The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method

► We evaluate the sensitivity of the Xinanjiang RR (rainfall-runoff) model parameters. ► Model parameters are estimated in batch using a particle swarm optimization algorithm. ► We examine the correlations between state variables and streamflow. ► The Xinanjiang RR model coupled with a dual ensemble Kalman Filter provides accurate predictions of streamflow. ► The coupled model is applied to an ungauged basin. To accurately estimate floods with hydrological models, the model parameters and the initial state variables must be known. Good estimations of parameters and initial state variables are required to enable the models to make accurate estimations. The Xinanjiang rainfall-runoff (RR) model has been widely used in humid and semi-humid regions in China. In this paper, we evaluate the sensitivity of the Xinanjiang RR model parameters and the correlation between the state variables and the output streamflow. In order to reduce the impact of streamflow data error, model structural error and parameter uncertainty, the Xinanjiang RR model is coupled with intelligent optimization algorithms and a data assimilation method to estimate the streamflow in the Luo River in China that was first considered as gauged, and then as ungauged for parameter calibration. Model parameters are estimated in batch using a particle swarm optimization algorithm and three variations of ensemble Kalman Filter data assimilation (the state variable assimilation, parameter assimilation and the dual assimilation at the same time) for the Luo River, and the ungauged basin. In this case, the model parameters are set equal to median values. The results show that when parameter values are determined by an optimization algorithm using 10years of data, the dual ensemble Kalman Filter notably improves the simulated results. When the parameters adopt the median, the state variable assimilation has little effect on the estimation results, but the parameter assimilation positively affects the simulated results. The dual ensemble Kalman Filter also notably improves the simulated results. The time scale of assimilation has little effect on simulation results for the dual assimilation, but it has a large effect on the state variable assimilation and the parameter assimilation.