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.
Highlights
Real-time in-situ measurements are increasingly used to improve the estimations of simulation models via data assimilation techniques such as particle filter
Whenever measurements become available, the most influential 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 and parameters were updated via data assimilation
Summary
Accurate and proper estimation of prognostic variables (e.g. soil moisture) has been receiving increasing attention in the past years. The mathematical models that describe such complex processes (e.g. Hydrus (Simunek et al, 1998)) contain a large number of parameters. Calibrating such models, which are non-linear, is far from trivial, especially since in real settings the data available for the task is limited. Real-time in-situ measurements are increasingly used to improve the 25 estimations of such simulation models via data assimilation techniques (Das & Mohanty, 2006; Das et al, 2008; Brandhorst et al, 2017; Abbaszadeh et al, 2018; Bauser et al, 2018; Berg et al, 2019; Jamal and Linker, 2019). One of the most widely used data assimilation (DA) methods is the ensemble Kalman filter (EnKF) (Reichle et al, 2002; De Lannoy et al, 2007; Jamal and Linker, 2019).
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