Multi-objective model auto-calibration and reduced parameterization: Exploiting gradient-based optimization tool for a hydrologic model |
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| Affiliation: | 1. Institute of Water Saving Agriculture in Arid Areas of China, Northwest A & F University, Yangling, PR China;2. College of Tourism and Environment, Shaanxi Normal University, Xi’an, Shaanxi, PR China;3. Key Laboratory for Agricultural Soil and Water Engineering in Arid Areas of Ministry of Education, Northwest A & F University, Yangling, PR China;4. National Engineering Laboratory for Crop Water Efficient Use, Yangling, PR China;1. CSIRO Land and Water, Private Mail Bag 2, Glen Osmond, SA 5064, Australia;2. CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia;3. Centre for Integrative Ecology, Deakin University, Burwood, VIC 3125, Australia;1. Institute for Life Sciences and the Environment, University of Southern Queensland, Australia;2. Department of Biosystems and Agricultural Engineering, Michigan State University (MSU), USA;3. Civil & Environmental Engineering Department, Colorado State University, USA;1. Department of Ecology and Environmental Science, University of Adelaide, Australia;2. South Australian Water Corporation, Australia |
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| Abstract: | Multi-objective model optimization methods have been extensively studied based on evolutionary algorithms, but less on gradient-based algorithms. This study demonstrates a framework for multi-objective model calibration/optimization using gradient-based optimization tools. Model-independent software Parameter ESTimation (PEST) was used to auto-calibrate ISWAT, a modified version of the distributed hydrologic model Soil and Water Assessment Tool (SWAT2005), in the Shenandoah River watershed. The time-series processor TSPROC was used to combine multiple objectives into the auto-calibration process. Two sets of roughness coefficients for main channels, one assigned and calibrated according on soil types and one determined via empirical equations, were examined for stream discharge simulation. Five different weighting alternatives were investigated for their effects on ISWAT calibrations. Results showed that using Manning's roughness coefficients obtained from empirical equations improves simulation results and calibration efficiency. Applying a two-step weighting alternative to different observation groups would provide the best calibration results. |
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| Keywords: | PEST SWAT TSPROC Multi-objectives Auto-calibration |
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