Comparison of Two Methods for Modeling Monthly Total Phosphorus (TP) Yield from a Watershed K. L. White 1, I. Chaubey 1, B. E. Haggard 2, M. D. Matlock 1 Ecological Engineering Group 1 Biological & Agricultural Engineering - University of Arkansas 2 USDA-ARS Poultry Production & Product Safety Research Unit - Fayetteville, AR Introduction The occurrence of increased nutrient loads has been identified as the leading cause of impairment in assessed lakes and reservoirs (USEPA, 2000). A reservoir that continues to receive excessive nutrients may experience premature decreases in oxygen concentrations, increases in suspended solids, progression from a diatom population to a blue-green or green algae population, changes in food web structure and fish species composition, and decreasing light penetration (OECD, 1982; Henderson-Sellers and Markland, 1987). Management of reservoir nutrient loading requires an understanding of nutrient transport and delivery from the watershed-stream system. Nutrients are generally transported from the landscape during runoff events and are carried into the stream system. Nutrients may also enter stream flow from other sources such as groundwater recharge and point source effluent discharges. In the stream, they undergo biotic and abiotic cycling. Computer models are available that simulate nutrient transport in a watershed and instream nutrient processes; however, they are generally not incorporated into one model. The Soil and Water Assessment Tool (SWAT) model has incorporated some ability to simulate instream nutrient processes. SWAT developers modified equations from the instream water quality model, QUAL2E (Brown and Barnwell, 1987), and provided SWAT model users with the ability to include or exclude these calculations in their watershed model simulations (Neitsch et al., 2001). Although the SWAT model has incorporated these instream components, there is uncertainty regarding their ability to substitute for a complete instream water quality model in predicting nutrient yields from a watershed. Methods SWAT model (method 1) For the calibration of the War Eagle SWAT model, three statistical objective functions were included. We defined the multi-objective function as the optimization of the following three statistics: relative error (RE), Nash-Sutcliffe coefficient ( R NS 2 ), and coefficient of determination (R 2 ) on annual and monthly time scales. The SWAT model was calibrated and validated with instream components active. Objectives Results and discussion Model calibrations were achieved for by optimizing the objective function. The SWAT model was successfully validated using a new data set and the same objective function. The SWAT-QUAL2E linked model was qualitatively validated as shown in Fig. 5. Validation suggested that summer predictions were the least representative of measured values. Table 1 indicates that there were some differences between the two modeling methods and measured TP values. These differences were tested using the statistical methods described (Tables 2 and 3), which indicated that neither method was significantly better at predicting monthly TP values. Results suggest that the SWAT model with active instream components predicts TP monthly yields from a watershed as well as a SWAT-QUAL2E linked model. Although there has been uncertainty regarding the incorporation of QUAL2E equations into the SWAT model (Houser and Hauck, 2002), these results indicated that the SWAT model with incorporated instream algorithms from QUAL2E was as sufficient as linking a SWAT model to a QUAL2E model at predicting monthly TP yields from War Eagle Creek. Conclusions Acknowledgements References Collecting field data to be used to describe the reaches in the QUAL2E model 1)Predict monthly TP yields from the SWAT model with instream components active for a watershed (method 1) 2)Predict monthly TP yields from a SWAT model without instream components active that is loosely linked to a QUAL2E model (method 2) 3)Determine if significant differences exist between values predicted with both modeling methods and measured data Fig 1: War Eagle Creek is one of the main tributaries to Beaver Reservoir, which is the primary drinking water supply for Northwest Arkansas. It encompasses approximately 68,100 ha with land use distributions of 63.7% forest, 35.6% pasture, 0.5% urban, and 0.2% water (CAST, 2002). Fig 3: Flow chart representing modeling method 2 Comparison of two modeling methods Results from modeling methods 1 and 2 were compared to determine if predicted monthly TP yields were significantly different from each other. Two variations of the Pearson product-moment correlation coefficient (p<0.05) were investigated which tested: (1) the null hypothesis that the correlation between the two variables in the underlying populations represented by the two samples were equal, and 2) the null hypothesis that the slopes of two regression lines obtained from two independent samples were equal (Sheskin, 2000). SWAT model linked with QUAL2E model (method 2) To accommodate for the steady state characteristic of the QUAL2E model, three QUAL2E models representing War Eagle Creek were defined for each season: summer (or low flow), fall (low flow after leaf abscission), and winter-spring (high flow) (Zhang et al., 1996). Summer, fall, and winter- spring were considered by months as Jul. through Sept., Oct. through Dec., and Jan. through Jun. These seasons were chosen to account for differences in stream flow and nutrient dynamics that occur throughout the year (Haggard et al., 2003). SWAT model nutrient and flow predictions were evaluated with SWAT model instream components inactive. These predictions were used as QUAL2E model input, hence linkage between the two models (Fig. 3). The linked models were calibrated using RE. Validation was performed using a qualitative approach based on predicted outputs and measured data. Fig 4: Physical representation of the QUAL2E model for War Eagle Creek Fig 2: Flow chart representing modeling method 1 Month USGS gauge (kg TP) SWAT (kg TP) SWAT plus QUAL2E (kg TP) January9591, February2,6221, March2,1431, April2,5911, May June July August September October November December2,4881, Modeling method Regression statistics R2R2 β11β11 β02β02 SWAT model SWAT model linked to QUAL2E model Table 1: Average monthly TP yields from 2001 and 2002 for the War Eagle Creek SWAT model (method 1) and the SWAT model linked to the QUAL2E model (method 2) and respective measured values Table 2: Statistics from regressing model predicted monthly TP on measured TP 1 Slope of the regression line 2 Y-intercept of the regression line Statistical testHypothesesTest statisticConclusion Pearson product- moment correlation considering population correlations z (Fisher’s z) =0.703 (z 0.05 =1.96) Failed to reject H 0 Pearson product- moment correlation considering slope of the regression lines t (Student’s t distribution) = 1.51 (t 0.05 = 2.09) Failed to reject H 0 Table 3: Results from testing the significant differences between the two modeling methods and their relationship to measured monthly TP values Objective 1: The SWAT model was successfully used to simulate the War Eagle Creek Watershed to estimate monthly TP yields for 2001 and Objective 2: The SWAT model was successfully linked to an independent QUAL2E model to predict monthly TP yields for 2001 and Objective 3: No statistically significant differences were observed between the predicted monthly TP yields of the two modeling approaches (the SWAT model (method 1) and the SWAT model linked to a QUAL2E model (method 2)) when compared to measured data. Brown, L. C. and T. O. Barnwell, Jr., The enhanced stream water quality models QUAL2E and QUAL2E- UNCAS: Documentation and User Manual. Tufts University and US EPA, Athens, Georgia. CAST, Land use/ land cover data. Available at: Accessed in January Haggard, B. E., P. A. Moore Jr, I. Chaubey, and E. H. Stanley, Nitrogen and phosphorus concentrations and export from an Ozark Plateau catchment in the United States. Biosystems Engineering 86(1): Henderson-Sellers, B. and H. R. Markland, Decaying Lakes: The origins and control of cultural eutrophication. Great Britain: John Wiley & Sons Ltd. Houser, J.B. and L.M. Hauck, Analysis of the in-stream water quality component of SWAT (Soil and Water Assessment Tool). Proceedings of the TMDL Environmental Regulations Conference. ASAE. St. Joseph, MI Neitsch, S.L., J. G. Arnold, J. R. Kiniry, and J. R. Williams, Soil and Water Assessment Tool Theoretical Documentation Version OECD (Organization for Economic Cooperation and Development), Eutrophication of waters: monitoring, assessment, and control. OECD Cooperative Programme on Monitoring of Inland Waters. Paris, p 154. Sheskin, D. J., Handbook of Parametric and Nonparametric Statistical Procedures Second edition. Boca Raton, FL.: CRC Press. USEPA, National Water Quality Inventory Report. Zhang, J., T. S. Tisdale, and R. A. Wagner, A basin scale phosphorus transport model for south Florida. Applied Engineering in Agriculture. 12(3): We would like to acknowledge the Division of Agriculture, University of Arkansas and the Arkansas Soil and Water Conservation Commission for their financial support. In addition, we would like to thank the ecological engineering undergraduate field workers, Chad Cooper, and Eylem Mutlu for their assistance in completing the field work. Table 1: Average monthly TP yields from 2001 and 2002 for the War Eagle Creek SWAT model (method 1) and the SWAT- QUAL2E linked model (method 2) and respective measured values The War Eagle Watershed is characterized by P sources that demonstrate different transport and delivery mechanisms (e.g., WWTP effluent, cattle manure, commercial fertilizer, poultry litter). Land cover composition, which influences TP transport and delivery within a watershed, was also detailed within the model. Results from this project suggest that watersheds with similar P sources and land cover would also be sufficiently represented by a SWAT model with active instream components. Watersheds with substantially different sources of P and/or land cover might not have the same result. Fig 5: Qualitative comparison of measured and QUAL2E predicted TP concentrations by reach for each season with concentrations ranked within each data set and represented with increasing symbol size corresponding to increasing concentration The steady state limitation of the QUAL2E model hinders its ability to simulate dynamic P processes, such as nonpoint sources of P and P resuspension within the stream channel. This suggests that if an instream model were available that possessed greater spatial and temporal resolutions than the QUAL2E model, there would be a need to reevaluate this concept by linking the SWAT model with the improved instream water quality model. Winter-Spring Summer Fall