Application of non-linear automatic optimization techniques for calibration of HSPF
1Department of Civil Engineering, University of Ottawa, 161, Louis Pasteur, Ottawa, Ontario K1N 6N5, Canada.
Summary
Automating Hydrological Simulation Program Fortran (HSPF) calibration using optimization techniques like PEST, RSM, and SCE-UA significantly improves efficiency over manual methods. These advanced approaches help identify optimal model parameters for Total Maximum Daily Loads (TMDLs) development.
Area of Science:
- Environmental Engineering
- Water Resource Management
- Computational Hydrology
Background:
- Total Maximum Daily Loads (TMDLs) development often relies on the Better Assessment Science Integrating point and Nonpoint Sources (BASINS) software.
- A core component of BASINS is the Hydrological Simulation Program Fortran (HSPF) watershed model, requiring extensive parameter calibration.
- Manual HSPF calibration is a complex and time-consuming process involving over 100 parameters.
Purpose of the Study:
- To compare the efficiency of three non-linear automatic optimization techniques for HSPF model calibration.
- To suggest an efficient method for calibrating the HSPF watershed model.
- To analyze parameter sensitivity and the impact of optimization variables on achieving the global minimum.
Main Methods:
- Application and comparison of three non-linear optimization techniques: Gauss-Marquardt-Levenberg (GML) within PEST, Random multiple Search Method (RSM), and Shuffled Complex Evolution (SCE-UA).
- Sensitivity analysis to identify the most and least sensitive HSPF parameters.
- Evaluation of the impact of optimization variables on the objective function's global minimum.
Main Results:
- All three automatic optimization methods (GML, RSM, SCE-UA) are more efficient than manual HSPF calibration.
- Optimization results from the methods are similar, with SCE-UA generally outperforming RSM, and RSM outperforming GML.
- Parameter sensitivity is influenced by the number of adjustable parameters; optimizing more parameters simultaneously allows a wider range for calibration.
Conclusions:
- Non-linear optimization techniques provide a more efficient approach to HSPF calibration for TMDL development.
- SCE-UA is robust and user-friendly, while GML is fast and effective with proper adjustments and initial parameter selection.
- Logical definition of key variables aids in achieving the global minimum, enhancing model accuracy and calibration efficiency.
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