DOI: 10.5194/hess-20-555-2016
Scopus记录号: 2-s2.0-84957597335
论文题名: Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter
作者: Erdal D ; , Cirpka O ; A
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 1 起始页码: 555
结束页码: 569
语种: 英语
Scopus关键词: Bandpass filters
; Groundwater
; Groundwater flow
; Kalman filters
; Parameter estimation
; Ensemble Kalman Filter
; Ground water recharge
; Inversion methods
; Joint estimation
; Predictive power
; Prior knowledge
; Regional groundwater flow
; Structural assumption
; Recharging (underground waters)
; groundwater
; groundwater flow
; hydraulic conductivity
; Kalman filter
; measurement method
; recharge
; research work
英文摘要: Regional groundwater flow strongly depends on groundwater recharge and hydraulic conductivity. Both are spatially variable fields, and their estimation is an ongoing topic in groundwater research and practice. In this study, we use the ensemble Kalman filter as an inversion method to jointly estimate spatially variable recharge and conductivity fields from head observations. The success of the approach strongly depends on the assumed prior knowledge. If the structural assumptions underlying the initial ensemble of the parameter fields are correct, both estimated fields resemble the true ones. However, erroneous prior knowledge may not be corrected by the head data. In the worst case, the estimated recharge field resembles the true conductivity field, resulting in a model that meets the observations but has very poor predictive power. The study exemplifies the importance of prior knowledge in the joint estimation of parameters from ambiguous measurements. © Author(s) 2016. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78923
Appears in Collections: 气候变化事实与影响
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作者单位: Center for Applied Geoscience, University of Tübingen, Tübingen, Germany
Recommended Citation:
Erdal D,, Cirpka O,A. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter[J]. Hydrology and Earth System Sciences,2016-01-01,20(1)