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Representing Management Practices in GLEAMS
Eur. J. Agron., 1995, 4(4), 499-505
Representing Management Practices in GLEAMS W. G. Knisel* 1, R. A. Leonard 2 and F. M. Davis
2
Biological and Agricultural Engineering Department University of Georgia, Coastal Plain Experiment Station Tifton, Georgia 31793 USA 2 U.S. Department of Agriculture, Agricultural Research Service Southeast Watershed Research Lab, Tifton, Georgia 31793 USA 1
Accepted: 24 May 1995
* To whom correspondence should be addressed. Abstract
Agricultural and forestry management practices may adversely affect surface and groundwater quality. The GLEAMS model was developed to assess management effects on edge-of-field and bottom-of-root-zone loadings of water, sediment, and chemicals. Management systems may affect one or more of the four model components : hydrology, erosion, pesticides, and plant nutrients. Each component is briefly described, and parameter sensitivity is discussed. The effects of changing man agement practices are soil and climate dependent, but generalized effects are presented. Effects of crop rotation, terracing, irrigation, and tillage practices on hydrologic response, and their impact on erosion, pesticide fate, and plant nutrient losses are summarized in a table. Effects of timing and application methods for pesticides, inorganic fertilizer, and animal waste are also discussed. Key-words : modelling, management practices, hydrology, erosion, sedimentation, pesticides, plant nutrients, nitrogen cycle, phosphorus cycle.
INTRODUCTION The CREAMS model (Chemicals, Runoff, and Ero sion from Agricultural Management Systems), was developed to estimate edge-of-field surface loadings of water, sediment, and chemicals (Knisel, 1980). Pesti cide detection in groundwater in the early 1980' s resulted in additional requirements for assessing non point source pollution from agriculture, and GLEAMS (Groundwater Loading Effects of Agricultural Man agement Systems) was developed as an extension of CREAMS to consider bottom-of-root-zone loadings (Leonard et al., 1987). The improvements built into GLEAMS consisted mainly of : ( 1) integrated model components in a single computer programme that allows component interactions ; (2) improved soil rep resentation ; (3) a pesticide component to consider ver tical flux of pesticides ; (4) a more comprehensive plant nutrient component ; (5) better representation of management practices; and (6) a more user-oriented model (Knisel, 1993). The daily-simulation model for field-size areas was developed to simulate climate soil-management interactions in a layered soil. Long term simulation allows comparisons of variable cli ISSN !161-0301195104/$ 4.001© Gauthier-Villars - ESAg
matic conditions. Management alternatives include crop rotations, tillage, mechanical practices such as terracing, pesticide selection, animal waste application practices, irrigation, and others. Management systems can have significant effects on non-point pollutant loads delivered to off-site water bodies and to satu rated zones. The purpose of this paper is to describe the representation of management practices in the GLEAMS model, and to give insight into what effects the practices have on pollutant loads. MODEL DESCRIPTION The GLEAMS model consists of four components operating simultaneously : hydrology, erosion/sediment yield, pesticides, and plant nutrients. The model has been described in detail elsewhere (Leonard et al., 1987 ; Knisel, 1993), and only a brief presentation is made here. Hydrology Daily water accounting is simulated in a soil system layered within the soil horizons of the root zone. The
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model distributes soil characteristics into a maximum of 12 computational layers with input from a maxi mum of 5 soil horizons. Daily potential evapotranspi ration is estimated by the Priestley-Taylor or Penman Monteith methods (Knisel, 1993). The uptake of soil water by a crop is simulated as a two-stage process with ET occurring at potential when water content is greater than 25 per cent plant available. Runoff is cal culated using a modified Soil Conservation Service curve number procedure (Williams and LaSeur, 1976). Curve numbers represent hydrologic soil group and management practice (USDA-SCS, 1972). The modifi cation by Williams and LaSeur (1976) mainly con sisted of replacing the 5-day antecedent rainfall with available soil water storage, and making the procedure a daily simulation rather than a design-type storm. Percolation through the soil layers uses a storage routing technique. Irrigation is based upon user specified threshold soil water content in the rooting depth, with an upper limit of application specified for possible water-deficit management. Daily rainfall data are required input and mean daily temperature data are optional for the hydrology component (Knisel, 1993). There are 67 codes and parameters in the hydrology component including mean monthly maximum, minimum, and dew point temperature, and mean monthly solar radiation and wind movement. Idealized leaf area data are included in the model for 78 crops with options for user supplied data. Only four parameters are sensitive for water balance calculations : curve number, soil poros ity, field capacity, effective root depth. Saturated con ductivity is sensitive only for heavy-texture or slowly permeable soils. Other parameters in the hydrology component which are sensitive in other components include : slope, length :width ratio, and clay, silt, and organic matter content of the soil. Soil pH, base satu ration, and calcium carbonate content are included as part of the hydrology input and are optional, depend ing upon the soil type and whether or not the plant nutrient component is run. Erosion The erosion component of GLEAMS is the Onstad Foster (Onstad and Foster, 1975) modification of the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). Rill and interrill erosion are calcu lated on the non-uniform slope of an overland flow element of the field. The erosivity (R) factor of the USLE is replaced by storm-by-storm rainfall energy calculated from daily rainfall. The management aspect of the USLE, i.e. soil loss ratio (C factor) and practice (P) factor, is maintained. In addition to a single repre sentative overland flow element, concentrated or chan nel flow can be represented in the field. A channel channel sequence, such as a representative terrace channel draining into a terrace outlet channel, can be
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designated to simulate a terrace system. A pond ele ment can also be included to represent an impound ment that drains shortly after runoff ends, such as an impoundment-type terrace, gully plug, or debris basin. Soil particles and organic matter detached by raindrop impact are routed through the delivery sequence of the field (Foster et al., 1985). A characteristic discharge, calculated from the storm runoff peak rate simulated at the field outlet in the hydrology component, is used to calculate sediment transport. The characteristic dis charge is translated back to the uppermost element and is used to calculate sediment transport capacity and deposition of each computational segment. Sedi ment yield and the associated sediment enrichment ratio (ratio of the specific surface area of the sediment to the specific surface area of the original soil) is cal culated at the end of each flow element and the outlet of the field (Foster et al., 1980). There are 45 user-supplied codes and parameters in the erosion component for the most complex applica tion. Some of the parameters are updateable, that is they change with crop growth and tillage. Parameter sensitivity is very site-specific for the erosion compo nent dependent upon the overland flow profile shape and the flow sequence (if there is a channel or pond element). Overland flow length and slope, flow resis tance, cover factor, and practice factor may be sensi tive in the overland flow element. Channel slope and channel flow resistance may be sensitive in the chan nel element. The pond pipe diameter and stage-volume relationships are sensitive parameters for the pond ele ment. Sensitivity is dependent upon whether the sys tem is transport-limiting or detachment-limiting. Pesticides The pesticide component of GLEAMS incorporates the surface pesticide response of CREAMS (Leonard and Wauchope, 1980) with a vertical flux component to route pesticides into, within, and through the root zone (Leonard et al., 1987). Characteristics of pesti cide adsorption to soil organic carbon are used to par tition compounds between solution and soil fractions for simulating extraction into runoff, sediment, and percolation losses. Pesticide dissipation in soil and on foliage is treated as a first-order process with a differ ent half-life for each fraction. Degradation products. (metabolites) can be considered when their character istics are known. Up to 10 pesticides can be simulated simultaneously, including metabolites. A data base is available in the parameter editor with pesticide charac teristics including water solubility, adsorptivity, aver age soil half-life, foliar half-life, and foliar washoff fraction (Knisel, 1993). Material washed from plants is added to the mass in the surface 1 cm of soil and the concentration adjusted for infiltration and runoff. Pesticides may be applied on the soil surface, incorpo rated in the soil, injected, applied on soil and foliage, Eur. J. Agron.
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Representing Management Practices in GLEAMS
or applied in irrigation water. Multiple applications can be made for any compound during a cropping period. Pesticide losses are simulated in runoff, with sediment, and in percolate at the bottom of the root zone. The product of sediment yield and enrichment ratio is used to calculate the sediment portion of the losses. Codes and parameters in the pesticide component total 27 including 5 pesticide characteristics. The pes ticide parameter editor contains a data base with char acteristics for about 250 compounds. The pesticide partitioning coefficient and soil half-life are generally sensitive for runoff, sediment, and percolation losses of pesticides. Low water solubilities may limit the availability of a compound and may be sensitive. Foliar characteristics, half-life and wash-off fraction may be sensitive for some soil and climatic regions. Depths of pesticide incorporation or injection and amount of irrigation water containing pesticides applied may be sensitive for leaching losses on light texture soils. Plant Nutrients
The plant nutrient component of GLEAMS consid ers nitrogen and phosphorus (Knisel, 1993). Compre hensive nitrogen and phosphorus cycles are treated in the model. Much of the nutrient component is very similar to the EPIC model (Sharpley and Williams, 1990), except the animal waste component. The nitro gen cycle includes : mineralization, immobilization, denitrification, ammonia volatilization, nitrogen fixa tion by legumes, fertilizer and animal waste applica tion, crop uptake, and runoff, sediment, and leaching losses. Mineralization is treated as a two-step process : first-order ammonification, and zero-order nitrification. The ammonification is consistent with animal waste loadings and ammonia volatilization. The phosphorus cycle includes : mineralization, immobilization, fertil izer and animal waste application, crop uptake, and runoff, sediment, and leaching losses. Inorganic fertil izer application considers surface application, incorpo ration, and fertigation. Animal waste application, with specification of nutrient content, may be represented as surface, incorporation, injection, or liquid such as lagoon effluent. Organic fractions of animal waste N and P are maintained as separate fractions that miner alize with different rate constants from those for fresh organic N and P in crop residue or mineralizable soil N and P. Tillage and soil temperature algorithms are included in the nutrient component. The tillage com ponent allows incorporation of crop residue, animal waste, and fertilizer, and the mixing of the respective pools in the ploughed layers. Ammonification, nitrifi cation, denitrification, volatilization, and mineraliza tion rates are adjusted by soil temperature and water content in the respective computational soil layers. Rainfall nitrogen is an input for the model application Vol. 4, n° 4 - 1995
site, and N and P in irrigation water can be considered for locations where concentrations in the water supply may be significant. A total of 51 codes and parameters are included in the plant nutrient component, of which 16 are crop, tillage, and animal waste characteristics that are inter nally defined or can be user supplied. The initial con tent of 7 nutrient pools that can be model-calculated or user-defined may be sensitive in simulation results, especially for short-term climatic records. Fertilizer placement and animal waste characteristics and place ment are sensitive in nutrient losses. Tillage type and depth may be sensitive in determining surface or leaching losses. Soil chemical characteristics (input in the hydrology component) are sensitive for phospho rus mineralization and sorption. Overall sensitivity is a function of soil and climatic region. Management
GLEAMS was developed to compare the responses of alternate management practices to long-term cli mate. Therefore, the model must be capable of repre senting a wide range of management practices. The SCS curve number procedure for estimating runoff ranks a number of practices by giving a curve number within each hydrologic soil group (USDA-SCS, 1972). In its original application for design type storm, curve numbers are listed for row crops, small grain, pasture, legumes, etc. Within each of the crop groups, curve numbers are given for conservation tillage, straight rows, contour tillage, and terraces. Good and poor hydrologic condition further categorized receptiveness of the soil for rainfall infiltration with different curve numbers. Thus, the modified method represents man agement in the continuous runoff simulation procedure (Williams and LaSeur, 1976). The Universal Soil Loss Equation (USLE) (Wisch meier and Smith, 1978) represents different tillage types, crops, cropping systems, surface residue, and conditions with different cropping factors (C-factors). Practice factors (P-factors) were included in the USLE to represent straight rows up-and-down-hill and con touring. The C and P factor concepts were retained by Onstad and Foster (197 5) in their modification of the USLE for storm-by-storm application in the CREAMS model (Knisel, 1980). Terraces are mechanical prac tices designed to break long, steep slopes and result in low velocity (low sediment transport capacity) along the terrace channels and vegetated terrace outlets. Tile outlet terraces with a perforated pipe riser in a tempo rary pond allow coarse-grain sediment to deposit in the ponded runoff where velocity and transport capac ity is practically zero. Surface residue is a function of production level and tillage type and degree, and it in tum affects the C-factor (designated as Soil Loss Ratio in GLEAMS). Several management practices are listed
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in Table 1 with respective changes in each component and the comparative results. There are numerous man agement systems not included in the table, but these are the ones easiest to depict with their results. It should be pointed out that no management prac tices are without limitations. For example, vegetated buffer strips and vegetated channels require significant maintenance to prevent erosion. Sediment deposition where runoff enters the buffer strip or channel will result in deltas that cause meandering flow or flow lat erally along the vegetated strip in subsequent storms. Spreading the sediment deposits is necessary to pre vent erosional damage that the resource management systems were to control initially.
Selection of pesticides for specific soils and crop ping systems can be made with the GLEAMS model based upon pesticide characteristics (Leonard et al., 1989), but this chemical comparison cannot be readily categorized in Table 1. For example, there may be an array of pesticides that can be used in a rotation for a specific pest, insect or weed. If adequate control can be achieved with alternate pesticides, selection can be made to minimize runoff losses (low K0 c and/or short half-life) or to minimize percolation losses (high K 0 c and short half-life). Strip cropping is not included in the practices of Table 1, but is an important erosion-control practice for some rotations in some soil and climatic regions. The erosion component is capable of simulating up to four crops (strips) along the overland flow profile. A single crop is assumed in hydrology, but a composite leaf area index can be input to represent the seasonal differences in such crops as winter small grain and a summer row crop. A curve number for small grain would be used to calculate the maximum available storage. The rotation feature in erosion allows the year-to-year alternate cropping by segment. Thus, the hydrology and erosion can give a reasonable approxi mation of the runoff and sediment yield from a strip crop system. However, the pesticide and plant nutrient components could not represent applications of chemi cals realistically on alternate strips. The broadcast equivalent representation would be less than satisfac tory for strip cropping since agrichemicals would be applied at different times for winter small grain and summer row crops. If the soil and climatic region is one in which runoff constitutes the major losses with negligible percolation losses, 'then broadcast equiva lent' might produce reasonable results. If significant runoff and percolation occur, results would not be valid. Animal waste application cited in Table 1 does not consider time of application, which may be critical in some soil-climatic regions. Animal waste applications for crop productivity must be made well before peak crop uptake in order for mineralization of nitrogen and phosphorus to occur. If waste application is made when the soil is frozen and there is a snowpack,
excess nutrient losses can occur with snowmelt runoff and percolation. Time and method of application rela tive to planting are important, and these can be con sidered as management alternatives with GLEAMS simulations. These aspects are not a part of the prac tices listed in Table 1. Crop rotations including multiple cropping, that is growing more than one crop during a year, represent intensive management that can be considered in GLEAMS. Vegetable crops are grown in rotation with field crops in some climatic regions. Most vegetable crops generally do not have root systems as deep as those for field crops. GLEAMS model users can input the root depth of individual crops, and water and chemical uptake is not considered below the current crop rooting depth (CCRD). For example, the effective root depth may be 60 cm which would be the depth for a field crop, but a vegetable crop may root only in the plow layer, or about 25 cm. If CCRD for the veg etable crop is input as 25 cm, there would be no root growth or water and chemical uptake below the 25-cm depth. The soil water content would remain at (or above) field capacity from 25 to 60 cm, and any per colation of water and chemicals below 25 cm would be re-equilibrated with the equivalent field capacity water content on a daily basis. Chemical transforma tions are simulated daily in each computational soil layer between 25 and 60 cm to approximate any mass percolate below the 60-cm depth.
MODEL APPLICATIONS The GLEAMS model is used in about 40 countries, mainly in North America and Europe. Over 1,000 cop ies have been distributed for use in validation, evalua tion, and assessment applications by environmental quality planners and regulators, consultants, research ers, and scientists. GLEAMS is widely used in the USDA-Natural Resources Conservation Service (formerly Soil Con servation Service) to evaluate management alterna tives. Reck (1994) evaluated nitrate leaching losses from alternate management practices on sandy soils. Management strategies were analysed for large-scale dairy operations in an area with a vulnerable shallow aquifer. Geter et al. ( 1992) used GLEAMS to develop a pesticide screening procedure for national applica tion in USDA-SCS. The US Environmental Protection Agency, Office of Pesticide Programs, recommended GLEAMS as one of the two preferred field-scale models for use by agrochemical companies to provide supporting infor mation for pesticide registration. It is used by State agencies for simulations to consider pesticide registra tion in individual states within the US. These recom mendations have resulted in GLEAMS model validaEur. J. Agron.
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Representing Management Practices in GLEAMS
Table 1.
Checklist of parameter changes in the GLEAMS model for selected management practices.
Practice
Model Component
Results
1
Hydrology
Erosion
Pesticides
Plant Nutrients
Conservation tillage
Lower curve Number (CN2)
Lower soil loss ratio (C-factor, overland flow element)
None
Tillage incorporates and mixes nutrient pools
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Contour tillage
Lower CN2
Lower P-factor (overland flow element)
None
None
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Terracing
Lower CN2 Longer flow path (Lower CHS, higher WLW)
flow seq. overland length P-factor
None
None
Runoff Runoff peak rate Percolation + Sediment yield
Impoundment (Sediment basin)
None
Change flow seq. Pond characeristics
None
None
Runoff = Runoff peak rate Percolation = Sediment yield Sur pst & nut Pere pst & nut
Small grain or meadow in rotation
Lower CN2
Lower C-factor (overland flow element)
None
None
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Vegetated buffer strip
None
Lower C-factor on lowest overland flow segment Higher Manning 'n' on lowest segment
Lower broadcast equivalent application rate
Lower unit fertilizer application rate
Runoff = Percolation = Sediment yield Sur pst & nut Pere pst & nut
Vegetated channel
None
Higher Manning 'n' in channel
None
None
Sediment yield Sur pst & nut -
Animal waste incorporated
None
None
None
Tillage, incorporation and mixing
Sur nutrients Pere nutrients + NH4 volatilization
Split fertilizer applications
None
None
None
Lower fertilizer rate each application
Nutrient uptake + Sur nutrients Pere nutrients
Irrigation
Water deficit management
None
None
None
Soil water + Runoff 2 Percolation 2 Sediment yield 2 Sur pst & nut 2 Pere pst & nut 2
Pesticide and fertilizer application with irrigation
None
None
CHMWAT (water depth)
FRTWAT (water depth)
Soil water + Runoff 2 Percolation 2 Sediment yield 2 Sur pst & nut 2 Pere pst & nut 2
Banding pesticides
None
None
Lower broadcast equivalent application rate
None
Sur pesticide Pere pesticide
Change Shorter flow Lower
1 Abbreviations : Sur = Surface ; Pere = Percolation ; pst = pesticide ; nut Symbols : - less ; + more ; = same ; 2:' same or more ; ::::; same or less.
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= nutrients
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tions and evaluations nationally and internationally (Shirmohammadi and Knisel, 1994 ; Smith et al., 1991 ; Zacharias and Heatwole, 1994). Researchers have aided in validation of the GLEAMS model or evaluation of some of its compo nents. Dixon et al. (1993) compared leaching charac teristics of banded and broadcast tracers to test and verify the concept of using 'broadcast- equivalent' representation of chemical application. Salo et al. (1994) evaluated pesticide flux and concentrations in clay and loamy sand soils in Finland. They found that GLEAMS over-predicted pesticide dissipation after the first few weeks following application under their tem perature regime. Knisel (1993) showed very good comparisons for nitrate leaching for two poultry litter application rates (management practices) when site-specific soil and waste characteristic data were used. Yoon et al. (1994) found that GLEAMS simulation results did not com pare very well with field observations of nutrient losses for poultry litter application when model default values were used. Their study points out the need to use local data for validation and parameter adjustment where possible, as recommended by the model devel opers. GLEAMS has been used to select 'environmentally safe' herbicide application windows within broader windows of vegetation control for forest site prepara tion and production (Smith et al., 1993 ; Smith et al., 1994). Long-term climatic records were used to exam ine year-to-year differences in herbicide runoff and leaching losses for periods up to six months recom mended by manufacturers for best vegetation control. Leonard et al. (1989) proposed using GLEAMS as a screening tool to group pesticide and soil character istics based upon surface and leaching losses. Geter et al. (1992) extended the concepts in a two-tier screen ing procedure for use by the USDA-SCS. Both of these ideas used specific soils and long-term climatic records. Goss (1992) developed a more generalized screening procedure that used the GLEAMS model with generalized soil characteristics and short-term synthetic rainfall distribution. GLEAMS has been successfully linked with other models and procedures also. Chung et al. (1992) com bined the pesticide and erosion components of GLEAMS with the hydrologic component of DRAIN MOD to form the ADAPT model (Agricultural Drain age And Pesticide Transport). ADAPT was evaluated for water table management in heavy clay soils. Shir mohammadi et al. (1989) used GLEAMS output from the bottom of the root zone as input to the vadose zone. Their work allowed analyses of further pesticide degradation and transport to shallow groundwater sys tems. There are several efforts underway to link GLEAMS with geographical information systems (GIS) to manage model parameters, data bases, and
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output. Searing and Shirmohammadi (1993) used GIS and GLEAMS to manipulate model parameters and organize output for generating best management prac tices in a drainage basin. Tucker et al. (1994) devel oped procedures for using GIS and text file conversion programmes to develop hydrology parameter files for GLEAMS and a riparian zone model. There are numerous other similar applications, but these are cited merely as examples. This brief description of GLEAMS applications indicate the widespread uses of the model. The refer ences provide additional information on the model.
REFERENCES Chung S. 0., Ward A. D. and Schalk C. W. (1992). Evaluation of the hydrologic component of the ADAPT water table man agement model. Trans. ASAE 35(2): 571- 579. Dixon K. L., Smith M. C., Thomas D. L. and Knisel W. G. (1993). Leaching characteristics of banded and broadcast inorganic tracers. Trans. ASAE 36(6) : 1779- 1788. Foster G. R., Lane L. J., Nowlin J. D., Laflen J. M. and Young R. A. (1980). A model to estimate sediment yield from field sized areas : Development of model. In : Knisel W.G. (Ed.) CREAMS : A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. U.S. Dept. of Agric., Conservation Research Report No. 26. pp. 36-64. Foster G. R., Young R. A. and Neibling W. H. (1985). Sediment composition for nonpoint source pollution analyses. Trans. ASAE 28(1): 133-139, 146. Geter W. F., Plotkin S., Bagdon J. K. and Hesketh E. S. (1992). The national agricultural pesticide risk assessment. 1992 Summer Meeting of the ASAE, Charlotte, North Carolina, June 1992. Paper No. 92-2039, 6 pp. Goss D. (1992). Screening procedure for soils and pesticides for potential water quality impacts. Weed Technol. 6 :701-708. Knisel W. G. (Ed.) (1980). CREAMS: A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Manage ment Systems. U.S. Dept. of Agric., Conservation Research Report No. 26, 643 pp. Knisel W. G. (Ed.) (1993). GLEAMS: Groundwater Loading Effects of Agricultural Management Systems, Version 2.10. Biological and Agricultural Engineering Department, Univer sity of Georgia, Coastal Plain Experiment Station, Tifton. BAED Pub!. No. 5,. 260 pp. Leonard R. A. and Wauchope R. D. (1980). The pesticide sub mode!. In : Knisel W.G. (Ed.) CREAMS: A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Man agement Systems, U.S. Dept. of Agric., Conservation Research Report No. 26, pp. 88-112. Leonard R. A., Knisel W. G. and Still D. A. (1987). GLEAMS : Groundwater Loading Effects of Agricultural Management Systems. Trans. ASAE 30(5): 1403-1418. Leonard R. A., Perkins H. F. and Knisel W. G. (1989). Relating agrichemical runoff and leaching to soil taxonomy : A GLEAMS model analyses. Proc. of the 1989 Georgia Water Resources Conference, Athens, Georgia, May 16-17, 1989, pp. 158-160. Onstad C. A. and Foster G. R. (1975). Erosion modeling on a watershed. Trans. ASAE 18(2) : 288-292. Reck W. R. (1994). GLEAMS modeling of BMPs to reduce nitrate leaching in Middle Suwanee River Area. Proc. of the Eur. ], Agron.
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Second Conference on Environmentally Sound Agriculture, April 20-22, 1994, Orlando, Florida, pp. 361-367. Salo S., Posch M. and Rekolainen S. (1994). Testing the modi fied CREAMS/GLEAMS model for pesticide concentration in soil. Agric.Sci. Finland, 3 : 59-68. Searing M. L. and Shirmohammadi A. (1993). Utilizing ors and GLEAMS to prescribe best management practices for reducing nonpoint source pollution. 1993 Summer Meeting of the ASAE. Paper No. 93-3558. Sharpley A. N. and Williams J. R. (Eds.). (1990). EPIC Erosion/Productivity Impact Calculator : 1. Model Documen tation. U.S. Dept. of Agric., Technical Bulletin No. 1768, 23 pp. Shirmohammadi A. and Knisel W. G. (1994). Evaluation of the GLEAMS model for pesticide leaching in Sweden. J. Envi ron. Sci. Health, Part A-Environmental Sci. and Eng. A29(6): 1167-1182. Shirmohammadi A., Gish T. J., Lehman D. E. and Magette W. L. (1989). GLEAMS and the vadose zone modeling of pesti cide transport. 1989 Summer Meeting of the ASAE, Paper No. 89-2071. 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. ASAE 34(3): 838-847. Smith M. C., Knisel W. G., Michael J. L. and Neary D. G. (1993). Simulating effects of forest management practices on pesticide losses with GLEAMS. Proc. of the International
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Runoff and Sediment Yield Modeling Symposium, September 1993, Warsaw, Poland, pp. 157-162. Smith M. C., Michael J. L., Knisel W. G. and Neary D. G. (1994). Using GLEAMS to select environmental windows for herbicide application in forests. Proc. of the Second Confer ence on Environmentally Sound Agriculture, April 20-22, 1994, Orlando, Florida, pp. 506-512. Tucker M. A., Thomas D. L., Altier L. S. and Bosch D. D. (1994). ors interfaced with field and riparian zone models. 1994 Summer Meeting of the ASAE, Kansas City, Missouri. Paper No. 94-2151, 13 pp. U.S. Dept. of Agric., Soil Conservation Service. (1972). SCS National Engineering Handbook, Section 4, Hydrology, 548 pp. Williams J. R. and LaSeur W. V. (1976). Water yield model using SCS curve numbers. J. Hydraul. Div. ASCE 102(HY9) : 1241-1253. Wischmeier W. H. and Smith D. D. (1978). Predicting rainfall erosion losses. U.S. Dept. of Agric., Agriculture Handbook No. 537, 58 pp. 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. ASAE 37(2) :453-459. Zacharias S. and Heatwole C. D. (1994). Evaluation of GLEAMS and PRZM for predicting pesticide leaching under field conditions. Trans. ASAE 37(2): 437-451.
Representing Management Practices in GLEAMS W. G. Knisel* 1, R. A. Leonard 2 and F. M. Davis
2
Biological and Agricultural Engineering Department University of Georgia, Coastal Plain Experiment Station Tifton, Georgia 31793 USA 2 U.S. Department of Agriculture, Agricultural Research Service Southeast Watershed Research Lab, Tifton, Georgia 31793 USA 1
Accepted: 24 May 1995
* To whom correspondence should be addressed. Abstract
Agricultural and forestry management practices may adversely affect surface and groundwater quality. The GLEAMS model was developed to assess management effects on edge-of-field and bottom-of-root-zone loadings of water, sediment, and chemicals. Management systems may affect one or more of the four model components : hydrology, erosion, pesticides, and plant nutrients. Each component is briefly described, and parameter sensitivity is discussed. The effects of changing man agement practices are soil and climate dependent, but generalized effects are presented. Effects of crop rotation, terracing, irrigation, and tillage practices on hydrologic response, and their impact on erosion, pesticide fate, and plant nutrient losses are summarized in a table. Effects of timing and application methods for pesticides, inorganic fertilizer, and animal waste are also discussed. Key-words : modelling, management practices, hydrology, erosion, sedimentation, pesticides, plant nutrients, nitrogen cycle, phosphorus cycle.
INTRODUCTION The CREAMS model (Chemicals, Runoff, and Ero sion from Agricultural Management Systems), was developed to estimate edge-of-field surface loadings of water, sediment, and chemicals (Knisel, 1980). Pesti cide detection in groundwater in the early 1980' s resulted in additional requirements for assessing non point source pollution from agriculture, and GLEAMS (Groundwater Loading Effects of Agricultural Man agement Systems) was developed as an extension of CREAMS to consider bottom-of-root-zone loadings (Leonard et al., 1987). The improvements built into GLEAMS consisted mainly of : ( 1) integrated model components in a single computer programme that allows component interactions ; (2) improved soil rep resentation ; (3) a pesticide component to consider ver tical flux of pesticides ; (4) a more comprehensive plant nutrient component ; (5) better representation of management practices; and (6) a more user-oriented model (Knisel, 1993). The daily-simulation model for field-size areas was developed to simulate climate soil-management interactions in a layered soil. Long term simulation allows comparisons of variable cli ISSN !161-0301195104/$ 4.001© Gauthier-Villars - ESAg
matic conditions. Management alternatives include crop rotations, tillage, mechanical practices such as terracing, pesticide selection, animal waste application practices, irrigation, and others. Management systems can have significant effects on non-point pollutant loads delivered to off-site water bodies and to satu rated zones. The purpose of this paper is to describe the representation of management practices in the GLEAMS model, and to give insight into what effects the practices have on pollutant loads. MODEL DESCRIPTION The GLEAMS model consists of four components operating simultaneously : hydrology, erosion/sediment yield, pesticides, and plant nutrients. The model has been described in detail elsewhere (Leonard et al., 1987 ; Knisel, 1993), and only a brief presentation is made here. Hydrology Daily water accounting is simulated in a soil system layered within the soil horizons of the root zone. The
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model distributes soil characteristics into a maximum of 12 computational layers with input from a maxi mum of 5 soil horizons. Daily potential evapotranspi ration is estimated by the Priestley-Taylor or Penman Monteith methods (Knisel, 1993). The uptake of soil water by a crop is simulated as a two-stage process with ET occurring at potential when water content is greater than 25 per cent plant available. Runoff is cal culated using a modified Soil Conservation Service curve number procedure (Williams and LaSeur, 1976). Curve numbers represent hydrologic soil group and management practice (USDA-SCS, 1972). The modifi cation by Williams and LaSeur (1976) mainly con sisted of replacing the 5-day antecedent rainfall with available soil water storage, and making the procedure a daily simulation rather than a design-type storm. Percolation through the soil layers uses a storage routing technique. Irrigation is based upon user specified threshold soil water content in the rooting depth, with an upper limit of application specified for possible water-deficit management. Daily rainfall data are required input and mean daily temperature data are optional for the hydrology component (Knisel, 1993). There are 67 codes and parameters in the hydrology component including mean monthly maximum, minimum, and dew point temperature, and mean monthly solar radiation and wind movement. Idealized leaf area data are included in the model for 78 crops with options for user supplied data. Only four parameters are sensitive for water balance calculations : curve number, soil poros ity, field capacity, effective root depth. Saturated con ductivity is sensitive only for heavy-texture or slowly permeable soils. Other parameters in the hydrology component which are sensitive in other components include : slope, length :width ratio, and clay, silt, and organic matter content of the soil. Soil pH, base satu ration, and calcium carbonate content are included as part of the hydrology input and are optional, depend ing upon the soil type and whether or not the plant nutrient component is run. Erosion The erosion component of GLEAMS is the Onstad Foster (Onstad and Foster, 1975) modification of the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). Rill and interrill erosion are calcu lated on the non-uniform slope of an overland flow element of the field. The erosivity (R) factor of the USLE is replaced by storm-by-storm rainfall energy calculated from daily rainfall. The management aspect of the USLE, i.e. soil loss ratio (C factor) and practice (P) factor, is maintained. In addition to a single repre sentative overland flow element, concentrated or chan nel flow can be represented in the field. A channel channel sequence, such as a representative terrace channel draining into a terrace outlet channel, can be
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designated to simulate a terrace system. A pond ele ment can also be included to represent an impound ment that drains shortly after runoff ends, such as an impoundment-type terrace, gully plug, or debris basin. Soil particles and organic matter detached by raindrop impact are routed through the delivery sequence of the field (Foster et al., 1985). A characteristic discharge, calculated from the storm runoff peak rate simulated at the field outlet in the hydrology component, is used to calculate sediment transport. The characteristic dis charge is translated back to the uppermost element and is used to calculate sediment transport capacity and deposition of each computational segment. Sedi ment yield and the associated sediment enrichment ratio (ratio of the specific surface area of the sediment to the specific surface area of the original soil) is cal culated at the end of each flow element and the outlet of the field (Foster et al., 1980). There are 45 user-supplied codes and parameters in the erosion component for the most complex applica tion. Some of the parameters are updateable, that is they change with crop growth and tillage. Parameter sensitivity is very site-specific for the erosion compo nent dependent upon the overland flow profile shape and the flow sequence (if there is a channel or pond element). Overland flow length and slope, flow resis tance, cover factor, and practice factor may be sensi tive in the overland flow element. Channel slope and channel flow resistance may be sensitive in the chan nel element. The pond pipe diameter and stage-volume relationships are sensitive parameters for the pond ele ment. Sensitivity is dependent upon whether the sys tem is transport-limiting or detachment-limiting. Pesticides The pesticide component of GLEAMS incorporates the surface pesticide response of CREAMS (Leonard and Wauchope, 1980) with a vertical flux component to route pesticides into, within, and through the root zone (Leonard et al., 1987). Characteristics of pesti cide adsorption to soil organic carbon are used to par tition compounds between solution and soil fractions for simulating extraction into runoff, sediment, and percolation losses. Pesticide dissipation in soil and on foliage is treated as a first-order process with a differ ent half-life for each fraction. Degradation products. (metabolites) can be considered when their character istics are known. Up to 10 pesticides can be simulated simultaneously, including metabolites. A data base is available in the parameter editor with pesticide charac teristics including water solubility, adsorptivity, aver age soil half-life, foliar half-life, and foliar washoff fraction (Knisel, 1993). Material washed from plants is added to the mass in the surface 1 cm of soil and the concentration adjusted for infiltration and runoff. Pesticides may be applied on the soil surface, incorpo rated in the soil, injected, applied on soil and foliage, Eur. J. Agron.
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Representing Management Practices in GLEAMS
or applied in irrigation water. Multiple applications can be made for any compound during a cropping period. Pesticide losses are simulated in runoff, with sediment, and in percolate at the bottom of the root zone. The product of sediment yield and enrichment ratio is used to calculate the sediment portion of the losses. Codes and parameters in the pesticide component total 27 including 5 pesticide characteristics. The pes ticide parameter editor contains a data base with char acteristics for about 250 compounds. The pesticide partitioning coefficient and soil half-life are generally sensitive for runoff, sediment, and percolation losses of pesticides. Low water solubilities may limit the availability of a compound and may be sensitive. Foliar characteristics, half-life and wash-off fraction may be sensitive for some soil and climatic regions. Depths of pesticide incorporation or injection and amount of irrigation water containing pesticides applied may be sensitive for leaching losses on light texture soils. Plant Nutrients
The plant nutrient component of GLEAMS consid ers nitrogen and phosphorus (Knisel, 1993). Compre hensive nitrogen and phosphorus cycles are treated in the model. Much of the nutrient component is very similar to the EPIC model (Sharpley and Williams, 1990), except the animal waste component. The nitro gen cycle includes : mineralization, immobilization, denitrification, ammonia volatilization, nitrogen fixa tion by legumes, fertilizer and animal waste applica tion, crop uptake, and runoff, sediment, and leaching losses. Mineralization is treated as a two-step process : first-order ammonification, and zero-order nitrification. The ammonification is consistent with animal waste loadings and ammonia volatilization. The phosphorus cycle includes : mineralization, immobilization, fertil izer and animal waste application, crop uptake, and runoff, sediment, and leaching losses. Inorganic fertil izer application considers surface application, incorpo ration, and fertigation. Animal waste application, with specification of nutrient content, may be represented as surface, incorporation, injection, or liquid such as lagoon effluent. Organic fractions of animal waste N and P are maintained as separate fractions that miner alize with different rate constants from those for fresh organic N and P in crop residue or mineralizable soil N and P. Tillage and soil temperature algorithms are included in the nutrient component. The tillage com ponent allows incorporation of crop residue, animal waste, and fertilizer, and the mixing of the respective pools in the ploughed layers. Ammonification, nitrifi cation, denitrification, volatilization, and mineraliza tion rates are adjusted by soil temperature and water content in the respective computational soil layers. Rainfall nitrogen is an input for the model application Vol. 4, n° 4 - 1995
site, and N and P in irrigation water can be considered for locations where concentrations in the water supply may be significant. A total of 51 codes and parameters are included in the plant nutrient component, of which 16 are crop, tillage, and animal waste characteristics that are inter nally defined or can be user supplied. The initial con tent of 7 nutrient pools that can be model-calculated or user-defined may be sensitive in simulation results, especially for short-term climatic records. Fertilizer placement and animal waste characteristics and place ment are sensitive in nutrient losses. Tillage type and depth may be sensitive in determining surface or leaching losses. Soil chemical characteristics (input in the hydrology component) are sensitive for phospho rus mineralization and sorption. Overall sensitivity is a function of soil and climatic region. Management
GLEAMS was developed to compare the responses of alternate management practices to long-term cli mate. Therefore, the model must be capable of repre senting a wide range of management practices. The SCS curve number procedure for estimating runoff ranks a number of practices by giving a curve number within each hydrologic soil group (USDA-SCS, 1972). In its original application for design type storm, curve numbers are listed for row crops, small grain, pasture, legumes, etc. Within each of the crop groups, curve numbers are given for conservation tillage, straight rows, contour tillage, and terraces. Good and poor hydrologic condition further categorized receptiveness of the soil for rainfall infiltration with different curve numbers. Thus, the modified method represents man agement in the continuous runoff simulation procedure (Williams and LaSeur, 1976). The Universal Soil Loss Equation (USLE) (Wisch meier and Smith, 1978) represents different tillage types, crops, cropping systems, surface residue, and conditions with different cropping factors (C-factors). Practice factors (P-factors) were included in the USLE to represent straight rows up-and-down-hill and con touring. The C and P factor concepts were retained by Onstad and Foster (197 5) in their modification of the USLE for storm-by-storm application in the CREAMS model (Knisel, 1980). Terraces are mechanical prac tices designed to break long, steep slopes and result in low velocity (low sediment transport capacity) along the terrace channels and vegetated terrace outlets. Tile outlet terraces with a perforated pipe riser in a tempo rary pond allow coarse-grain sediment to deposit in the ponded runoff where velocity and transport capac ity is practically zero. Surface residue is a function of production level and tillage type and degree, and it in tum affects the C-factor (designated as Soil Loss Ratio in GLEAMS). Several management practices are listed
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in Table 1 with respective changes in each component and the comparative results. There are numerous man agement systems not included in the table, but these are the ones easiest to depict with their results. It should be pointed out that no management prac tices are without limitations. For example, vegetated buffer strips and vegetated channels require significant maintenance to prevent erosion. Sediment deposition where runoff enters the buffer strip or channel will result in deltas that cause meandering flow or flow lat erally along the vegetated strip in subsequent storms. Spreading the sediment deposits is necessary to pre vent erosional damage that the resource management systems were to control initially.
Selection of pesticides for specific soils and crop ping systems can be made with the GLEAMS model based upon pesticide characteristics (Leonard et al., 1989), but this chemical comparison cannot be readily categorized in Table 1. For example, there may be an array of pesticides that can be used in a rotation for a specific pest, insect or weed. If adequate control can be achieved with alternate pesticides, selection can be made to minimize runoff losses (low K0 c and/or short half-life) or to minimize percolation losses (high K 0 c and short half-life). Strip cropping is not included in the practices of Table 1, but is an important erosion-control practice for some rotations in some soil and climatic regions. The erosion component is capable of simulating up to four crops (strips) along the overland flow profile. A single crop is assumed in hydrology, but a composite leaf area index can be input to represent the seasonal differences in such crops as winter small grain and a summer row crop. A curve number for small grain would be used to calculate the maximum available storage. The rotation feature in erosion allows the year-to-year alternate cropping by segment. Thus, the hydrology and erosion can give a reasonable approxi mation of the runoff and sediment yield from a strip crop system. However, the pesticide and plant nutrient components could not represent applications of chemi cals realistically on alternate strips. The broadcast equivalent representation would be less than satisfac tory for strip cropping since agrichemicals would be applied at different times for winter small grain and summer row crops. If the soil and climatic region is one in which runoff constitutes the major losses with negligible percolation losses, 'then broadcast equiva lent' might produce reasonable results. If significant runoff and percolation occur, results would not be valid. Animal waste application cited in Table 1 does not consider time of application, which may be critical in some soil-climatic regions. Animal waste applications for crop productivity must be made well before peak crop uptake in order for mineralization of nitrogen and phosphorus to occur. If waste application is made when the soil is frozen and there is a snowpack,
excess nutrient losses can occur with snowmelt runoff and percolation. Time and method of application rela tive to planting are important, and these can be con sidered as management alternatives with GLEAMS simulations. These aspects are not a part of the prac tices listed in Table 1. Crop rotations including multiple cropping, that is growing more than one crop during a year, represent intensive management that can be considered in GLEAMS. Vegetable crops are grown in rotation with field crops in some climatic regions. Most vegetable crops generally do not have root systems as deep as those for field crops. GLEAMS model users can input the root depth of individual crops, and water and chemical uptake is not considered below the current crop rooting depth (CCRD). For example, the effective root depth may be 60 cm which would be the depth for a field crop, but a vegetable crop may root only in the plow layer, or about 25 cm. If CCRD for the veg etable crop is input as 25 cm, there would be no root growth or water and chemical uptake below the 25-cm depth. The soil water content would remain at (or above) field capacity from 25 to 60 cm, and any per colation of water and chemicals below 25 cm would be re-equilibrated with the equivalent field capacity water content on a daily basis. Chemical transforma tions are simulated daily in each computational soil layer between 25 and 60 cm to approximate any mass percolate below the 60-cm depth.
MODEL APPLICATIONS The GLEAMS model is used in about 40 countries, mainly in North America and Europe. Over 1,000 cop ies have been distributed for use in validation, evalua tion, and assessment applications by environmental quality planners and regulators, consultants, research ers, and scientists. GLEAMS is widely used in the USDA-Natural Resources Conservation Service (formerly Soil Con servation Service) to evaluate management alterna tives. Reck (1994) evaluated nitrate leaching losses from alternate management practices on sandy soils. Management strategies were analysed for large-scale dairy operations in an area with a vulnerable shallow aquifer. Geter et al. ( 1992) used GLEAMS to develop a pesticide screening procedure for national applica tion in USDA-SCS. The US Environmental Protection Agency, Office of Pesticide Programs, recommended GLEAMS as one of the two preferred field-scale models for use by agrochemical companies to provide supporting infor mation for pesticide registration. It is used by State agencies for simulations to consider pesticide registra tion in individual states within the US. These recom mendations have resulted in GLEAMS model validaEur. J. Agron.
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Table 1.
Checklist of parameter changes in the GLEAMS model for selected management practices.
Practice
Model Component
Results
1
Hydrology
Erosion
Pesticides
Plant Nutrients
Conservation tillage
Lower curve Number (CN2)
Lower soil loss ratio (C-factor, overland flow element)
None
Tillage incorporates and mixes nutrient pools
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Contour tillage
Lower CN2
Lower P-factor (overland flow element)
None
None
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Terracing
Lower CN2 Longer flow path (Lower CHS, higher WLW)
flow seq. overland length P-factor
None
None
Runoff Runoff peak rate Percolation + Sediment yield
Impoundment (Sediment basin)
None
Change flow seq. Pond characeristics
None
None
Runoff = Runoff peak rate Percolation = Sediment yield Sur pst & nut Pere pst & nut
Small grain or meadow in rotation
Lower CN2
Lower C-factor (overland flow element)
None
None
Runoff Percolation + Sediment yield Sur pst & nut Pere pst & nut +
Vegetated buffer strip
None
Lower C-factor on lowest overland flow segment Higher Manning 'n' on lowest segment
Lower broadcast equivalent application rate
Lower unit fertilizer application rate
Runoff = Percolation = Sediment yield Sur pst & nut Pere pst & nut
Vegetated channel
None
Higher Manning 'n' in channel
None
None
Sediment yield Sur pst & nut -
Animal waste incorporated
None
None
None
Tillage, incorporation and mixing
Sur nutrients Pere nutrients + NH4 volatilization
Split fertilizer applications
None
None
None
Lower fertilizer rate each application
Nutrient uptake + Sur nutrients Pere nutrients
Irrigation
Water deficit management
None
None
None
Soil water + Runoff 2 Percolation 2 Sediment yield 2 Sur pst & nut 2 Pere pst & nut 2
Pesticide and fertilizer application with irrigation
None
None
CHMWAT (water depth)
FRTWAT (water depth)
Soil water + Runoff 2 Percolation 2 Sediment yield 2 Sur pst & nut 2 Pere pst & nut 2
Banding pesticides
None
None
Lower broadcast equivalent application rate
None
Sur pesticide Pere pesticide
Change Shorter flow Lower
1 Abbreviations : Sur = Surface ; Pere = Percolation ; pst = pesticide ; nut Symbols : - less ; + more ; = same ; 2:' same or more ; ::::; same or less.
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= nutrients
.
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tions and evaluations nationally and internationally (Shirmohammadi and Knisel, 1994 ; Smith et al., 1991 ; Zacharias and Heatwole, 1994). Researchers have aided in validation of the GLEAMS model or evaluation of some of its compo nents. Dixon et al. (1993) compared leaching charac teristics of banded and broadcast tracers to test and verify the concept of using 'broadcast- equivalent' representation of chemical application. Salo et al. (1994) evaluated pesticide flux and concentrations in clay and loamy sand soils in Finland. They found that GLEAMS over-predicted pesticide dissipation after the first few weeks following application under their tem perature regime. Knisel (1993) showed very good comparisons for nitrate leaching for two poultry litter application rates (management practices) when site-specific soil and waste characteristic data were used. Yoon et al. (1994) found that GLEAMS simulation results did not com pare very well with field observations of nutrient losses for poultry litter application when model default values were used. Their study points out the need to use local data for validation and parameter adjustment where possible, as recommended by the model devel opers. GLEAMS has been used to select 'environmentally safe' herbicide application windows within broader windows of vegetation control for forest site prepara tion and production (Smith et al., 1993 ; Smith et al., 1994). Long-term climatic records were used to exam ine year-to-year differences in herbicide runoff and leaching losses for periods up to six months recom mended by manufacturers for best vegetation control. Leonard et al. (1989) proposed using GLEAMS as a screening tool to group pesticide and soil character istics based upon surface and leaching losses. Geter et al. (1992) extended the concepts in a two-tier screen ing procedure for use by the USDA-SCS. Both of these ideas used specific soils and long-term climatic records. Goss (1992) developed a more generalized screening procedure that used the GLEAMS model with generalized soil characteristics and short-term synthetic rainfall distribution. GLEAMS has been successfully linked with other models and procedures also. Chung et al. (1992) com bined the pesticide and erosion components of GLEAMS with the hydrologic component of DRAIN MOD to form the ADAPT model (Agricultural Drain age And Pesticide Transport). ADAPT was evaluated for water table management in heavy clay soils. Shir mohammadi et al. (1989) used GLEAMS output from the bottom of the root zone as input to the vadose zone. Their work allowed analyses of further pesticide degradation and transport to shallow groundwater sys tems. There are several efforts underway to link GLEAMS with geographical information systems (GIS) to manage model parameters, data bases, and
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output. Searing and Shirmohammadi (1993) used GIS and GLEAMS to manipulate model parameters and organize output for generating best management prac tices in a drainage basin. Tucker et al. (1994) devel oped procedures for using GIS and text file conversion programmes to develop hydrology parameter files for GLEAMS and a riparian zone model. There are numerous other similar applications, but these are cited merely as examples. This brief description of GLEAMS applications indicate the widespread uses of the model. The refer ences provide additional information on the model.
REFERENCES Chung S. 0., Ward A. D. and Schalk C. W. (1992). Evaluation of the hydrologic component of the ADAPT water table man agement model. Trans. ASAE 35(2): 571- 579. Dixon K. L., Smith M. C., Thomas D. L. and Knisel W. G. (1993). Leaching characteristics of banded and broadcast inorganic tracers. Trans. ASAE 36(6) : 1779- 1788. Foster G. R., Lane L. J., Nowlin J. D., Laflen J. M. and Young R. A. (1980). A model to estimate sediment yield from field sized areas : Development of model. In : Knisel W.G. (Ed.) CREAMS : A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. U.S. Dept. of Agric., Conservation Research Report No. 26. pp. 36-64. Foster G. R., Young R. A. and Neibling W. H. (1985). Sediment composition for nonpoint source pollution analyses. Trans. ASAE 28(1): 133-139, 146. Geter W. F., Plotkin S., Bagdon J. K. and Hesketh E. S. (1992). The national agricultural pesticide risk assessment. 1992 Summer Meeting of the ASAE, Charlotte, North Carolina, June 1992. Paper No. 92-2039, 6 pp. Goss D. (1992). Screening procedure for soils and pesticides for potential water quality impacts. Weed Technol. 6 :701-708. Knisel W. G. (Ed.) (1980). CREAMS: A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Manage ment Systems. U.S. Dept. of Agric., Conservation Research Report No. 26, 643 pp. Knisel W. G. (Ed.) (1993). GLEAMS: Groundwater Loading Effects of Agricultural Management Systems, Version 2.10. Biological and Agricultural Engineering Department, Univer sity of Georgia, Coastal Plain Experiment Station, Tifton. BAED Pub!. No. 5,. 260 pp. Leonard R. A. and Wauchope R. D. (1980). The pesticide sub mode!. In : Knisel W.G. (Ed.) CREAMS: A field-scale model for Chemicals, Runoff, and Erosion from Agricultural Man agement Systems, U.S. Dept. of Agric., Conservation Research Report No. 26, pp. 88-112. Leonard R. A., Knisel W. G. and Still D. A. (1987). GLEAMS : Groundwater Loading Effects of Agricultural Management Systems. Trans. ASAE 30(5): 1403-1418. Leonard R. A., Perkins H. F. and Knisel W. G. (1989). Relating agrichemical runoff and leaching to soil taxonomy : A GLEAMS model analyses. Proc. of the 1989 Georgia Water Resources Conference, Athens, Georgia, May 16-17, 1989, pp. 158-160. Onstad C. A. and Foster G. R. (1975). Erosion modeling on a watershed. Trans. ASAE 18(2) : 288-292. Reck W. R. (1994). GLEAMS modeling of BMPs to reduce nitrate leaching in Middle Suwanee River Area. Proc. of the Eur. ], Agron.
Representing Management Practices in GLEAMS
Second Conference on Environmentally Sound Agriculture, April 20-22, 1994, Orlando, Florida, pp. 361-367. Salo S., Posch M. and Rekolainen S. (1994). Testing the modi fied CREAMS/GLEAMS model for pesticide concentration in soil. Agric.Sci. Finland, 3 : 59-68. Searing M. L. and Shirmohammadi A. (1993). Utilizing ors and GLEAMS to prescribe best management practices for reducing nonpoint source pollution. 1993 Summer Meeting of the ASAE. Paper No. 93-3558. Sharpley A. N. and Williams J. R. (Eds.). (1990). EPIC Erosion/Productivity Impact Calculator : 1. Model Documen tation. U.S. Dept. of Agric., Technical Bulletin No. 1768, 23 pp. Shirmohammadi A. and Knisel W. G. (1994). Evaluation of the GLEAMS model for pesticide leaching in Sweden. J. Envi ron. Sci. Health, Part A-Environmental Sci. and Eng. A29(6): 1167-1182. Shirmohammadi A., Gish T. J., Lehman D. E. and Magette W. L. (1989). GLEAMS and the vadose zone modeling of pesti cide transport. 1989 Summer Meeting of the ASAE, Paper No. 89-2071. 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. ASAE 34(3): 838-847. Smith M. C., Knisel W. G., Michael J. L. and Neary D. G. (1993). Simulating effects of forest management practices on pesticide losses with GLEAMS. Proc. of the International
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Runoff and Sediment Yield Modeling Symposium, September 1993, Warsaw, Poland, pp. 157-162. Smith M. C., Michael J. L., Knisel W. G. and Neary D. G. (1994). Using GLEAMS to select environmental windows for herbicide application in forests. Proc. of the Second Confer ence on Environmentally Sound Agriculture, April 20-22, 1994, Orlando, Florida, pp. 506-512. Tucker M. A., Thomas D. L., Altier L. S. and Bosch D. D. (1994). ors interfaced with field and riparian zone models. 1994 Summer Meeting of the ASAE, Kansas City, Missouri. Paper No. 94-2151, 13 pp. U.S. Dept. of Agric., Soil Conservation Service. (1972). SCS National Engineering Handbook, Section 4, Hydrology, 548 pp. Williams J. R. and LaSeur W. V. (1976). Water yield model using SCS curve numbers. J. Hydraul. Div. ASCE 102(HY9) : 1241-1253. Wischmeier W. H. and Smith D. D. (1978). Predicting rainfall erosion losses. U.S. Dept. of Agric., Agriculture Handbook No. 537, 58 pp. 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. ASAE 37(2) :453-459. Zacharias S. and Heatwole C. D. (1994). Evaluation of GLEAMS and PRZM for predicting pesticide leaching under field conditions. Trans. ASAE 37(2): 437-451.