Evaluation of landscape and instream modeling to predict watershed nutrient yields |
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| Affiliation: | 1. Biological & Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK;2. USDA ARS, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA;3. AgResearch Ltd, Land & Environment, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand;4. Department of Geography, Durham University, Durham DH1 3LE, UK;5. School of Biosystems Engineering, Agriculture and Food Science Centre, University College Dublin, Belfield, Dublin, Ireland;6. Centre for Research into Environment & Health, Aberystwyth University, Wales SY23 3DB, UK;7. ADAS Group Ltd, HQ Pendeford House, Pendeford Business Park, Wolverhampton WV9 5AP, UK;8. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK;9. Cranfield Water Science Institute, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK;10. TEAGASC, Agricultural Catchments Programme, Johnstown Castle, Wexford, Ireland;11. Department of Civil & Environmental Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK |
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| Abstract: | The project goal was to loosely couple the SWAT model and the QUAL2E model and compare their combined ability to predict total phosphorus (TP) and NO3-N plus NO2-N yields to the ability of the SWAT model with its completely coupled water quality components to predict TP and NO3-N plus NO2-N yields from War Eagle Creek watershed in Northwest Arkansas. Model predictions were compared using a statistical approach to identify significant differences between the two modeling methods. Results from two variations of the Pearson product-moment correlation (p < 0.05) indicated that correlation coefficients and regression slopes for the two data sets were not significantly different. This implies that neither modeling method was significantly better in predicting monthly TP and NO3-N plus NO2-N yields from the watershed. Additionally, no significant differences were present between predicted outputs of the SWAT model with instream components active compared with when instream components were inactive, indicating a need for further testing and refinement of the SWAT algorithms simulating instream processes. We can further infer that the instream processes available in SWAT may not be enhancing its predictive abilities as far as simulating instream components. |
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