SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Application of multimodal optimization for uncertainty estimation of computationally expensive hydrologic models 
Authors:Cho, H. and F. Olivera 
Journal:Journal of Water Resources Planning and Management 
Volume (Issue):140(3) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:sediment loss and transport 
Watershed Description:598 km^2 Big Sandy Creek in northeast Texas, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:The Generalized Likelihood Uncertainty Estimation (GLUE) framework has been widely used in hydrologic studies. However, an extensive random sampling causes a high computational burden, which prohibits the efficient application of GLUE to costly distributed hydrologic models such as the Soil and Water Assessment Tool (SWAT). In this study, a multi-modal optimization algorithm called Isolated-Speciation-based Particle Swarm Optimization (ISPSO) is employed to take samples from the search space. A comparison between the ISPSO-GLUE, proposed here, and traditional GLUE approaches shows that the two approaches generate similar uncertainty bounds, but that the convergence rate to stable uncertainty bounds is much faster for ISPSO-GLUE than for GLUE. That is, ISPSO-GLUE needs a much smaller number of samples than GLUE to arrive to a very similar answer. Although the ISPSO-GLUE slightly underestimated the prediction uncertainty and missed a number of observed values, the proposed approach is considered to be a good alternative to the typical GLUE approach that employs random sampling.