Water, Air, and Soil Pollution

, Volume 154, Issue 1–4, pp 271–293 | Cite as

Predicting Water, Sediment and NO3-N Loads under Scenarios of Land-use and Management Practices in a Flat Watershed

Article

Abstract

Changes in land-use or management practices may affect water outflow, sediment, nutrients and pesticides loads. Thus, there is an increasing demand for quantitative information at the watershed scale that would help decision makers or planners to take appropriate decisions. This paper evaluates by a modeling approach the impact of farming practices and land-use changes on water discharge, sediment and NO3-N loads at the outlet of a 51.29 km2 watershed of central Iowa (Walnut Creek watershed). This intensively farmed (corn-soybean rotation) watershed is characterized by a flat topography with tiles and potholes. Nine scenarios of management practices (nitrogen application rates: increase of current rate by 20, 40%, decrease of current rate by 20, 40 and 60%; no tillage) and land-use changes (from corn-soybean rotation to winter wheat and pasture) were tested over a 30 yr simulated period. The selected model (Soil and Water Assessment Tool, SWAT) was first validated using observed flow, sediment and nutrient loads from 1991 to 1998. Scenarios of N application rates did not affect water and sediment annual budgets but did so for NO3-N loads. Lessening the N rate by 20, 40 and 60% in corn-soybean fields decreased mean NO3-N annual loads by 22, 50 and 95%, respectively, with greatest differences during late spring. On the other hand, increasing input N by 20 and 40% enhanced NO3-N loads by 25 and 49%, respectively. When replacing corn-soybean rotation by winter wheat, NO3-N loads increased in early fall, immediately after harvest. Pasture installation with or without fertilization lessened flow discharge, NO3-N and sediment delivery by 58, 97 and 50%, respectively. No-tillage practices did not significantly affect the water resource and sediment loads. Finally, such realistic predictions of the impact of farming systems scenarios over a long period are discussed regarding environmental processes involved.

erosion hydrologic modeling hydrology land-use management practices nitrogen scenarios SWAT 

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References

  1. Arnold, J. G., Williams, J. R. and Maidment, D. R.: 1995, ‘Continuous-time water and sediment-routing model for large basins’, J. Hydr. Eng. 121, 171—183.Google Scholar
  2. Burt, T. P.: 2001, ‘Integrated management of sensitive catchment systems’, Catena 42, 275—290.CrossRefGoogle Scholar
  3. Burkart, M. R., Simpkins, W.W., Squillace, P. J. and Helmke, M.: 1999, ‘Tributary stream infiltration as a source of herbicides in an alluvial aquifer’, J. Environ. Qual. 28, 69—74.Google Scholar
  4. Cambardella, C. A., Moorman, T. B., Jaynes, D. B., Parkin, T. B., Simpkins, W.W. and Karlen, D. L.: 1999, ‘Water quality in Walnut Creek watershed: Nitrate-nitrogen in soils, subsurface drainage water and shallow groundwater’,J. Environ. Qual. 28, 25—34.Google Scholar
  5. Eidem, J. M., Simpkins, W. W. and Burkart, M. R.: 1999, ‘Geology, groundwater flow and water quality in the Walnut Creek watershed' J. Environ. Qual. 28, 60—69.Google Scholar
  6. EPA: 2000, http://www.epa.gov/ost/ftp/basins/system/BASINS3.Google Scholar
  7. ESR: 1997, ‘Understanding GIS. The ArcView GIS 3.2’, ESRI, 380 New York Steeet, Redlands, CA, U.S.A., 92273.Google Scholar
  8. Geddes, N. and Dunkerley, D.: 1999, ‘The influence of organic litter on the erosive effects of raindrops and of gravity drops released from desert shrubs’, Catena 36, 303—313.CrossRefGoogle Scholar
  9. Grimaldi, C. and Chaplot, V.: 2000, ‘Nitrate removal in headstreams. Influence of exchanges between stream and near-bank environnement’, Water, Air, Soil Pollut. 124, 95—112.CrossRefGoogle Scholar
  10. Hatfield, J. L. and Prueger, J. H.: 1997, ‘Spatial Variation of Rainfall in Walnut Creek Watershed’, in C. J. Richardson (ed.), Climate and Weather Workshop Proc., USDA-ARS Bull., 1996-03, USDA, Washington D.C., pp. 161—171.Google Scholar
  11. Hatfield, J. L. Prueger, J. H. and Sauer, T. J.: 1996, ‘Comparison of Evapotranspiration Equations over Different Surfaces’, in C. R. Camp et al. (ed.), Proc. ASAE Evapotranspiration and Irrigation Scheduling, ASAE, St. Joseph, MI, pp. 1065—1070.Google Scholar
  12. Hatfield, J. L. and Prueger, J. H.: 1997, ‘Spatial Variation of Rainfall in Walnut Creek Watershed’, in C. J. Richardson (ed.), Climate and Weather Workshop Proc., USDA-ARS Bull., 1996-03, USDA, Washington D.C., pp. 161—171.Google Scholar
  13. Horick, P. J.: 1984, ‘Mississippian Aquifer of Iowa’, Misc. Map Series 10, Iowa Geological Survey, Iowa City, IA.Google Scholar
  14. Horick, P. J. and Steinhilber, W. L.: 1973, ‘Mississippian Aquifer of Iowa’, Misc. Map Series 3, Iowa Geological Survey, Iowa City, IA.Google Scholar
  15. Horick, P. J. and Steinhilber, W. L.: 1978, ‘Jordan Aquifer of Iowa’, Misc. Map Series 6, Iowa Geological Survey, Iowa City, IA.Google Scholar
  16. IGBP (International Geosphere-Biosphere Programme): 1995, Land-use and Land Cover Change, Science Research Plan, Stockholm, pp. 123.Google Scholar
  17. Ingram, J., Lee, J. and Valentin, C.: 1996,’ The GCTE soil erosion network: A multidisciplinary research program’, J. Soil Water Conservation 51, 377—380.Google Scholar
  18. Jaynes, D. B., Hatfield, J. L. and Meek, D. W.: 1999, ‘Water quality in Walnut Creek watershed: Herbicides and nitrate in surface water’, J. Environ. Qual. 28, 45—59.Google Scholar
  19. Lasanta, T., Garcýa-Ruiz, J. M., Perez-Rontome, C. and Sancho-Marcen, C.: 2000, ‘Runoff and sediment yield in a semi-arid environment: The effect of land management after farmland abandonment’, Catena 38, 265—278.CrossRefGoogle Scholar
  20. Luijten, J., Jones, J. and Knapp, E.: 2000, ‘Dynamic modeling of strategic water availability in the Cabuyal River, Colombia: The impact of land cover change on the hydrological balance’, Adv. Environ. Monit. Modell. 1, 36—60.Google Scholar
  21. Neitsch, S., Arnold, J., Kiniry, J. and Williams, J.: 2000, Soil and Water Assessment Toll Theoretical Documentation 2000, Grassland, Soil and Water Research Laboratory, Agricultural Research Service, 808 East Blackland Road, Temple, Texas 76502, pp. 506.Google Scholar
  22. Nicks, A. D., Lane, L. J. and Gander, G. A.: 1995, ‘Weather Generator’, in D. C. Flanagan and M. A. Nearing (eds), USDA — Water Erosion Prediction Project Hillslope Profile and Watershed Model Documentation, NSERL Report No. 10, West Lafayette, Indiana, pp. 2.1—2.22.Google Scholar
  23. Moorman, T. B., Jaynes, D. B., Cambardella, C. A., Hatfield, J. L., Pfeiffer, R. L. and Morrow, A. J.: 1999, ‘Water quality in Walnut Creek watershed: Herbicides in soils, subsurface drainage, and groundwater’, J. Environ. Qual. 28, 35—45.Google Scholar
  24. Prueger, J. H., Hatfield, J. L. and Sauer, T. J.: 1998, ‘Field-scale metolachlor volatilization flux estimates from broadcast and banded application methods in central Iowa’, J. Environ. Qual. 28, 75—81.Google Scholar
  25. Saleh, A., Arnold, J., Gasman, P., Hauck, L., Rosenthal, W., Williams, J. and McFarland, A.: 2000, ‘Application of SWAT for the upper north Bosque river watershed', Transactions ASAE 43, 1077—1087.Google Scholar
  26. Soil Conservation Service: 1984, Soil Survey of Story County, Iowa, USDA-SCS, Washington D.C.Google Scholar
  27. Takken, I., Jetten, V., Govers, G., Nachtergaele, J. and Steegen, A.: 2001, ‘The effect of tillageinduced roughness on runoff and erosion patterns’, Geomorphology 37, 1—14.CrossRefGoogle Scholar
  28. USDA-NASS: 1995, Agricultural Chemical Usage 1994 Field Crops Summary, National Agric. Statistics Service, USDA, Washington D.C.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • V. Chaplot
    • 1
  • A. Saleh
    • 2
  • D. B. Jaynes
    • 3
  • J. Arnold
    • 4
  1. 1.IRD — Ambassade de FranceVientianeLaos
  2. 2.TIAER, Tarleton State UniversityStephenvilleU.S.A.
  3. 3.USDA-ARS, National Soil Tilth Lab.AmesU.S.A.
  4. 4.USDA-ARSTempleU.S.A.

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