Impacts of different types of measurements on estimating unsaturated flow parameters

Shi, Liangsheng; Song, Xianfang; Tong, Jinnan; Zhu, Yan; Zhang, Qiuru

doi:10.1016/j.jhydrol.2015.01.078

Cited by 39 publications

(30 citation statements)

References 63 publications

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“…It demonstrates that the goodness of fitting of observed data is sensitive to α, n and K 0 , but insensitive to the parameters θ r and θ s . And the parameters θ r and θ s have higher uncertainties than those of α, n and K 0 , which is consistent with Shi et al (2015) and Younes et al (2013).…”

Section: Resultssupporting

confidence: 76%

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Yan¹,

Liu²,

Zhang³

et al. 2017

International Agrophysics

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A b s t r a c t. Nonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the 'optimal' soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the 'optimal' solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

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Section: Resultssupporting

confidence: 76%

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Yan¹,

Liu²,

Zhang³

et al. 2017

International Agrophysics

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show abstract

“…For comparisons, three conventional sampling strategies (C1, C2, and C3) are used. The sampling location is selected from the middle of each soil layer, respectively, which is generally representative for previous studies [ Shi et al ., ; Man et al ., ]. For fair comparison, the ensemble size is set as 200 for all the cases.…”

Section: Case Studiesmentioning

confidence: 99%

Sequential ensemble-based optimal design for parameter estimation

Man

Zhang

et al. 2016

Water Resour. Res.

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The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well‐designed sampling (data‐collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble‐based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first‐order and second‐order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one‐dimensional and two‐dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

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“…As opposed to the extended Kalman filter (EKF), EnKF does not require linearization and is suitable for nonlinear systems. It has been widely used in groundwater flow and solute transport scenarios (e.g., among others Chen & Zhang, 2006;Crestani et al, 2015;Gharamti et al, 2015;Huang et al, 2009;Liu et al, 2008;Shi et al, 2015;Tong et al, 2010Tong et al, , 2013Zovi et al, 2017).…”

Section: Iterative Ensemble Kalman Filtermentioning

confidence: 99%

Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter

Xia

Tong

et al. 2018

Water Resources Research

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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, hf, and salinity, Sa, are set as the target state variables. The randomly heterogeneous field of equivalent freshwater hydraulic conductivity, Kf, 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 hf 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 Kf for density‐independent than for density‐dependent flow conditions; hf data are more informative in a density‐dependent than in a density‐independent setting. The sole use of Sa information does not yield satisfactory updates of hf for the covariance localization scheme of the standard EnKF, while the sole use of hf does. The domain localization scheme leads to difficulties in the attainment of global filter convergence when only Sa data are used. A covariance localization scheme associated with a standard EnKF can significantly alleviate this issue.

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Impacts of different types of measurements on estimating unsaturated flow parameters

Cited by 39 publications

References 63 publications

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

Sequential ensemble-based optimal design for parameter estimation

Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter

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