| Title: | Evaluation of alternative surface runoff accounting procedures using the SWAT model |
| Authors: | Yen, H., M.J. White, J. Jeong, M. Arabi and J.G. Arnold |
| Year: | 2015 |
| Journal: | International Journal of Agricultural and Biological Engineering |
| Volume (Issue): | 8(1) |
| Pages: | 54-68 |
| Article ID: | |
| DOI: | 10.3965/j.ijabe.20150803.833 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic and pollutant |
| Primary Application Category: | modified runoff curve number approach |
| Secondary Application Category: | model and/or data comparison |
| Watershed Description: | 248 km^2 Eagle Creek Watershed, a tributary of the White River, located in central Indiana, U.S. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | This article is part of a set of 11 articles that comprise a SWAT special issue published in IJABE volume 8(3) in June, 2015. |
| Abstract: | For surface runoff estimation in the Soil and Water Assessment Tool (SWAT) model, the curve number (CN) procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil moisture condition (SCSI) in field. From SWAT2005 and onward, an alternative approach has become available to apply the CN method by relating the runoff potential to daily evapotranspiration (SCSII). While improved runoff prediction with SCSII has been reported in several case studies, few investigations have been made on its influence to water quality output or on the model uncertainty associated with the SCSII method. The objectives of the research were: 1) to quantify the improvements in hydrologic and water quality predictions obtained through different surface runoff estimation techniques; and 2) to examine
how model uncertainty is affected by combining different surface runoff estimation techniques within SWAT using Bayesian model averaging (BMA). Applications of BMA provide an alternative approach to investigate the nature of structural uncertainty associated with both CN methods. Results showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to SCSI. In addition, the application of BMA has a positive effect on finding well performed solutions in the multi-dimensional parameter space, but the predictive uncertainty is not evidently reduced or enhanced. Therefore, we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants. |
| Language: | English |
| Keywords: | SWAT, curve number method, Bayesian model averaging, uncertainty analysis; Hydrology, Water quality |