Preprints
https://doi.org/10.5194/hess-2021-295
https://doi.org/10.5194/hess-2021-295

  28 Jun 2021

28 Jun 2021

Review status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Covariance-based selection of parameters for particle filter data assimilation in soil hydrology

Alaa Jamal1 and Raphael Linker2 Alaa Jamal and Raphael Linker
  • 1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign; 205 N Mathews Ave, Urbana, Illinois
  • 2Faculty of Civil and Environmental Engineering, Technion- Israel Institute of Technology; Derech Ya'akov Dori, Haifa, Israel

Abstract. Real-time in-situ measurements are increasingly used to improve the estimations of simulation models via data assimilation techniques such as particle filter. However, models that describe complex processes such as water flow contain a large number of parameters while the data available is typically very limited. In such situations, applying particle filter to a large, fixed set of parameters chosen a priori can lead to unstable behavior, i.e. inconsistent adjustment of some of the parameters that have only limited impact on the states that are being measured. To prevent this, in this study correlation-based variable selection is embedded in the particle filter, so that at each data assimilation step only a subset of the parameters is adjusted. More specifically, whenever measurements become available, the most influential (i.e., the most highly correlated) parameters are determined by correlation analysis, and only these are updated by particle filter. The proposed method was applied to a water flow model (Hydrus-1D) in which states (soil water contents) and parameters (soil hydraulic parameters) were updated via data assimilation. Two simulation case studies were conducted in order to demonstrate the performance of the proposed method. Overall, the proposed method yielded parameters and states estimates that were more accurate and more consistent than those obtained when adjusting all the parameters.

Alaa Jamal and Raphael Linker

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-295', Anonymous Referee #1, 30 Jun 2021
    • AC1: 'Reply on RC1', Alaa Jamal, 14 Sep 2021
  • RC2: 'Comment on hess-2021-295', Anonymous Referee #2, 28 Jul 2021
    • AC2: 'Reply on RC2', Alaa Jamal, 14 Sep 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-295', Anonymous Referee #1, 30 Jun 2021
    • AC1: 'Reply on RC1', Alaa Jamal, 14 Sep 2021
  • RC2: 'Comment on hess-2021-295', Anonymous Referee #2, 28 Jul 2021
    • AC2: 'Reply on RC2', Alaa Jamal, 14 Sep 2021

Alaa Jamal and Raphael Linker

Alaa Jamal and Raphael Linker

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Short summary
Data assimilation uses field measurements to improve field state estimation and parameters of simulation models. However, in dynamic problems, the influence of parameters on the field state estimation that corresponds to the field measurements changes over time. Therefore, when the influence of the parameters is low, the estimations of these parameters might be inaccurate. In this study, a dynamic high-influence parameter is presented in order to improve the data assimilation estimations.