SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Investigating the dynamic influence of hydrological model parameters on runoff simulation using sequential uncertainty fitting-2-based multilevel-factorial-analysis method 
Authors:Zhou S., Y. Wang, J. Chang, A. Guo and Z. Li 
Year:2018 
Journal:Water 
Volume:10(9) 
Pages: 
Article ID:1177 
DOI:10.3390/w10091177 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:hydrologic assessment 
Watershed Description:12,190 km^2 Yellow River source region, located in west central China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model. 
Language:English 
Keywords:CMADS dataset; parameter sensitivity; SUFI-2; Yellow River