基于表观电导率和Hydrus模型同化的土壤盐分估算  

Estimation of soil salinity by assimilating apparent electrical conductivity data into HYDRUS model

作　　者：姚荣江[1,2] 杨劲松 郑复乐[1,3] 王相平 谢文萍 张新[1,2] 尚辉[4] Yao Rongjiang;Yang Jinsong;Zheng Fule;Wang Xiangping;Xie Wenping;Zhang Xing;Shang Hui(State Key Laboratory of Soil and Sustainable Agriculture / Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;Dongtai Institute of Tidal Flat Research, Nanjing Branch of Chinese Academy of Sciences, Dongtai 224200, China;University of Chinese Academy of Sciences, Beijing 100049, China;Jiangsu Province Coastal Development (Dongtai) Co., Ltd., Dongtai 224237, China)

机构地区：[1]土壤与农业可持续发展国家重点实验室/中国科学院南京土壤研究所,南京210008 [2]中国科学院南京分院东台滩涂研究院,东台224200 [3]中国科学院大学,北京100049 [4]江苏省沿海开发(东台)有限公司,东台224237

出　　处：《农业工程学报》2019年第13期91-101,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目（41571223、U1806215）;中国科学院南京土壤研究所“一三五”计划和领域前沿项目（ISSASIP1633）;国家重点研发计划项目（2016YFC0501300、2016YFD0200303）;江苏省重点研发计划（现代农业）子项目（BE2017337-3）

摘　　要：精细刻画农田土壤盐分运移过程对盐渍化精准治理具有重要意义。该文以磁感式大地电导率仪 EM38 测定的土壤表观电导率作为数据源,利用表观电导率与剖面土壤盐分之间的反演模型作为观测算子,将集合卡尔曼滤波(ensembleKalman filter,EnKF)同化方法应用于土壤水盐运移过程模型(HYDRUS-1D),进行滨海盐渍农田周年土壤盐分动态的模拟,并分析了同化过程的敏感性。结果表明:与单纯使用 HYDRUS 模型相比,EnKF 同化方法对模型观测算子的更新,有效提高剖面土壤盐分模拟精度,且 EnKF 同化值的精度优于 EnKF 同化模拟值,在同化过程中的调整量亦最大;敏感性分析结果显示土壤盐分同化过程对状态变量集合数大小不敏感,对观测数据误差和引入观测数据的深度较为敏感,观测数据误差水平越高、引入观测数据的深度越浅其误差越大。研究表明基于水盐运移模型和土壤表观电导率数据的 EnKF 同化方法能提高土壤盐分的模拟精度,为利用多源数据和机理模型进行较大尺度生态过程模拟预测提供了有效手段。Accurate and real-time information on soil salinity is required to understand the evolution of soil salinization, to develop appropriate management strategies, and to implement practices to improve the soil productivity and ecological restoration. Therefore, describing the accurate process of soil salt transport is of great significance for the precise management of salt-affected soils. Using the proximal soil sensor (electromagnetic induction, type EM38) and ensemble Kalman filter (EnKF) method, this study investigated the feasibility of soil salinity estimation by assimilating 1-D hydrological model (HYDRUS-1D) and apparent electrical conductivity data measured by EM38. Soil sampling and periodical EM38 survey at 11 dates was performed in the experimental site, located in a marine-terrestrial interlaced area in north Jiangsu Province. Soil physical and chemical properties, groundwater attributes and meteorological data were also collected as driving data of assimilation system during November 2015 and October 2016. The inversion model relating apparent electrical conductivity to soil salinity was adopted as observation operator, and EnKF method was applied to HYDRUS-1D model to simulate soil salinity on the profile. This study also examined the sensitivity of simulation accuracy to ensemble number, error level and number of soil salinity observation data during assimilation procedure. The main conclusions included: 1) EnKF assimilation method improved the simulation accuracy of soil salinity on 0-1 m profile. In comparison with simulated value after EnKF assimilation and HYDRUS-simulated value, the root mean square error of EnKF assimilation value decreased and the NSE of EnKF assimilation value increased. This indicated that EnKF assimilation value was more accurate than the simulated value after EnKF assimilation, whereas the simulated value after EnKF assimilation was better than HYDRUS-simulated value;2) The simulated value after EnKF assimilation was closer to the measured value than HYDRUS-simulated value

关 键 词：土壤 电导率 盐分 水盐运移模型 磁感式大地电导率仪 数据同化 集合卡尔曼滤波 

分 类 号：S156.4[农业科学—土壤学] S271[农业科学—农业基础科学]