Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter. Issue 8 (2nd August 2020)

• Record Type: Journal Article
• Authors: Sun, Y.; Bao, W.; Valk, K.; Brauer, C. C.; Sumihar, J.; Weerts, A. H.
• Is Part Of: Water resources research. Volume 56:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-02
• Subjects: state updating -- lowland hydrology -- Kalman filters -- streamflow -- verification; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR027468
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: For operational water management in lowlands and polders (for instance, in the Netherlands), lowland hydrological models are used for flow prediction, often as an input for a real‐time control system to steer water with pumps and weirs to keep water levels within acceptable bounds. Therefore, proper initialization of these models is essential. The ensemble Kalman filter (EnKF) has been widely used due to its relative simplicity and robustness, while the unscented Kalman filter (UKF) has received little attention in the operational context. Here, we test both UKF and EnKF using a lowland lumped hydrological model. The results of a reforecast experiment in an operational context using an hourly time step show that when using nine ensemble members, both filters can improve the accuracy of the forecast by updating the state of a lumped hydrological model (Wageningen Lowland Runoff Simulator, WALRUS) based on the observed discharge, while UKF has achieved better performance than EnKF. Additionally, we show that an increase in the ensemble members does not necessarily mean a significant increase in performance. WALRUS model with either UKF or EnKF could be considered for hydrological forecasting for supporting water management of polders and lowlands, with UKF being the computationally leaner option. Key Points: The ensemble Kalman filter and unscented Kalman filter are utilized to improve forecast skill of a hydrological model by state estimation Both approaches have improved the forecast performance, with unscented Kalman filter being the computationally leaner option The unscented Kalman filter can be considered as a new and effective option to assimilate observations into low‐dimensional models