USING KALMAN FILTERING TO IMPROVE AND QUANTIFY THE UNCERTAINTY OF NUMERICAL GROUNDWATER SIMULATIONS .1. THE ROLE OF SYSTEM NOISE AND ITS CALIBRATION

Deterministic models are widely used to describe the behavior of the hydraulic head in time and space. It would be useful to know the uncertainty of these approximations of reality, what data on groundwater head they require and whether these data can be used to improve the model's performance after calibration/validation. Therefore the use of a Kalman filter algorithm for assessing the uncertainty of a deterministic groundwater model is described. It is suggested that the strength of the Kalman filter is that it makes good use of the available information to assess uncertainty and to improve model performance. This has implications for applications such as the analysis and design of groundwater networks and on-line groundwater management. After briefly describing how to combine a Kalman filter with a deterministic groundwater model, some important aspects like quantifying the system noise statistics and calibrating these values are emphasized. These aspects are illustrated using a one-dimensional hypothetical example of a groundwater system.