The Role of Prior Model Calibration on Predictions with Ensemble Kalman Filter

What is it about?

Groundwater flow models are usually conditioned to a limited amount of data through inverse modelling but data assimilation techniques, such as the ensemble Kalman filter (EnKF), can update in real time the conditioned models with newly available measurement data. This publication demonstrates the feasibility of data assimilation in groundwater modelling with EnKF for a real world case (Limmat Aquifer, Zurich) and showed that improved predictions of groundwater levels can be obtained, particularly for models that were conditioned on the basis of few data.

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Why is it important?

A great tool to improve prediction.