A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation

Zhang, Qiuru; Shi, Liangsheng; Holzman, Mauro; Ye, Ming; Wang, Yakun; Carmona, Facundo

doi:10.1016/j.advwatres.2019.103407

Cited by 36 publications

(29 citation statements)

References 65 publications

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“…Therefore, they are good candidates to statistically characterize the model structural error. In this study, we further integrate the hybrid data assimilation approach (Zhang et al, 2019) into our previous sequential data-worth analysis framework (Wang et al, 2018). Once the potential predictions are obtained, data-worth analysis is implemented to quantify the worth of alternative monitoring strategy (in terms of observation location, frequency, data type, etc.).…”

Section: Core Ideasmentioning

confidence: 99%

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Wang

Shi

Lin

et al. 2020

Vadose Zone Journal

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As the collection of soil moisture data is often costly, it is essential to implement dataworth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-toobtain meteorological data into GP training yielded better data-worth assessment.

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Section: Core Ideasmentioning

confidence: 99%

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Wang

Shi

Lin

et al. 2020

Vadose Zone Journal

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“…Using multiple summary metrics that measure relevant parts of system behavior, we can gain information about how and where the model may be improved (Sadegh et al, 2015;Vrugt & Sadegh, 2013). In future works, we can simultaneously consider the surrogate and the model structural errors by combining the approach proposed in this work with methods that address model structural inadequacy (e.g., Duan et al, 2007;Madadgar & Moradkhani, 2014;Xu et al, 2017;Xu & Valocchi, 2015;Ye et al, 2010;Zeng et al, 2016;Zeng et al, 2018;Zhang et al, 2019).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error

Zhang

Zheng

Chen

et al. 2020

Water Resources Research

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Bayesian inverse modeling is important for a better understanding of hydrological processes.However, this approach can be computationally demanding, as it usually requires a large number of model evaluations. To address this issue, one can take advantage of surrogate modeling techniques. Nevertheless, when approximation error of the surrogate model is neglected, the inversion result will be biased. In this paper, we develop a surrogate-based Bayesian inversion framework that explicitly quantifies and gradually reduces the approximation error of the surrogate. Specifically, two strategies are proposed to quantify the surrogate error. The first strategy works by quantifying the surrogate prediction uncertainty with a Bayesian method, while the second strategy uses another surrogate to simulate and correct the approximation error of the primary surrogate. By adaptively refining the surrogate over the posterior distribution, we can gradually reduce the surrogate approximation error to a small level. Demonstrated with three case studies involving high dimensionality, multimodality, and a real-world application, it is found that both strategies can reduce the bias introduced by surrogate approximation error, while the second strategy that integrates two methods (i.e., polynomial chaos expansion and Gaussian process in this work) that complement each other shows the best performance.

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“…In many cases, however, only one-dimensional heterogeneity can be considered in parameter estimation based on information from local measurement profiles. For example, Zha, Zhu, Zhang, Mao, and Shi (2019) directly estimated a one-dimensional parameter field, and Brandhorst, Erdal, and Neuweiler (2017), Erdal, Neuweiler, and Wollschläger (2014), and Zhang et al (2019) estimated an additional bias to the state to consider model structural error, which can also stem from unresolved small-scale heterogeneity.…”

Section: Core Ideasmentioning

confidence: 99%

Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements

Bauser

Riedel

Berg

et al. 2020

Vadose Zone Journal

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The description of soil water movement at the field scale requires soil hydraulic material properties. These cannot be measured directly and exhibit an intrinsic multiscale heterogeneity. The estimation of soil hydraulic properties through inverse modeling or data assimilation requires measurements of the state of the system. However, these measurements are typically scarce and do not resolve soil heterogeneity leading to effective material properties with limited predictive capabilities. In a synthetic study at the scale of soil profiles, we explore the possibility to estimate an effective reduced one-dimensional representation based on only four local water content measurements in a vertical profile located in one-and two-dimensional heterogeneous media. We allow the effective material properties to deviate locally to consider the impact of the small-scale heterogeneity on the water content measurements. In the synthetic experiments, the estimated reduced one-dimensional representations predict soil water content and fluxes sufficiently well but can fail to predict the accurate timing and magnitude of infiltration fronts. Cumulative fluxes below the water content measurements were predicted correctly even on short time scales, if evaporation was estimated accurately. However, this proved to be challenging if the local evaporation fluxes above the measurement profile deviated strongly from the average evaporation fluxes of the heterogeneous medium. The one-dimensional estimation leads to soil hydraulic parameters describing effective material properties that differ from the true material properties to compensate the missing representation of heterogeneity and two-dimensional flow.

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A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation

Cited by 36 publications

References 65 publications

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error

Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements

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