SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Improving streamflow predictions at ungauged locations with real-time updating: Application of an EnKF-based state-parameter estimation strategy 
Authors:Xie, X., S. Meng, S. Liang and Y. Yao 
Journal:Hydrology and Earth System Sciences 
Volume (Issue):18(10) 
Article ID: 
URL (non-DOI journals): 
Model:SWAT (modified) 
Broad Application Category:hydrologic only 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:hydrologic assessment 
Watershed Description:1129 km^2 Zhanghe River, located in Hubei Province in central China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The realtime updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remotesensing observations.