Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

查看全文 作　　者：CHU [1,2]Nan;HUANG [2,3]ChunLin;LI [2,4]Xin;DU [1]PeiJun

机构地区：[1]School of Environment Science and Spatial Informatics, China University of Mining and Technology;[2]Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences;[3]Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences;[4]CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences

出　　处：《Science China Earth Sciences》索引2015年第58卷第12期,共13页

基　　金：supported by the Natural National Science Foundation of China(Grant Nos.91325106&41271358);the Hundred Talent Program of the Chinese Academy of Sciences(Grant No.29Y127D01);the Cross-disciplinary Collaborative Teams Program for Science;Technology and Innovation of the Chinese Academy of Sciences 摘　　要：In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother(DEn KS) and simple biosphere model(Si B2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter(En KF), ensemble Kalman smoother(En KS), and dual En KF(DEn KF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as En KF and En KS. The estimation accuracy of the model parameters decreased with increasing error sources. DEn KS outperformed DEn KF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEn KS approach is a useful and practical way to improve soil moisture estimation. 关键词：卡尔曼平滑土壤水分土壤性质集合参数估计方法平滑器卡尔曼滤波模型参数

相关文献

参考文献（44）
引证文献（1）
耦合文献（54）

参考文献(44)

引证文献(1)

耦合文献(54)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

参考文献(44)

引证文献(1)

耦合文献(54)

用户提示