IMPACT OF SYSTEM NOISE SPECIFICATION ON ENSEMBLE KALMAN FILTER PERFORMANCE IN A DISTRIBUTED HYDROLOGICAL MODEL

Abstract

 This paper presents a study on the performance of the Ensemble Kalman Filter with different system error settings when multi-site river flow observations are assimilated into a distributed hydrological model based on kinematic wave theory. We varied the scaling factor of the model error term and its correlation structure for a fixed observation error scaling and found the filter behaviour and the prediction accuracy to be highly dependent on the model error properties.While using standard normal model errors without scaling produced inferior results, with scaled errors, the effect of the scaling diminished when the model errors had higher correlation lengths. Identifying the proper model error correlation structure is therefore more important than determining the magnitude of the scaling for improving the filter performance.