Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging
In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is...
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| Published in | Journal of hydrology (Amsterdam) Vol. 374; no. 3; pp. 307 - 317 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier B.V
15.08.2009
[Amsterdam; New York]: Elsevier Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-1694 1879-2707 |
| DOI | 10.1016/j.jhydrol.2009.06.023 |
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| Abstract | In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT. |
|---|---|
| AbstractList | In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT. The genetic algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). |
| Author | Zhang, Xuesong Srinivasan, Raghavan Bosch, David |
| Author_xml | – sequence: 1 givenname: Xuesong surname: Zhang fullname: Zhang, Xuesong email: Xuesongzhang2004@gmail.com organization: Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA – sequence: 2 givenname: Raghavan surname: Srinivasan fullname: Srinivasan, Raghavan organization: Spatial Sciences Laboratory, Department of Ecosystem Sciences and Management, Texas A&M University, College Station, TX 77843, USA – sequence: 3 givenname: David surname: Bosch fullname: Bosch, David organization: Southeast Watershed Research Laboratory, Agricultural Research Service, US Department of Agriculture, Tifton, GA 31793, USA |
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| Keywords | SWAT Uncertainty Modeling Basin Optimization bayesian analysis rivers algorithms models digital simulation case studies North America optimization prediction soils calibration uncertainties streamflow |
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| SubjectTerms | ALGORITHMS BASIC BIOLOGICAL SCIENCES Basin Bayesian model averaging CALIBRATION China Earth sciences Earth, ocean, space ENVIRONMENTAL SCIENCES Exact sciences and technology Freshwater GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE genetic algorithms GENETICS Georgia hydrologic models Hydrology Hydrology. Hydrogeology mathematics and statistics model validation Modeling Optimization RIVERS SIMULATION Soil and Water Assessment Tool model SOILS statistical analysis stream flow SWAT Uncertainty uncertainty analysis VALIDATION WATER watershed hydrology watersheds YELLOW RIVER |
| Title | Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging |
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