Comparison between gradient based UCODE_2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling

被引:0
作者
Tong Juxiu [1 ,2 ]
Hu, Bill X. [1 ,2 ,3 ]
Yang JinZhong [4 ]
机构
[1] China Univ Geosci, Key Lab Groundwater Cycle & Environm Evolut, Minist Educ, Beijing 100083, Peoples R China
[2] China Univ Geosci, Sch Water Resources & Environm, Beijing 100083, Peoples R China
[3] Florida State Univ, Dept Earth Ocean & Atmospher Sci Geol Sci, 108 Carraway Bldg,909 Antarctic Way, Tallahassee, FL 32306 USA
[4] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
基金
中国国家自然科学基金;
关键词
Inverse methods; UCODE_2005; Ensemble Kalman Filter; Heterogeneous hydraulic conductivity; Filter divergence; SEQUENTIAL SELF-CALIBRATION; DATA ASSIMILATION; SOLUTE TRANSPORT; SCHEME;
D O I
10.1007/s11430-015-0235-1
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter (EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter (EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.
引用
收藏
页码:899 / 909
页数:11
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