Development on an Automatic Calibration Module of the SWMM for Watershed Runoff Simulation and Water Quality Simulation

The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

[1]  W. Price Global optimization algorithms for a CAD workstation , 1987 .

[2]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[3]  Taeuk Kang,et al.  A Study for an Automatic Calibration of Urban Runoff Model by the SCE-UA , 2012 .

[4]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[5]  Q. Duan,et al.  A global optimization strategy for efficient and effective calibration of hydrologic models. , 1991 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[8]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[9]  James E Ball,et al.  Evaluation of spatially variable control parameters in a complex catchment modelling system : a genetic algorithm application , 2007 .

[10]  J. Cho,et al.  Parameter Optimization for Runoff Calibration of SWMM , 2006 .

[11]  A Study for a Reasonable Application of the SWMM to Watershed Runoff Event Simulation , 2012 .

[12]  Stephan Haas,et al.  Global optimization algorithms , 2009 .

[13]  Kyung-sook Choi,et al.  Parameter estimation for urban runoff modelling , 2002 .

[14]  Yan-Bin Jia,et al.  The simplex method , 2019, 100 Years of Math Milestones.

[15]  Michael K. Stenstrom,et al.  Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment , 2008 .

[16]  B. Martin PARAMETER ESTIMATION , 2012, Statistical Methods for Biomedical Research.