| Title: | Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python |
| Authors: | Zhang, X., P. Beeson, R. Link, D. Manowitz, R.C. Izaurralde, A. Sadeghi, A.M. Thomson, R. Sahajpal, R. Srinivasan and J.G. Arnold |
| Year: | 2013 |
| Journal: | Environmental Modelling & Software |
| Volume (Issue): | 46 |
| Pages: | 208–218 |
| Article ID: | |
| DOI: | 10.1016/j.envsoft.2013.03.013 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | computational approaches |
| Primary Application Category: | computational efficiency |
| Secondary Application Category: | calibration, sensitivity, and/or uncertainty analysis |
| Watershed Description: | 334 km^2 Little River Experimental in southwestern Georgia, U.S. and the 788 km^2 South Fork of the Iowa River in north central Iowa, U.S. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Language: | English |
| Keywords: | Parallel processing, Evolutionary multi-objective optimization, High performance computer, Soil and water assessment tool, Parameter calibration |