Modelling pesticide fate with GLEAMS

Eur. J. Agron., 1995, 4(4), 485-490

Modelling pesticide fate with GLEAMS R. A. Leonard* 1 , W. G. Knisel

2

,

and F. M. Davis

1

U. S. Department of Agriculture, Agricultural Research Service Southeast Watershed Research Laboratory, Tifton, Georgia 31793 USA 2 Biological and Agricultural Engineering Department University of Georgia, Coastal Plain Experiment Station Tifton, Georgia 31793 USA 1

Accepted: 21 June 1995

* To whom correspondence should be addressed. Abstract

The pesticide component of GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) model was developed to evaluate the complex interrelationships among pesticide and soil properties, management alternatives, and climate using long-term simulations. GLEAMS resulted from an enhancement of the CREAMS model to allow simulation of pesticide transport within and through the plant root zone in addition to transport in surface runoff from field-sized areas. The pesticide submode! is operated with daily inputs from the hydrology and soil erosion sub-models. Pesticide input parameters are required to specify application rates, dates and methods and the prop­ erties of the chemical. Outputs are pesticide concentrations and mass in runoff water and attached to transported sediments, pesticide mass leached below the root zone, and pesticide distribution with depth in the root zone. Multiple applications of up to 10 different pesticides can be simulated simul­ taneously for periods of up to 50 years. The model also considers pesticide metabolites produced by sequential first-order reactions and plant uptake of pesticides. Application methods simulated may be soil surface application, soil incorporation, soil injection, foliar application, or through irrigation water. Values for pesticide soil half-life, foliar half-life, foliar wash-off potential, solubility, and K 0 c (sorption coefficient for soil carbon) may be obtained from the internal data base or supplied by the user if more specific information is available. Key-words : non-point source pollution, herbicides, hydrology, chemical transport, ground water.

INTRODUCTION The GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) model was devel­ oped as a tool to examine the complex interrelation­ ships among pesticide properties, soil characteristics, management variables, and climate affecting potential pesticide transport in surface runoff and subsurface drainage (Leonard et al., 1987). GLEAMS resulted from an extension and enhancement of the earlier U.S. Department of Agriculture Model, CREAMS, Chemi­ cals, Runoff, and Erosion from Agricultural Manage­ ment Systems (Knisel, 1980). This extension of CREAMS (Leonard et al., 1987) allowed simulation of pesticide degradation and transport within and through the soil-root zone as well as pesticide transISSN 1161-0301195104/$ 4.00/© Gauthier-Villars - ESAg

port in surface runoff and by sediments. A more recent version of GLEAMS (Knisel, 1993) simulates nitrogen and phosphorus transport from applied fertilizers and animal wastes and cycling in agricultural production systems. The purpose of this paper is to provide a brief over­ view of the pesticide component of GLEAMS and its application. GLEAMS is a field-scale model designed to compare potential chemical loadings to the environ­ ment at the field edge and bottom of the soil root zone. The model is not intended for quantitative site specific prediction of chemical concentrations in the various environmental compartments for regulatory compliance. It is intended as a tool to assist in select­ ing environmentally sound management practices on different soils and in different climatic regimes.

486

MODEL OVERVIEW To address management systems, GLEAMS is con­ structed in four components or sub-models : hydrol­ ogy, soil erosion/sedimentation, pesticides, and plant nutrients. The hydrology and erosion/sedimentation components drive the system and provide the vehicles for pesticide transport and are discussed briefly in this context. The model has been described in detail else­ where (Leonard et al., 1987; Knisel, 1993). The hydrology component uses daily climatic data to calculate the water balance in the root zone. Pre­ cipitation is partitioned between surface runoff and infiltration into the soil surface, using the U.S. Depart­ ment of Agriculture, Soil Conservation Service (1972) curve-number method as modified by Williams and Nicks (1982) to estimate runoff. A storage-routing technique is used to simulate redistribution of infil­ trated water within the root zone, and percolation out of the bottom of the root zone. Storage-routing means that water drains from one layer to the next to field moisture capacity. Daily potential evapotransporation is estimated by the Priestley-Taylor or Penman­ Monteith methods (Knisel, 1993). Computational soil layers are determined by the depth of the root zone and the number of soil horizons. A minimum of 3 and a maximum of 12 layers of variable thickness are established for water and pesticide routing. The sur­ face layer is 1 cm thick, and other layers in the top soil horizon are a maximum of I 0 cm thick, but divided into equal thickness. The remaining layers are 15 cm thick, equally divided within each horizon. The lower layers may be greater than 15 cm to meet the constraint of the 12-layer maximum. The GLEAMS model uses the work of Foster et al. (1980) who modified the Universal Soil Loss Equation (Wischmeier and Smith, 1978) for storm-by-storm simulation of rill and interrill erosion in overland flow areas. Channel and pond elements calculate erosion or deposition in the field delivery system to estimate sediment yield at the edge of the field. Eroded soil is routed with runoff by particle size (Foster et al., 1985) which enables calculation of storm sediment enrich­ ment ratios for use in simulating adsorbed pesticide transport. Model input requirements are daily climatic records for the simulation period, soil characteristics by genetic horizon, depth of the root zone, crop and man­ agement descriptors, and pesticide properties. Inputs for pesticide simulation specify methods, rates, and dates of application ; degradation half-life in soil by soil depth, pesticide solubility, the adsorption constant based on soil carbon ( K 0 ) ; and for foliar applica­ tions, the pesticide dissipation half-life on crop canopy, and a coefficient describing the pesticide frac­ tion dislodgeable by rainfall. Usually the outputs of interest are pesticide concentrations and mass in runoff

R. A. Leonard et al.

and attached to sediments, and that leached below the root zone. Outputs may be by storm event, or monthly or annual summaries. For all four model components, the user may specify up to 20 output variables from a list of 120. This allows a detailed look at some aspects of special interest such as pesticide concentra­ tions by soil computational layer over some finite period after pesticide application. In a model run, up to 10 different pesticides with single or multiple appli­ cations may be simulated simultaneously for periods up to 50 years. The GLEAMS software contains parameter editors and other user aids to facilitate model implementation. A pesticide properties data base with average or repre­ sentative values for soil and foliar half-life, solubility, plant canopy washoff potential, and K 0 c is included for use if desired. However, site and condition-specific values are recommended if available. PESTICIDE PROCESS REPRESENTATION Processes affecting pesticide fate in soil and water and on vegetation are extremely complex and often incompletely understood. Development of functional simulation models requires simplifications, assump­ tions, and representation in algorithms that are com­ promises between process detail and computational efficiency. Research models such as LEACHMP (Wagenet and Hutson, 1986) and RZWQM (DeCour­ sey and Rojas, 1990) contain more process detail than PRZMl and PRZM2 (Carsel et al., 1984; Mullins et al., 1993) models developed for regulatory decisions, or GLEAMS, which was developed for evaluation of management alternatives. Development of algorithms in GLEAMS have been previously described (Leonard et al., 1987; Leonard et al., 1990) and only the pri­ mary assumptions and concepts are discussed herein. Pesticide sources Depending on the method of field application and intended target, pesticides may be applied on the soil surface or plant foliage, mixed to some finite depth in the soil, or injected below the soil surface. For aerial applications significant quantities may be lost off­ target in drift and by volatilization. In GLEAMS, the fraction of an application reaching the soil and/or crop canopy is estimated by the user. From this distribu­ tion, initial concentrations in the soil by depth are computed in the model considering application meth­ ods and depth of incorporation. Surface applications are assumed to be mixed in the upper 1 cm of soil for reasons discussed later. Pesticide distribution between solution and solid phases A linear, reversible Freundlich-type adsorption­ desorption process is assumed to determine the distriEur. J. Agron.

487

Modelling pesticide fate with GLEAMS

bution of pesticide between water and soil or sedi­ ment. Soil organic matter is also assumed to be the primary adsorbent and the adsorption constant, K 0 c is expressed on a soil carbon basis. The K 0 c is therefore dependent only on the pesticide, and the model distri­ bution between water and soil or sediment is com­ puted by the model from the carbon content in each soil layer and the Koc· Degradation/transformation in soil Pesticides are assumed to degrade in soil by pseudo-first order kinetics and their persistence is described by a half-life, t 112 , parameter. The t 112 is a lumped parameter, based on empirical data and includes all mechanisms of removal except by water transport. Although the rate of degradation processes is affected by temperature, soil water content and other factors, adjustments to the rate constants are not made based on daily climatic data. Gross regional cli­ mate differences may be reflected by the value of the t u2 input. Different t 112 values may be input for the different soil horizons, thus reflecting different rates of microbiological or chemical activity in the different soil layers. Rapid pesticide dissipation at the soil sur­ face as a result of vapor loss or photodecay may be allowed for in the assigned t 112 • Appearance and ultimate fate of environmentally significant pesticide daughter products or metabolites produced by sequential first-order reactions are also simulated if desired. The coupled rate equations are solved numerically using one-day time steps. Inputs required are metabolite half-life and coefficients equal to the ratio of rate constants for the reaction of interest and to the rate of such other side reactions as hydroly­ sis. Degradation/dissipation on foliage Pesticide dissipation from plant foliage by processes other than rainfall dislodgement are also described as a pseudo first-order process. For most pesticides, the foliar half-life for dislodgeable residues is significantly shorter than that for degradation in soil. The rate of pesticide dislodgement by rainfall is dependent on total rainfall amount rather than intensity (Willis et al., 1980). At the time of rainfall the fraction dislodgeable (an input parameter) depends on pesticide solubility and the other factors and tabulated values for different pesticides were estimated mainly from such data as by Willis et al. (1980) and Willis and McDowell (1987). In GLEAMS the pesticide residue on foliage is dis­ lodged and added to that at the soil surface when rain­ fall exceeds the volume of canopy interception. Entrainment into runoff Pesticide concentrations entrained in surface runoff are strongly correlated with concentration in the surVol. 4. n° 4 - 1995

face 0-1 cm of soil (Leonard et al., 1979). Some investigators using laboratory scale rainfall simula­ tions have suggested that the zone of interaction or extraction depth at the soil surface to be as small as 1 to 3 mm. However, at a field scale this would be dif­ ficult to prove because of surface roughness and sam­ pling difficulties. GLEAMS therefore, uses the 0-1 cm soil depth increment as the source of entrained pesti­ cides. The concentration in the surface soil increment is computed using t 112 and time after application and by adding any pesticide dislodged from the crop canopy. Before computations of runoff concentrations, pesticide mass at the soil surface is reduced to account for infiltration through the surface zone. The entrain­ ment algorithm then uses the surface available concen­ tration, a coefficient conceptually equal to the ratio of soil mass : water mass at the entrainment interface, and K to distribute pesticides between solution and sediment phases. Pesticide solubility limits maximum solution concentration. For pesticide concentrations in transported sediments, the erosion sub-model provides an enrichment factor to account for preferential trans­ port of fine sediment and organic matter. 0

,

Transport in and through the root zone Pesticides are moved between computational layers in the soil with percolating water. Pesticide concentra­ tions in soil percolate are computed from the pesticide mass in each computational layer, water content, and Koc· Evaporation at the soil surface between rainfall events is allowed to move pesticides towards the soil surface. Plant uptake Translocation of water to plant root surfaces in response to transpiration redistributes pesticides resid­ ing in the soil solution. The model allows the user the option of assuming plant uptake in the transpiration stream if desired.

MODEL VALIDATION Models such as GLEAMS with many interacting components give rise to almost an infinite number of combinations. Certain parameters are statistical in nature and selected or scaled to provide 'average' responses in time and space. Therefore, complete vali­ dation in a global sense by comparing simulations with data from limited short-term field observations can never be fully achieved (Leonard and Knisel, 1990). Uncertainties in field data arising from limited sampling of heterogenous systems is an additional problem.

488

The surface response in GLEAMS as to entrain­ ment of chemical in runoff is essentially the same as in CREAMS (Knisel, 1980). During development of the pesticide component in CREAMS, outputs from the hydrology, erosion, and chemistry sub-models were computed independently and linked through pass-files. This allowed validation of the pesticide component with actual rainfall, runoff, and sediment yield data to drive simulation of pesticide runoff con­ centrations for comparison with measured concentra­ tions. In this way errors in hydrological and soil ero­ sional responses were not reflected in pesticide predictions (Leonard and Wauchope, 1980). GLEAMS runs as a single operating program so that piece-wise validation cannot be achieved. During development of GLEAMS, simulation of surface and leaching responses were compared with observations at four sites using published data (Leonard et al., 1987). Other model evaluations by the developers using field data are : pesticide metabolite accumulation and fate in soil (Leonard et al., 1990), chemigation manage­ ment (Leonard et al., 1989), and pesticide fate in the soil root zone, (Leonard et al., 1991).

R. A. Leonard et al.

increased with increasing K 0 c and then declined slightly. Runoff losses for the low K 0 c pesticides were less because they were rapidly translocated below the surface soil layer effective in imparting chemicals to runoff. As the K 0 c increased, a greater proportion was adsorbed to transported sediment so that amounts in water declined as K 0 c exceed a value of about 700. At a K 0 c of 1,000, equal amounts of pesticides were transported in water and sediment phases. In addition to the dependance on Koc' the distribution between water and sediment is also dependent on the water : sediment ratio in any given storm event. Both the water and sediment-phase pesticide losses increased with t 112 as expected. The t 112 range of 3 to 60 days produced about 10-fold difference in surface loss.

c MODEL APPLICATION The primary purpose of GLEAMS is to evaluate management systems for non-point source pollution abatement. Management application is discussed in a companion paper (Knisel et al., 1995) to illustrate how primary users of GLEAMS such as the U.S. Soil Conservation Service, water quality management agencies in the individual U.S. states, and others use GLEAMS. Model applications discussed here illustrate some of the interrelationships between input and output vari­ ables and to give an example of application to a spe­ cific issue that has both agronomic and environmental implications. The two most important pesticide properties affect­ ing runoff and leaching losses are the K 0 c and t 112 • As the K 0 c increases, a higher proportion of the pesticide is adsorbed to the soil organic matter and is not imme­ diately available for water transport. As t 112 increases, the pesticide degrades less rapidly in the soil and therefore is available for transport over a longer period of time. Pesticide solubility is also important, but K 0 c and solubility are related for most compounds so that K 0 c is the dominant variable. The relationship between simulated 50-year annual mean pesticide sur­ face runoff losses from a sandy clay loam soil near Tifton, Georgia, USA and pesticide K0 c and t 112 is depicted in Figure 1 (Leonard and Knisel, 1988). These simulations assumed pre-emergence herbicides surface-applied after maize planting. The surface losses, as percentages of that applied, initially

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Figure 1. Pesticide runoff from Greenville sandy clayloam soil as affected by pesticide K 0 " and t 112 • Annual means from a 50-year simulation.

Simulated pesticide leaching losses from the same sandy clay loam soil are shown in Figure 2. As with surface losses, subsurface losses increased with increased t 112 . Losses below the root zone at the low K 0 c end of the range were about 7 per cent applica­ tion for a pesticide with a t 112 of 60 days. Subsurface Eur. J. Agron.

489

Modelling pesticide fate with GLEAMS

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Figure 2. Pesticide leaching losses below root zone of Greenville sandy clay loam soil as affected by pesticide K 0 c and t 112 • Annual means for a 50-year simulation.

losses declined rapidly and were probably insignificant as the K 0 c exceeded 200-300. Exercises as above may be repeated for different soils and crops, planting times and climatic scenarios and results combined with considerations of available chemicals, costs, toxicity, efficiency, and other produc-

tion requirements to select available products and practices to minimize potential adverse environmental affects (Hickman et al., 1993). Management practices where pesticides are retained in the root zone are desirable for minimizing adverse effects on ground water quality. Considerable work has been done on encapsulation of pesticides for con­ trolled release over some finite time after application to soil. Theoretically, controlled-release formulations could maintain some low concentration in soil over an extended period of time as needed to provide pest control. The GLEAMS model was applied to investi­ gate the potential for controlled-release formulations to reduce leaching losses to ground water, while main­ taining some low threshold of pesticide in the surface layer of soil (Leonard and Knisel, 1989). In model applications, a formulated controlled-release pesticide was assumed to be the pesticide parent and the released active ingredient to be the first daughter prod­ uct in a first-order reaction. The formulated parent vas assumed to release the active ingredient with a t 112 of 7-days. The active ingredient vas assigned a t 112 of either 5 or 14-days, and K 0 c of 50 or 200. Examples are given in Table 1 from simulations comparing conventional and controlled-release pesti­ cides applied to a coarse-textured sandy soil receiving an annual mean rainfall of about 1,200 mm per year for 50 years. This scenario represents an extreme case as to potential pesticide leaching. The scenario also assumed that a concentration of at least 0.5 mg kg- 1 of pesticide would be required in the 0-7 .5 cm soil sur­ face for about 25 days to achieve effective pest control and the application rate was adjusted accordingly. Simulation results show that encapsulation could potentially reduce leaching losses by about 50 per cent. However, the major benefit could be the much lower application rate required for maintenance of soil concentrations above 0.5 mg kg- 1 for the targeted duration which had even more potential for reducing leaching losses. This exercise was fictitious but serves

Table 1. Pesticide leaching losses from a coarse-textured sandy soil, conventional and controlled-release formulations and residence time in 0-7.5 cm soil sulface. Annual means for 50-year simulation period. (Adapted from Leonard and Knisel, 1989) Pesticide Formulation

Conventional

Controlled-Release, 7-day half-life

Vol. 4, n° 4- 1995

f 112

Appl. rate needed kg ha-I

Amount leached kg ha-I

Days > 0.5 mg kg-I in soil

Days

Koc

5 5 14 14

50 200 50 200

78.0 78.0 5.4 5.4

3.90 1.00 0.57 0.15

25 28 21 26

5 5 14 14

50 200 50 200

13.7 13.7 3.5 3.5

0.38 0.38 0.23 0.05

28 28 23 26

490

to illustrate how models such as GLEAMS may be used to answer 'what if' management and product development questions.

CONCLUSION The GLEAMS model was designed for relative pre­ dictions and for comparative analyses. Limited valida­ tion has been done but as with all models and simpli­ fied representations of real systems, absolute values for specific points in time and space cannot be pre­ dicted with known accuracy. Fortunately, however, in many situations, it is not as important to know the exact values as it is to know whether some scenario will have positive or negative benefit to water quality relative to another. Based on widespread acceptance and use, the GLEAMS model has been proved to be a useful and versatile tool for evaluating the complex interactions of pesticide chemistry, soil properties, climate, and management in affecting chemical loads in surface runoff and below the soil root zone.

REFERENCES Carse! R. F., Smith C. N., Mulkey L.A., Dean J. D. and Jowise P. (1984). Users Manual for Pesticide Root Zone Model (PRZM) : Release I, Rep. EPA-600/3-84-109, 219 pp. Ath­ ens, GA, USA : Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protec­ tion Agency. DeCoursey D. G. and Rojas K. W. (1990). RZWQM - A model for simulating the movementof water and solutes in the Root Zone. In : Proc. of the International Symposium on Water Quality Modeling of Agric. Non-Point Sources. Part 2. D. G. Decoursey, (Ed.), U.S. Dept. of Agric., Agric. Research Ser­ vice, ARS-81, p. 813-p. 821. 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 : W.G. Knisel (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, 133-139, 146. Hickman J. S., Finnell P. R. and Schlepp R. L. (1993). Soil­ pesticide ratings for Kansas. Agron. Abs., Cinn, OH. USA : Amer. Soc. Agron. Annual Meetings. p. 34. Knisel W. G. (Ed.) (1980). CREAMS: A field-scale model for Chemicals, Runoff and Erosion from Agricultural Manage­ ment Systems, U.S. Department of Agriculture, 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, BAED Publication No. 5. 260 pp.

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Knisel W. G., Leonard R. A. and Davis F. M. (1995). Repre­ senting management practices in GLEAMS. Proc. of the European Society for Agronomy Workshop "Modeling the Fate of Agrochemicals and Fertilizers in the Environment'', Venice, Italy, March 3-5, 1994. Eur. J. Agron., 4, 499-505. Leonard R. A., Langdale G. W. and Fleming W. G. (1979). Her­ bicide runoff from upland Piedmont watersheds - data and implications for modeling pesticide transport. J. environ. Qual. 8, 223-229. Leonard R. A., Knisel W. G. and Still D. A. (1987). GLEAMS : Groundwater Loading Effects of Agricultural Management Systems. Trans. ASAE. 30, 1403-1418. Leonard R. A. and Wauchope R. D. (1980). The pesticide sub­ mode!. p. 88-112. In: W.G. Knisel (Ed) Model documenta­ tion. Vol. 1. CREAMS : A field scale model for chemical, run­ off and erosion from agricultural management systems. USDA Conser. Res. Rep. 26. Washington, DC.: U.S. Gov. Print. Office. Leonard R. A. and Knisel W. G. (1989). Groundwater Loadings by Controlled-Release Pesticides : A GLEAMS Simulation. Trans. ASAE. 32, 1915-1922. Leonard R. A., Knisel W. G. and Davis F. M. (1989). Ground­ water Loadings of Pesticides from Chemigation : A GLEAMS Model Simulation. Proceedings of the American Society of Civil Engineers, Irrigation & Drainage National Conference, Newark, Delaware, July 18-20; pp. 430-442. Leonard R. A., Knisel W. G., Davis F. M. and Johnson A. W. (1990). Validating GLEAMS with Field Data for Fenamiphos and its Metabolites. J. Irrig. Drain. Eng. 116, 24-35. Leonard R. A. and Knisel W. G. (1990). Can Pesticide Trans­ port Models be Validated Using Field Data: Now and in the Future ? Agric. Engineering Department, Depart. Publication No. 3, University of GA., 26 pp. Leonard R. A. and Knisel, W. G. (1988). Evaluating groundwa­ ter contamination potential from herbicide use. Weed Technol. 2, 207-216. Leonard R. A., Knisel W. G., Davis F. M. and Truman C. C. (1991) Application of the GLEAMS model at the Plains, Georgia, Agricultural Management Site, USGS Toxic Sub­ stances Hydrology Program, Proc., Monterey, CA, March 11-15. G. E. Mallard and D. A. Aronson (Eds.). Water Res. Invest. Rep. 91-4034, 601-604. Mullins J. A., Carse! R. F., Scarbrough J. E. and Ivery A. M. (1993). PRZM2 Users Manual Version 1.0., Athens, GA. USA: Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency. U.S. Department of Agriculture, Soil Conservation Service. (1972). SCS National Engineering Handbook, Section 4, Hydrology. 548 pp. Wagenet R. J. and Hutson J. L. (1986). Predicting the fate of nonvolatile pesticides in the unsaturated zone. J. environ. Qual. 15, 315-322. Williams J. R. and Nicks A. D. (1982). CREAMS hydrology model-Option 1. In : Proceedings of the International Sympo­ sium on Rainfall-Runoff Modeling. 69-86. Littleton, CO: Water Resources Publications. Willis G. H., Spencer W.F., and McDowell L.L. (1980). The interception of applied pesticides by foliage and their persis­ tence and washoff potential. Vol. 3, Ch. 18. In: W.G. Knisel (Ed.) CREAMS: A field scale model for chemicals, runoff, and erosion from Agricultural Management Systems. U.S. Dept. of Agri., Sci. and Education Adm., Conservation Research Report No. 26. pp. 595-606. Washington, DC, USA : U.S. Gov. Printing Office. Willis G. H. and McDowell L. L. (1987). Pesticide persistence on foliage. Rev. Environ. Contam. Toxicol. 100, 23-73.

Eur. J. Agron.