基于集合卡尔曼滤波和HYDRUS-1D模型的土壤剖面含水量同化试验  

EnKF and HYDRUS-1D based data assimilation experiments for improving soil moisture profile prediction

作　　者：王文[1] 刘永伟[1] 寇小华[1] 吕海深[1] 

机构地区：[1]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

出　　处：《水利学报》2012年第11期1302-1311,共10页Journal of Hydraulic Engineering

基　　金：国家自然科学基金项目(51190091;40930635)

摘　　要：本文建立了一个基于集合卡尔曼滤波（EnKF）方法和HYDRUS-1D模型相结合的单点土壤水分同化方案，以期通过同化表层土壤含水量的实测值来改进土壤剖面含水量预测精度。结果表明，该同化方案能有效改进表层（4～10cm）及浅层（50cm）的土壤含水量预测精度，对下层（100～150cm）的土壤含水量预测精度仍有影响，但其影响有好有坏，而对于深层（≥200cm）的土壤含水量预测精度几乎没有任何影响；对同化过程中的状态变量及关键状态参数的变化分析表明，同化效果的产生是由于通过数据同化修正了模型的状态变量及关键参数，而随着深度的增加，变量和参数的调整量减小，同化效果也就随之减弱。A one-dimensional soil moisture assimilation scheme at point scale based on the combination of the ensemble Kalman filter （EnKF） and HYDRUS-1D model is developed to improve the soil moisture proile prediction accuracy by assimilating the surface soil moisture. The results show that the assimilation scheme has a remarkable effect on the surface layer of 4-10cm and the shallow layer of 50cm. For the layers at depth 100-150cm, it still has certain influence, but the influence may be negative for the layer in the depth more than 200cm, which assimilation has no effect on the soil moisture prediction. The analysis based on changes of state variable and key parameters shows that the assimilation effect is caused by adjusting the state variable and key parameters in the model during assimilation process, and along with the increase of depth the amount of adjustment decrease, which leads to the weakening of assimilation effect.

关 键 词：集合卡尔曼滤波 HYDRUS-1D 数据同化 土壤剖面含水量 

分 类 号：S152.7[农业科学—土壤学]