The prediction of tidal waves is essential for improving not only our understanding of the hydrological cycle at the boundary between the land and ocean but also energy production in coastal areas. As tidal waves are affected by various factors, such as astronomical, meteorological, and hydrological effects, the prediction of tidal waves in estuaries remains uncertain. In this study, we present a novel method that can be used to improve short-term tidal wave prediction using a fixed-lag smoother based on sequential data assimilation (DA). The proposed method was implemented for tidal wave predictions of the estuary of the Nakdong River. As a result, the prediction accuracy was improved by 63.9% through DA and calibration using regression. Although the accuracy of the DA diminished with the increasing forecast lead times, the 1 h lead forecast based on DA still showed a 44.4% improvement compared to the open loop without DA. Moreover, the optimal conditions for the fixed-lag smoother were analyzed in terms of the order of the smoothing function and the length of the assimilation window and forecast leads time. It was suggested that the optimal DA configuration could be obtained with the 8th-order polynomial as the smoothing function using past and future DA assimilation windows assimilated 6 h or longer.

In this study, we analyzed the impact of model spatial resolution on streamflow predictions, focusing on high-resolution scenarios (<1 km) and flooding conditions at catchment scale. Simulation experiments were implemented for the Geumho River catchment in South Korea using Weather Research and the Forecasting Hydrological Modeling System (WRF-Hydro) with spatial resolutions of 100 m, 250 m, and 500 m. For the estimation of parameters, an automatic calibration tool based on the Model-Independent Parameter Estimation and Uncertainty Analysis (PEST) method was utilized. We assessed the hydrological predictions across different spatial resolutions considering calibrated parameters, calibration runtime, and accuracy of streamflow before and after calibration. For both Rainfall Events 1 and 2, significant improvements were observed after event-specific calibration in all resolutions. Particularly for 250 m resolution, NSE values of 0.8 or higher were demonstrated at lower gauging locations. Also, at a 250 m resolution, the changes in the calibrated parameter values ($REFKDT$) were minimized between Rainfall Events 1 and 2, implicating more effective calibration compared to the other resolutions. At resolutions of 100 m and 500 m, the optimal parameter values for the two events were distinctively different while more computational resources were required for calibration in Event 2 with drier antecedent conditions.

Predicting the tidal wave is essential not only to better understand hydrological cycle at the boundary between land and ocean but also to improve energy production in the coastal area. As affected by various factors such as astronomical, meteorological, and hydrological effects, predicting the tidal wave at the estuary remains uncertain. In this study, we present a novel method to improve short-term tidal wave predictions using a fixed-lag smoother based on sequential data assimilation (DA). The proposed method is implemented for the tidal wave predictions at the estuary of Nakdong River. As a result, the prediction accuracy was improved by 63.9% with DA and the calibration using the regression. Although the accuracy of DA diminished for increasing forecast lead times, the 1-hr lead forecast by DA had still 44.4% improvement over the open loop without DA. Plus, the optimal conditions for the fixed-lag smoother were analyzed in terms of the order of a smoothing function and the length of assimilation window and forecast lead time. It was suggested that the optimal DA configuration could be obtained with the 8th order polynomial as a smoothing function assimilated under 6-hr or longer past and future DA windows.