SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Improving the distributed hydrological model performance in Upper Huai River Basin: Using streamflow observations to update the basin states via the Ensemble Kalman Filter 
Authors:Liu, Y., W. Wang, Y. Hu and W. Cui 
Journal:Advances in Meteorology 
Volume (Issue):2016 
Article ID:4921616 
URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:hydrologic assessment 
Watershed Description:16,005 km^2 Upper Huai River, located in northeast China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.