春林 杜培军 |
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| 作者单位： | 1.中国矿业大学环境与测绘学院, 江苏徐州 221116; |
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| 基金项目： | 国家自然科学基金资助项目(91325106);中国科学院“百人计划”资助项目(29Y127D01)~~ |
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| 摘 要： | 为研究观测资料稀少情况下土壤质地及有机质对土壤水分同化的影响,发展了集合卡尔曼平滑(Ensemble Kalman Smooth, EnKS)的土壤水分同化方案。利用黑河上游阿柔冻融观测站2008年6月1日至10月29日的观测数据,使用EnKS算法将表层土壤水分观测数据同化到简单生物圈模型(Simple Biosphere Model 2, SiB2)中,分析不同方案对土壤水分估计的影响,并与集合卡尔曼滤波算法(EnKF)的结果进行比较。研究结果表明,土壤质地和有机质对表层土壤水分模拟结果影响最大而对深层的影响相对较小;利用EnKF和EnKS算法同化表层土壤水分观测数据,均能够显著提高表层和根区土壤水分估计的精度,EnKS算法的精度略高于EnKF且所受土壤质地和有机质的影响小于EnKF;当观测数据稀少时,EnKS算法仍然可以得到较高精度的土壤水分估计。
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| 关 键 词： | 土壤水分 数据同化 集合卡尔曼滤波 集合卡尔曼平滑 土壤质地 土壤有机质 |
| 收稿时间： | 2014-10-15 |
Ensemble Kalman smoother for soil moisture data assimilation |
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| CHU Nan;HUANG Chunlin;DU Peijun.Ensemble Kalman smoother for soil moisture data assimilation[J].Advances in Water Science,2015,26(2):243-249. |
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| Authors: | CHU Nan;HUANG Chunlin;DU Peijun |
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| Affiliation: | 1.School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;2.Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China |
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| Abstract: | To study the influence of soil texture and organic matter on soil moisture assimilation when observation data are rare, we develop a soil moisture assimilation scheme based on ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS). Surface soil moisture observations obtained from Arou station in the upper reaches of Heihe river basin in 2008 are assimilated into simple biosphere model 2 (SiB2) by using EnKF and EnKS respectively to analyze the influences of soil texture and organic matter and observation frequency on estimation. Results showed that influences of soil texture and organic matter on soil moisture estimation are large at surface layer but little at deep layer. Both EnKF and EnKS can significantly improve the accuracy of soil moisture estimation at surface and root zone by assimilating surface soil moisture observations, and EnKS is little better than EnKF. High accuracy of soil moisture estimation can be obtained by EnKS when observation data are scarce. |
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| Keywords: | soil moisture data assimilation ensemble Kalman filter ensemble Kalman smoother soil texture organic matter |
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