Impact of assimilating dam outflow measurements to update distributed hydrological model states: Localization for improving ensemble Kalman filter performance

Abstract

This paper presents an investigation on the effect of dam operation on the ensemble Kalman filter (EnKF) performance in a distributed hydrological model based on kinematic wave theory. For this purpose, two flood events in a mountainous catchment in Japan are selected, and the model error parameters for the EnKF implementation are first determined through a series of experiments by varying the input error magnitude, state variable perturbation, and its correlation length. Then, through the assimilation of dam outflow observations, it is shown that special treatment of such measurements is critical in ensuring the success of the filter when flood control operation significantly changes the flow hydrograph and when the covariances between distant grids are high. Localization methods based on Euclidean distance, flow accumulation values, and river distance are all able to limit the deleterious updates to model states at distant grids and improve filter efficacy. However, no single localization approach is found to be consistently optimal for varying noise configurations, number (and type) of assimilated stations, or flood events. Separate localization of dam outflows from streamflow measurements by assuming the dam grids to be only related to their downstream grids shows potential in improving prediction at locations near the dams themselves while minimizing disruptions at long distances.