Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

In hydrologic modeling, state-parameter estimation using data assimilation techniques is increasing in popularity. Several studies, using both the ensemble Kalman filter (EnKF) and the particle filter (PF) to estimate both model states and parameters have been published in recent years. Though there is increasing interest and a growing literature in this area, relatively little research has been presented to examine the effectiveness and robustness of these methods to estimate uncertainty. This study suggests that state-parameter estimation studies need to provide a more rigorous testing of these techniques than has previously been presented. With this in mind, this paper presents a study with multiple calibration replicates and a range of performance measures to test the ability of each technique to calibrate two separate hydrologic models. The results show that the EnKF is consistently overconfident in predicting streamflow, which relates to the assumption of a Gaussian error structure. In addition, the EnKF and PF were found to perform similarly in terms of tracking the observations with an expected value, but the potential for filter divergence in the EnKF is highlighted.