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APPLICATION OF NON-LINEAR AUTOMATIC OPTIMIZATION TECHNIQUES FOR CALIBRATION HSPF

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Abstract:

Development of TMDL (total maximum daily load) is often facilitated by using the software system BASINS (Better Assessment Science Integrating point and Nonpoint Sources). The key element of BASINS is the watershed model HSPF (Hydrological Simulation Program Fortran) developed by USEPA. Calibration of HSPF is a very tedious and time consuming task, more than 100 parameters are involved in the calibration process. In current research, three non-linear automatic optimization techniques are applied and compared, as well an efficient way to calibrate HSPF is suggested. Parameter optimization using local and global optimization techniques for the watershed model is discussed. Approaches to automatic calibration of HSPF using the non-linear parameter estimator PEST (Parameter Estimation Tool) with its Gauss-Marquardt-Levenberg (GML) method, Random multiple Search Method (RSM) and SCE-UA (Shuffled Complex Evolution method developed in the University of Arizona) are presented.

Sensitivity analysis was conducted and the most and the least sensitive parameters were identified. It was noted that sensitivity depends on number of adjustable parameters. More parameters are optimized simultaneously; wider range of parameter values can maintain model in the calibrated state. Impact of GML, RSM and SCE-UA variables on ability to find the global minimum in the objective function (OF) was studied and the best variables are suggested. All three methods proved to be efficient and superior to manual HSPF calibration. Optimization results conducted by these methods are very similar. Though in most cases RSM outperforms GML and SCE-UA outperforms RSM. GML is a very fast method, it can perform as good as SCE-UA when the variables are properly adjusted, initial guess is good and insensitive parameters are eliminated from the optimization process. SCE-UA is a very robust and convenient to use. Logical definition of key variables in most cases leads to the global minimum. It was observed that proper formulation of the OFs and their weights is the key to reliable calibration of the watershed model.

Document Type: Research Article

DOI: https://doi.org/10.2175/193864705783812837

Publication date: 2005-01-01

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