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Kalman filtering in groundwater flow modelling: problems and prospects

Abstract

The popularity of applying filtering theory in the environmental and hydrological sciences passed its first climax in the 1970s. Like so many other new mathematical methods it was simply the fashion at the time. The study of groundwater systems was not immune to this fashion, but neither was it by any means a prominent area of application. The spatial-temporal characteristics of groundwater flow are customarily described by analytical or, more frequently, numerical, physics-based models. Consequently, the state-space representations associated with filtering must be of a high order, with an immediately apparent computational over-burden. And therein lies part of the reason for the but modest interest there has been in applying Kalman filtering to groundwater systems, as reviewed critically in this paper. Filtering theory may be used to address a variety of problems, such as: state estimation and reconstruction, parameter estimation (including the study of uncertainty and its propagation), combined state-parameter estimation, input estimation, estimation of the variance-covariance properties of stochastic disturbances, the design of observation networks, and the analysis of parameter identifiability. A large proportion of previous studies has dealt with the problem of parameter estimation in one form or another. This may well not remain the focus of attention in the future. Instead, filtering theory may find wider application in the context of data assimilation, that is, in reconstructing fields of flow and the migration of sub-surface contaminant plumes from relatively sparse observations.

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Received: October 27, 1997

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Eigbe, U., Beck, M., Wheater, H. et al. Kalman filtering in groundwater flow modelling: problems and prospects. Stochastic Hydrology and Hydraulics 12, 15–32 (1998). https://doi.org/10.1007/s004770050007

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  • DOI: https://doi.org/10.1007/s004770050007

  • Key words: Kalman filtering
  • groundwater modelling
  • inverse methods
  • uncertainty analysis
  • state prediction
  • parameter estimation