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Distributed Large Basin Runoff Model
Other Distributed Agricultural Runoff Models
Estimating point and non-point source pollutions and CSOs is critical to planning and enforcement agencies in protection of surface water and groundwater quality. During the past four decades, a number of simulation models have been developed to aid in the understanding and management of surface runoff, sediment, nutrient leaching, and pollutant transport processes. The widely used water quality models include ANSWERS (Areal Non-point Source Watershed Environment Simulation) (Beasley and Huggins 1980), CREAMS (Chemicals, Runoff and Erosion from Agricultural Management Systems website) (Knisel 1980), GLEAMS (Groundwater Loading Effects of Agricultural Management Systems website) (Leonard et al.1987), AGNPS (Agricultural Non-point Source Pollution Model website) (Young et al. 1989), EPIC (Erosion Productivity Impact Calculator website) (Sharpley and Williams 1990), and SWAT (Soil and Water Assessment Too websitel) (Arnold et al. 1998), to name a few.
These models all use the SCS Curve Number method, an empirical formula for predicting runoff from daily rainfall. Although the Curve Number method has been
widely used worldwide, researchers have expressed concern that it does not reproduce measured runoff from specific storm rainfall events because the time distribution is not considered (Kawkins 1978; Wischmeier and Smith 1978; Beven 2000; Garen and Moore 2005). Limitations of the Curve Number method also include no explicit account of the effect of the antecedent moisture conditions in runoff computation, difficulties in separating storm runoff from the total discharge hydrograph, and runoff processes not considered by the empirical formula (Beven 2000; Garen and Moore 2005).
Consequently, estimates of runoff and infiltration derived from the Curve Number method may not well represent the actual. As sediment, nutrient, and pesticide loadings are directly related to infiltration and runoff, use of the Curve Number method may also result in incorrect estimates of non-point source pollution rates.
Considering the limitations of the Curve Number method, ANSWERS, CREAMS, GLEAMS, AGNPS, and SWAT were developed to assess impacts of different agricultural management practices, not to predict exact pesticide, nutrient, and sediment loading in a study area (Ghadiri and Rose 1992; Beven 2000; Garen and Moore 2005). In addition, most water quality models, such as CREAMS and GLEAMS, are field-size models and cannot be used directly at the watershed scale. Applications of these models have been limited to field scale or small experimental watersheds. Some models, e.g. ANSWERS, CREAMS, EPIC, and AGNPS, also do not consider subsurface and groundwater processes.
Recently, several water quality models have been modified to take into consideration available multiple physical and agricultural databases. The US EPA designated two of the most widely used water quality models, SWAT and HSPF (Hydrologic Simulation Program in FORTRAN website ) (Bicknell et al. 1996), for simulation of hydrology and water quality nationwide. SWAT is a comprehensive watershed model and considers runoff production, percolation, evapotranspiration, snowmelt, channel and reservoir routing, lateral subsurface flow, groundwater flow, sediment yield, crop growth, nitrogen and phosphorous, and pesticides. But it uses the Curve Number method for estimating runoff and therefore has those same limitations the Curve Number method has in runoff simulation. The basic simulation unit in SWAT is the sub-watershed, instead of a grid network, thus limiting its incorporation of spatial variability in simulating hydrologic processes. Evolved from the Stanford Watershed Model (Crawford and Linsey 1966), HSPF is one of the most extensively used general hydrologic and water quality models (Bicknell et al. 1996). Under the auspices of the US EPA, the first version of the HSPF was completed in 1980. Since then, the model has gone through extensive revisions, corrections, refinements, and validations in many areas and is one of the three simulation models included in BASINS (Better Assessment Science Integrating Point and Non-point Sources website ), the US EPA’s watershed modeling tools for support of water quality management programs throughout the country (Lahlou et al. 1998). HSPF utilizes time series meteorology data to simulate hydrological processes in both pervious and impervious land segments. The hydrological processes in the model include accumulation and melting of snow and ice, water budget, sediment transport, soil moisture and temperature. The water quality modules of the model include concentration and transport of nitrogen, phosphorus, pesticides, and other pollutants. However, HSPF requires extensive input parameters such as wind speed, dew point temperature,
potential evapotranspiration, and channel characteristics. Many of these parameters are not available in most watersheds, particularly large watersheds. In addition, HSPF is basically a lumped parameter model and thus lacks consideration of spatial variability of hydrological processes. Moreover, neither SWAT nor HSPF considers non-point sources from animal manure and CSOs and infectious diseases.
References
Arnold, G., Srinavasan, R., Muttiah, R.S., and Williams, J.R. (1998), ‘Large Area Hydrologic Modeling and Assessment. Part I. Model Development,’ Journal of the American Water Resources Association, vol. 34, pp. 73-89.
Beasley, D.B., and Huggins, L.F. (1980), ‘ANSWERS (Areal Nonpoint Source Watershed Environment Simulation) - User's Manual,’ Department of Agricultural Engineering, Purdue University, West Lafayette, Indiana.
Beven, K.J. (2000), Rainfall-Runoff Modeling: The Primer , John Wiley & Sons, Ltd., New York, New York.
Bicknell, B.R., Imhoff, J.C., Kittle, J., Donigian, A.S., and Johansen, R.C. (1996), ‘Hydrological Simulation Program—FORTRAN, User’s Manual for Release 11,’ U.S. Environmental Protection Agency, Environmental Research Laboratory, Athens, Georgia.
Crawford , N.H., and Linsley, R.K. (1966), ‘Digital Simulation in Hydrology: Stanford Watershed Model IV,’ Technical Report 39, Dept. of Civil Engineering, Stanford University, California.
Garen , D.C. , and Moore, D.S. (2005), ‘Curve Number Hydrology in Water Quality Modeling: Uses, Abuses, and Future Directions,’ Journal of the American Water Resources Association, vol. 41, pp. 377-388.
Ghadiri, H., and Rose, C.W. (1992), Modeling Chemical Transport in Soils: Natural and Applied Contaminants, Lewis Publishers, Ann Arbor, Michigan.
Kawkins, R.H. (1978), ‘Runoff Curve Number Relationships with Varying Site Moisture,’ Journal of the Irrigation and Drainage Division, vol. 104, pp. 389-398.
Knisel, W.G. (1980), ‘CREAMS: A Fieldscale Model for Chemical, Runoff, and Erosion from Agricultural Management Systems,’ USDA, Science and Education Administration, Conservation Report No. 26, Washington, D.C.
Lahlou, N., Shoemaker, L., Choudhury, S., Elmer, R., Hu, A., Manguerra, H., and Parker, A. (1998), ‘BASINS V.2.0 User’s Manual,’ U.S. Environmental Protection Agency Office of Water, EPA-823-B-98-006, Washington, D.C.
Leonard, R.A. Knisel, W.G., and Still, D.A. (1987), ‘GLEAMS: Groundwater Loading Effects of Agricultural Management Systems,’ Transactions of ASAE, vol. 30, pp. 1403-1418.
Sharpley, A.N., and Williams, J.R. (1990) ‘EPIC-Erosion/Productivity Impact Calculator,’ USDA, Agricultural Research Service, Technical Bulletin No. 1768, Washington, D.C., pp. 235 pp.
Young, R.A., Onstad, C.A., Bosch, D.D., and Anderson, W.P. (1989), ‘AGNPS: A Non-Point-Source Pollution Model for Evaluating Agricultural Watersheds,’ Journal of Soil and Water Conservation, vol. 44, pp. 168:173.
Last updated: 2006-09-08ks