Water Engineering Research

Korea Water Resources Association (한국수자원학회)
권호 표지

APPLICATION AND EVALUATION OF THE GLEAMS MODEL TO A CATTLE GRAZING PASTURE FIELD IN NORTH ALABAMA

  • Kang, M. S. (Postdoctoral Research Associate) ;
  • P. prem, P.-Prem (Department of Biosystems Engineering, Auburn University) ;
  • Yoo, K. H. (Department of Biosystems Engineering, Auburn University) ;
  • Im, Sang-Jun (Water Resources Research Dept., KICT)
  • Published : 2004.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

The GLEAMS (Groundwater Loading Effects of Agricultural Management System, version 3.0) water quality model was used to predict hydrology and water quality and to evaluate the effects of soil types from a cattle-grazed pasture field of Bermuda-Rye grass rotation with poultry litter application as a fertilizer in North Alabama. The model was applied and evaluated by using four years (1999-2002) of field-measured data to compare the simulated results for the 2.71- ha Summerford watershed. $R^2$ values between observed and simulated runoff, sediment yields, TN, and TP were 0.91, 0.86, 0.95, and 0.69, respectively. EI (Efficiency Index) of these parameters were 0.86, 0.67, 0.70, and 0.48, respectively. The statistical parameters indicated that GLEAMS provided a reasonable estimation of the runoff, sediment yield, and nutrient losses at the studied watershed. The soil infiltration rates were compared with the rainfall events. Only high intensity rainfall events generated runoff from the watershed. The measured and predicted infiltration rates were higher during dry soil conditions than wet soil conditions. The ratio of runoff to precipitation was ranging from 2.2% to 8.8% with average of 4.3%. This shows that the project site had high infiltration and evapotranspiration which generated the low runoff. The ratio of runoff to precipitation according to soil types by the GLEAMS model appeared that Sa (Sequatchie fine sandy loam) soil type was higher and Wc (Waynesboro fine sandy loam, severely eroded rolling phase) soil type relatively lower than the weighted average of the soil types in the watershed. The model under-predicted runoff, sediment yields, TN, and TP in Wb (Waynesboro fine sandy loam, eroded undulating phase) and Wc soil types. General tendency of the predicted data was similar for all soil types. The model predicted the highest runoff in Sa soil type by 105% of the weighted average and the lowest runoff in Wc soil type by 87% of the weighted average

Keywords

References

  1. ASTM Designation: D5093-90 (Reapproved 1997). (1990). Standard test method for field measurement of infiltration rate using a double-ring infiltrometer with a sealed-inner ring. Annual book of ASTM standards, Vol. 04-08
  2. Bakhsh, A., and Kanwar, R.S. (2001). Simulating tillage effects on nonpoint source pollution from agricultural lands using GLEAMS. Trans. of ASAE, Vol. 44, No. 4, pp. 891-898
  3. Bakhsh, A., Kanwar, R.S., Jaynes, D.B., Colvin, T.S., and Ahuja, L.R. (2000). Prediction of $NO_3-N$ losses with subsurface drainage water from manured and UAN-fergilized plots using GLEAMS. Trans. of ASAE, Vol. 43, No. 1, pp. 69-77
  4. Carter, T.E., Wolfe, M.L., and Woeste, F.E. (1996). Risk assessment formulation for nitrate in ground water. Presented at the International Summer Meeting of ASAE. ASAE Paper No. 96-2030. St. Joseph, Mich.: ASAE
  5. Chin, Y.M., Park, S.W., Kim, S.M., Kang, M.S., and Kang, M.G. (2002). Nutrient loads estimation at paddy field using CREAM-PADDY model. J. of the Korean Society of Rural Planning (KSRP,) Vol. 8, No. 1, pp. 60-68. (In Korean)
  6. Chinkuyu, A.J., and Kanwar, R.S. (2001). Predicting soil nitrate-nitrogen losses from incorporated poultry manure using the GLEAMS model. Trans. of ASAE, Vol. 44, No. 6, pp. 1643-1650
  7. Diebel, P.L., Taylor, D.B., Batie, S.S., and Heatwole, CD. (1992). Low-input agriculture as a ground water protection strategy. Water Res. Bull. Vol. 28, No. 4, pp. 755-761
  8. Gerwig, B.K., Stone, K.C., Williams, R.G., Watts, D.W., and Novak, J.M. (2001). Using GLEAMS and REMM to estimate nutrient movement from a spray field and through a riparian forest. Trans. of ASAE, Vol. 44, No. 3, pp. 505-512
  9. Knisel, W.G. (1980). CREAMS: A field scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. Cons. Res. Rpt 26. Washington, D.C.: USDA
  10. Knisel, W.G. (1993). GLEAMS groundwater loading effects of agricultural management systems (v.2.1), User's Mannual. UGA-CPES-BAED Pub. No. 5. Washington, D.C.: USDA
  11. Knisel, W.G. and Davis, F.M. (2000). GLEAMS Groundwater Loading Effects of Agricultural Management Systems Version 3.0 User Manual. Pub. No.: SEWRL-WGK/FMD-050199
  12. Leonard, R.A., Knisel, W.G., and Still, D.A. (1987). GLEAMS: Groundwater Loading Effects of Agricultural Management Systems. Trans. of ASAE, Vol. 30, No. 5, pp. 1403-1418
  13. Ma, L., Shaffer, M.J., Boyd, J.K., Waskom, R., Ahuja, L.R., Rojas, K.W., and Xu, C. (1998). Manure management in an irrigated silage corn field: Experiment and modeling. Soil Sci. Soc, Am. J. Vol. 62, No. 4, pp. 1006-1017
  14. Maidment, D. R., et al. (1993). Handbook of Hydrology. McGraw-Hill, Inc.
  15. Minkara, M.Y., Wilhoit, J.H., Wood, C.W., and Yoo, K.S. (1995). Nitrate monitoring and GLEAMS simulation for poultry litter application to pine seedlings. Trans. of ASAE, Vol. 38, No. l,pp. 147-152
  16. Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology 10: 282-290 https://doi.org/10.1016/0022-1694(70)90255-6
  17. Prem, B. P. (2003). Application of WEPP, and AGNPS computer models for simulation of sediment yield and nutrient losses. MS theis. Auburn, Alabama: Auburn University, Department of Biosystems Engineering
  18. Ramamarayanan, T.S., Williams, J.R., Dugas, W.A., Hauck, L.M., and McFarland, A.M.S. (1997). Using APEX to identify alternative practices for animal waste management. ASAE Paper 97-2209
  19. Reyes, M.R., and Cecil, K.D. (1997). Comparing GLEAMS, RUSLE, EPIC, and WEPP soil loss predictions with two years of observed data, ASAE paper No. 97-2120. St. Joseph, Mich.: ASAE
  20. Sabbagh, G.J., Geleta, S., Elliott, R.L., Willians, J.R., and Griggs, R.H. (1991). Modification of EPIC to simulate pesticide activities. Trans. of ASAE, Vol. 34, No. 4, pp. 1683-1692
  21. Santhi, C, Arnold, J.G., Williams, J.R., Hauck, L.M., and Dugas, W.A. (2001). Application of a watershed model to evaluate management effects on point and nonpoint source pollution. Trans. of ASAE, Vol. 44, No. 6, pp. 1559-1570
  22. Seo, C.S., Park, S.W., Im, S.J., Yoon, K.S., Kim, S.M., and Kang, M.S. (2002). Development of CREAMS-PADDY model for simulating pollutants from irrigated paddies. J. of the Korea Society of Agricultural Engineers (KSAE,) Vol. 44, No. 3, pp.146-156
  23. Shirmohammadi, A., Ulen, B., Bergstrom, L.F., and Knisel, W.G. (1998). Simulation of nitrogen and phophorus leaching in a structured soil using GLEAMS and a nuw submodel, 'PARTLE'. Trans. of ASAE, Vol. 41, No. 2, pp. 353-360
  24. Smith, M.C., Bottcher, A.B., Campbell, K.L., and Thomas, D.L. (1991). Field testing and comparison of the PRZM and GLEAMS models. Trans. of ASAE, Vol. 34, No. 3, pp. 838-847
  25. Stone, K.C., Hunt, P.G., Johnson, M.H., and Coffey, S.W. (1998). GLEAMS simulation of groundwater nitrate-N from row crop and swine wastewater sprafy fields in the Eastern Coastal Plain. Trans. of ASAE, Vol. 41, No. 1, pp. 51-57
  26. Tucker, M.A., Thomas, D.L., Bosch, D.D., and Vellidis, G. (2000). GIS-based coupling of GLEAMS and REMM hydrology: I . development and sensitivity. Trans. of ASAE, Vol. 43, No. 6, pp. 1525-1534
  27. Tucker, M.A., Thomas, D.L., Bosch, D.D., and Vellidis, G. (2000). GIS-based coupling of GLEAMS and REMM hydrology: II. Field test results. Trans. of ASAE, Vol. 43, No. 6, pp. 1535-1544
  28. Wedwick, S., Lakhani, B., Stone, J., Waller, P., and Artiola J. (2001). Development and sensitivity analysis of the GLEAMS-IR model. Trans. of ASAE, Vol. 44, No. 5, pp. 1095-1104
  29. Yoon, K.S., Yoo, K.H., Wood, C.W., and Hall, B. M. (1994). Application of GLEAMS to predict nutrient losses from land application of poultry litter. Trans. of ASAE, Vol. 37, No. 2, pp. 453-459