Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter. Issue 9 (13th September 2018)

• Record Type: Journal Article
• Authors: Xia, Chuan‐An; Hu, Bill X.; Tong, Juxiu; Guadagnini, Alberto
• Is Part Of: Water resources research. Volume 54:Issue 9(2018)
• Page Start: 6259
• Page End: 6281
• Publication Date: 2018-09-13
• Subjects: variable density flow -- value of data -- iterative ensemble Kalman filter -- ensemble Kalman filter; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022369
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Parameter estimation in variable‐density groundwater flow systems is confronted with challenges of strong nonlinearity and heavy computational burden. Relying on a variant of the Henry problem, we evaluate the performance of a domain localization scheme of the iterative ensemble Kalman filter in the framework of data assimilation settings for variable‐density groundwater flows in a seawater intrusion scenario. The performance of the approach is compared against (a) the corresponding domain localization scheme of the ensemble Kalman filter in its standard formulation as well as (b) a covariance localization scheme of the latter. The equivalent freshwater head, h f, and salinity, S a, are set as the target state variables. The randomly heterogeneous field of equivalent freshwater hydraulic conductivity, K f, is considered as the system parameter field. Density‐independent and density‐driven flow settings are considered to evaluate the assimilation results using various methods and data. When only h f data are assimilated, all tested approaches perform generally well and a localization scheme embedded in the iterative ensemble Kalman filter appears to consistently outperform the domain localized version of the standard ensemble Kalman filter (EnKF) in a density‐driven scenario; Dirichlet boundary conditions tend to show a more pronounced negative effect on estimating K f for density‐independent than for density‐dependent flow conditions; h f data are more informative in a density‐dependent than in a density‐independent setting. The sole use of S a information does not yield satisfactory updates of h f for the covariance localization scheme of the standard EnKF, while the sole use of h f does. The domain localization scheme leads to difficulties in the attainment of global filter convergence when only S a data are used. A covariance localization scheme associated with a standard EnKF can significantly alleviate this issue. Key Points: We study localized forms of iterative ensemble Kalman filter for data assimilation in a density‐driven flow Equivalent freshwater head has higher data worth than salinity for estimating both equivalent freshwater head and salinity A local analysis scheme is not necessarily associated with a guaranteed filter convergence when only salinity data are used