不同滤波算法在土壤湿度同化中的应用

doi:10.7522/j.issn.1000-0240.2018.0063

冰川冻土 ›› 2018, Vol. 40 ›› Issue (3): 580-587.doi: 10.7522/j.issn.1000-0240.2018.0063

不同滤波算法在土壤湿度同化中的应用

付晓雷^1,2, 余钟波^3,4, 丁永建², 杨传国^3,4, 蒋晓蕾⁴, 鞠琴^3,4

1. 福州大学土木工程学院, 福建福州 350116;
2. 中国科学院西北生态环境资源研究院, 甘肃兰州 730000;
3. 河海大学水文水资源与水利工程科学国家重点实验室, 江苏南京 210098;
4. 河海大学水文水资源学院, 江苏南京 210098

收稿日期:2017-12-13 修回日期:2018-02-22 出版日期:2018-06-25 发布日期:2018-07-16
作者简介:付晓雷(1986-),男,山东临沂人,讲师,2015年在河海大学获博士学位,从事土壤湿度数据同化、水文预报研究.E-mail:fuxiaolei518@163.com.
基金资助:
国家重点研发计划“973计划”项目（2016YFC0402706；2016YFC0402710）；国家自然科学基金项目（51709046；41601562；41471016；51539003；41323001）；河海大学水文水资源与水利工程科学国家重点实验室开放基金面上项目（2015490311）资助

Application of different filters in soil moisture assimilation

FU Xiaolei^1,2, YU Zhongbo^3,4, DING Yongjian², YANG Chuanguo^3,4, JIANG Xiaolei⁴, JU Qin^3,4

1. College of Civil Engineering, Fuzhou University, Fuzhou 350116, China;
2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;
4. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Received:2017-12-13 Revised:2018-02-22 Online:2018-06-25 Published:2018-07-16

摘要/Abstract

摘要： 为研究不同滤波算法在土壤湿度同化中的有效性，以及土壤湿度模拟结果对模型参数的敏感性，结合简单生物圈模型SiB2，设置敏感性实验，探求土壤饱和水力传导度对土壤湿度模拟结果的影响；并在此基础上，采用集合卡尔曼滤波（EnKF）、无迹卡尔曼滤波（UKF）和无迹粒子滤波（UPF）开展土壤湿度实时同化实验。结果表明：土壤饱和水力传导度能显著影响土壤湿度模拟精度；利用EnKF、UKF、UPF同化站点观测数据，均能改善土壤湿度模拟结果；3种同化方法在不同土壤层的同化效果不同，在土壤表层，EnKF的有效性优于UKF和UPF，在根域层和土壤深层，3种滤波方法有效性在降雨前后相差较大。因此，针对性地选择同化方法，是提高土壤湿度模拟精度的有效手段。

关键词: 土壤湿度, 集合卡尔曼滤波, 无迹卡尔曼滤波, 无迹粒子滤波, 饱和水力传导度

Abstract: To study the performances of different filters in soil moisture assimilation and the model parameter sensitivity to soil moisture simulation, the assimilation and sensitivity experiments were developed. First, the sensitivity of soil saturated hydraulic conductivity (Ks) to soil moisture simulation results was analyzed based on simple biosphere model (SiB2). Then, the hourly in-situ observations were assimilated into SiB2 by ensemble Kalman filter (EnKF), unscented Kalman filter (UKF) and unscented particle filter (UPF) based on the sensitivity analysis results. The results show that:(1) soil saturated hydraulic conductivity can affect the soil moisture simulation accuracy significantly; (2) all the three filters can improve the soil moisture simulation results significantly; (3) the performances of the three filters are different at different soil layers:at soil surface layer, EnKF performs better than UKF and UPF, at root zone layer and deep soil layer, the performances of the three filters are different before and after precipitation. Thus, selecting a suitable data assimilation method is an efficient way to improve the soil moisture simulation accuracy.

Key words: soil moisture, ensemble Kalman filter, unscented Kalman filter, unscented particle filter, saturated hydraulic conductivity

中图分类号:

S152.7+1

付晓雷, 余钟波, 丁永建, 杨传国, 蒋晓蕾, 鞠琴. 不同滤波算法在土壤湿度同化中的应用[J]. 冰川冻土, 2018, 40(3): 580-587.

FU Xiaolei, YU Zhongbo, DING Yongjian, YANG Chuanguo, JIANG Xiaolei, JU Qin. Application of different filters in soil moisture assimilation[J]. JOURNAL OF GLACIOLOGY AND GEOCRYOLOGY, 2018, 40(3): 580-587.

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不同滤波算法在土壤湿度同化中的应用

Application of different filters in soil moisture assimilation

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