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DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

GLEAMS, Opus, PRZM2ß, and PRZM3 Simulations Compared with Measured Atrazine Runoff

^a USDA-ARS, U.S. Salinity Lab, Soil Physics and Pesticide Research Unit, 450 West Big Springs Rd., Riverside, CA 92507 USA
^b Dep. of Crop and Soil Sciences, Univ. of Georgia, P.O. Box 748, Tifton, GA 31793-0748 USA
^c USDA-ARS Insect Biology and Pest Management Research Unit, Tifton, GA USA
^d USDA-ARS Southeast Watershed Research Unit, P.O. Box 748, Tifton, GA 31793-0748 USA
^e Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80524 USA
^f USDA-ARS National Grain Insect Research Lab., Brookings, SD 57006-9803 USA

jimhook{at}tifton.cpes.peachnet.edu

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
High-intensity storms that occur shortly after chemical application have the greatest potential to cause chemical runoff. We examined how effectively current chemical transport models GLEAMS, Opus, PRZM2ß, and PRZM3 could predict water runoff and runoff losses of atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine] under such conditions, as compared with observations from a controlled field runoff experiment. The experiment was conducted for 2 yr using simulated rainfall on two 14.6- by 42.7-m plots within a corn (Zea mays L.) field on Tifton loamy sand (fine-loamy, kaolinitic, thermic Plinthic Kandiudults) under conventional tillage practices. For each plot-year, atrazine was applied as surface spray immediately after planting and followed by a 50-mm, 2-h simulated rainfall 24 h later. A similar preapplication rainfall and four subsequent rainfalls during the growing season were also applied. Observed water runoff averaged 20% of the applied rainfall. Less runoff occurred from freshly tilled soil or under full canopy cover; more runoff occurred when nearly bare soil had crusted. Observed total seasonal atrazine runoff averaged 2.7% of that applied, with the first posttreatment event runoff averaging 89% of the total. GLEAMS, Opus, PRZM2ß and PRZM3 adequately predicted water runoff amounts, with normalized root mean square errors of 29, 29, 31, and 31%, respectively. GLEAMS and PRZM3 predicted atrazine concentrations in runoff within a factor of two of observed concentrations. PRZM2ß overpredicted atrazine concentrations. Opus adequately predicted atrazine concentrations in runoff when it was run with an equilibrium adsorption submodel, but significantly underestimated atrazine concentrations when it was run with a kinetic sorption submodel.

Abbreviations: GLEAMS, Groundwater Loading Effects of Agricultural Management Systems • Opus, An Integrated Simulation Model for Transport of Non-Point Source Pollutants at the Field Scale • PRZM, Pesticide Root Zone Model. PRZM2ß and PRZM3 are ß-test version 2 and version 3 of PRZM, respectively

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
PESTICIDE RUNOFF from agricultural fields is greatest when heavy rainfall occurs shortly after soil surface pesticide applications (Wauchope, 1978; Leonard, 1990; Squillace and Thurman, 1992). Although this kind of scenario is infrequently observed on any given site, storms that cause significant amounts of surface runoff are common in the southeast Coastal Plains and southern Piedmont. Given the extensive area that is treated with pesticides in this region, particularly during the months of March, April, and May, it is likely that a storm of sufficient magnitude to cause surface water runoff will occur on some fields that have recently been treated with pesticides.

Field studies of pesticide runoff typically depend on the occurrence of natural rainfall because simulated rainfall cannot be generated at hectare and larger scales. Micro-scale plots (microplots), typically 5 to 50 m², are commonly used with rainfall simulators. While valuable in specific pesticide transport studies, microplots are thought to overestimate pesticide runoff as compared with typical field studies. This is more likely due to the severe conditions often used in microplots (Wauchope et al., 1995; Wauchope and Burgoa, 1995). Moreover, microplots cannot adequately represent typical agricultural management practices and the major characteristics of a typical field. In the early 1990s, Coody et al. (1990), Coody and Lawrence (1994), developed a rainfall simulator for use on intermediate-scale plots, typically 500 m², sometimes referred to as "mesoplots." This equipment made it feasible for worst-case pesticide runoff studies at near field scale.

Perhaps the most useful role of mesoplot studies is to test chemical transport models for a variety of scenarios. Simulation models are efficient analytical tools to evaluate the effects of various agricultural management practices on surface and ground water quality. They are especially valuable for predicting system behavior under a wide range of conditions that may be economically or technically impossible to investigate by experiment. Two models, GLEAMS and PRZM, have been applied extensively for pesticide leaching predictions (Carsel et al., 1985, 1986; Leonard et al., 1987; Sauer et al., 1990; Willian et al., 1999; Zacharias and Heatwole, 1994; Malone et al., 1999); however, they have not been adequately evaluated for severe or "worst-case" scenarios. Applications of Opus are rather recent, with a focus on water and chemical leaching (Smith, 1995). Our objective was to evaluate the performance of GLEAMS (version 2.10), Opus (version 1.9), PRZM2ß, and PRZM3 models for predicting atrazine runoff under severe rainfall conditions. Predictions of surface water runoff by GLEAMS, Opus, and PRZM2 were reported earlier (Ma et al., 1998). They were included here to compare with those predicted by PRZM2ß and PRZM3. We included surface water runoff also because atrazine runoff is closely related to water runoff.

	Model description

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
Detailed descriptions of GLEAMS, Opus, PRZM2ß, and PRZM3 models have been presented elsewhere (Carsel et al., 1998; Leonard et al., 1987; Smith, 1992). Briefly, GLEAMS estimates surface water runoff on the basis of the Soil Conservation Services (SCS) curve number method driven by daily rainfall, with modifications that relate the curve number to daily soil water content in the root zone (Williams and Nicks, 1982). The PRZM2ß and PRZM3 models also use curve number method, but relate the curve number to soil moisture limits in the surface zone (top 0.3 m) (Haith, 1979). The soil moisture limits are calculated from soil water content in the surface zone and are simplified to match the three antecedent soil moisture conditions based on 5-d antecedent rainfall by SCS (USDA-SCS, 1972). In these associations, a 1-cm difference in soil water storage is assumed among the three soil moisture limits. In Opus, the user can select an infiltration-based runoff model which requires detailed breakpoint rainfall input, or a modification of the SCS curve number method as that used in GLEAMS (Williams and Nicks, 1982; Williams et al., 1984). We used the daily hydrology option.

GLEAMS, PRZM2ß, and PRZM3 use a "tipping bucket" approach for calculation of water flow downward in the soil profile, which is based on soil layer field capacity (33 kPa suction), wilting point (1500 kPa suction), and saturation point. Opus simulates water retention and hydraulic characteristics using a modification of the Brooks-Corey functions (Brooks and Corey, 1964; Smith, 1992).

All four models assume first-order decay for pesticide dissipation. GLEAMS and PRZM2ß ignore the effects of temperature and soil water content on pesticide decay. PRZM3 uses a Q₁₀ parameter, which is the increase of degradation rate with 10°C increase in temperature, to describe temperature-dependent degradation. Also, both PRZM2ß and PRZM3 can simulate pesticide biodegradation using a model developed by Soulas (1982). However, there is not much evidence that this biodegradation model has been frequently used because of the extensive data requirements. Opus uses the pesticide degradation model developed by Walker (1974) to describe temperature- and moisture-dependent degradation. Opus also accounts for the effect of microbial activity on pesticide decay by assuming that the decay rate below the microbial-active zone (about 0.2 m) is a user-defined fraction of the surface soil decay rate.

All models assume that pesticide adsorption follows a linear, instantaneous equilibrium adsorption isotherm described by:

(1)

where C_l (mg L^-1) and C_s (mg kg^-1) are pesticide concentrations in solution and on adsorbed phase, respectively. K_d (L kg^-1) is the pesticide equilibrium adsorption coefficient, which is chemical- and site-dependent. K_d is often related to K_oc (L kg^-1), the normalized equilibrium adsorption constant on soil organic C content, which is primarily chemical-dependent (Chiou et al., 1979).

Opus has an option to describe pesticide sorption by a kinetic sorption submodel, which assumes that pesticide concentrations in the adsorbed phase change with time in proportion to the extent of the non-equilibrium. This process is described by:

(2)

where t (h) is time, and (h^-1) is a kinetic sorption rate coefficient. Note that this kinetic sorption model is different from a two-site or bi-continuum sorption model in which a fraction of the sorption sites is in instantaneous equilibrium while the rest of the sites are in kinetics (Rao et al., 1979; Brusseau and Rao, 1991; Ma et al., 1995; Ahuja et al., 1996).

In GLEAMS, PRZM2ß, and PRZM3, the amount of pesticide runoff is calculated from water runoff volumes, empirical extraction coefficients, and pesticide concentrations within the surface layer of the soil, assuming a fixed mixing depth at the soil surface. For GLEAMS, the mixing depth is 10 mm. For PRZM2ß, it is the topmost, user-defined soil layer, usually 10 mm or less. For PRZM3, a nonuniform mixing submodel (Ahuja, 1986) is used in which the mixing depth is 20 mm and the extent of mixing decreases exponentially with depth. Opus uses an effective mixing depth which is a user-defined fraction of the top 10-mm soil layer.

The crop growth models employed by GLEAMS, Opus, PRZM2ß, and PRZM3 are empirical in nature. Processes such as major management practices and tillage operations are explicitly defined in GLEAMS and Opus. While in PRZM2ß and PRZM3, optional "special actions" allow for users to assign new values to some time-dependent variables for a specified period of time. These variables include runoff curve number, soil bulk density, and pesticide adsorption coefficient. This feature of PRZM2ß and PRZM3 can be used to calibrate the effects of management practices and soil surface cover on model variables.

	Materials and methods

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
Field Experiment and Runoff Collection
Detailed description of the field experimental design has been reported elsewhere (Sumner et al., 1996; Ma et al., 1998; Wauchope et al., 1999). Briefly, two 14.6- by 42.7-m plots (A and B) were defined within a field in 1992. Tillage and corn management followed normal practices for the region. The field was disked twice to a 15-cm depth in January; deep-turned to 30 cm with a moldboard plow, and the beds shaped with a light harrow in April; see Wauchope et al. (1999) for further details. Each plot had eight 1.8-m wide beds divided by wheel tracks. Each bed had two rows of corn 0.9 m apart. Plots were separated from each other and from the outside areas by 18 m with soil preparation and corn management as within the plots. The soil was Tifton loamy sand with 3% of slope parallel to the rows.

Soil properties were obtained from 24 pedon samples taken along four down-slope transects of the plots. Saturated hydraulic conductivity was measured with undisturbed cores (60-mm i.d., 89-mm height) by the constant head method of Klute and Dirksen (1986), with the modifications suggested by Hill and King (1982). These cores were then used for measuring soil bulk density and water retention characteristics. Soil water content at 33 and 1500 kPa suctions were measured in pressure chambers from loose soil. Particle size distribution and organic carbon content were determined by the methods of Day (1965) and Walkley-Black (Nelson and Sommers, 1982), respectively. These values are summarized in Table 1 .

View larger version (24K): [in this window] [in a new window]   Fig. 1 Daily natural rainfalls for the experimental site for 1992 and 1993

Values for soil organic carbon sorption constant for atrazine (100 L kg^-1), and atrazine dissipation half-life in the field soil (60 d) were obtained from a previous study on the site (Ma et al., 1996). The activation energy (34.6 kJ mol^-1) required by Opus was obtained from Rocha and Walker (1995).

Sensitivity Analysis
Sensitivity analysis in the current study is focused on the effective mixing depth for chemical transfer to surface runoff and the kinetic sorption rate coefficient. These parameters are difficult to measure but may be important for accurate runoff predictions. Sensitivity of the model predictions to soil hydraulic parameters was presented by Smith (1993) for Opus for a set of assumed scenarios and by Ma et al. (1998) for GLEAMS, Opus and PRZM2 using the same hydrology data as used in this study. Therefore, they are not repeated here. Briefly, runoff predictions by all models were very sensitive to curve number. In addition, runoff predictions by GLEAMS and PRZM were also sensitive to soil water contents at field capacity and wilting point. The geometric mean and arithmetic mean of the measured K_s were compared using the Opus model to examine their influences on runoff predictions, as both means were reported in the literature.

Criteria for Goodness of Fit
The normalized root mean square error (Loague and Green, 1991), which counts errors of both overpredictions and underpredictions, was used for evaluations of surface water runoff. A paired t-test was also used where applicable since this test does not require an equal variance assumption. We used the commonly accepted criterion proposed by Parrish and Smith (1990), that acceptable model predictions should be within a factor of two of the measurements, for atrazine runoff evaluations. This criterion was originally proposed for evaluating model predictions for chemical concentration profiles in soils, but it is equally applicable for comparing pesticide concentrations in runoff. We accepted a 0.05 level of significance unless otherwise specified.

	Results and discussion

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
Measured and Predicted Surface Water Runoff
Measured surface water runoff from all 24 runoff events for the four plot-years, reported previously by Ma et al. (1998), was compared (Fig. 2) . The runoff observed at different times over the seasons reflects the effects of tillage operations, surface crusts and seals, corn canopy, and rainfall and runoff history on runoff generation. Measured water runoff averaged 20% of the applied rainfall overall. Events 1 and 2 generated less runoff because these events occurred on freshly tilled soil with a high infiltration rate. Events 3 and 4 had high runoff because the soil was crusted, greatly reducing infiltration rate. Runoff decreased for Events 5 and 6 because the growing corn canopy increasingly intercepted rainfall and caused "stem flow."

View larger version (62K): [in this window] [in a new window]   Fig. 2 Measured and predicted surface water runoff by GLEAMS, Opus, and PRZM3 models during 2 yr of corn growing seasons from two mesoplots on the Tifton loamy sand from 1992 to 1993

When individual event predictions are compared with observations, GLEAMS and Opus generally overpredicted runoff when measured runoff was low and underpredicted runoff when it was high. This is primarily a result of limitations imposed by the curve number method which lumps the most important factors controlling runoff, namely seasonal changes in canopy development and soil conditions. PRZM2ß and PRZM3 better predicted high and low runoff, when the `special actions' feature of the models was used to correct for the effect of canopy cover on runoff predictions. Without this correction, both PRZM2ß and PRZM3 predicted a very different runoff pattern over the season, as is shown in Fig. 3 for Plot A in 1992 and 1993. Similar results were obtained for other plot-years (not shown).

View larger version (41K): [in this window] [in a new window]   Fig. 3 Effects of the `special actions' options on surface water runoff predictions by PRZM3

Measured and Predicted Atrazine Concentrations and Loads in Runoff
Since all four models gave reasonable simulations of water runoff, we proceeded with comparison of atrazine runoff (Fig. 4) . Measured atrazine concentrations in runoff were above the analytical detection limit of 1.0 µg L^-1 for the first three runoff events after atrazine application, and only these events are used for atrazine comparisons. For runoff events which occurred 1 d after atrazine application (first post-treatment events), the average of the ratios of GLEAMS-predicted to measured atrazine concentrations in runoff was 0.85, indicating that GLEAMS reasonably predicted atrazine concentrations for these runoff events on the basis of the Parrish and Smith's "within 2x" criterion. For runoff events occurring later in the season, the ratio of GLEAMS-predicted to the measured atrazine concentrations in runoff varied between 0.15 and 1.73. Larger deviations occurred mostly during the second runoff event after atrazine application. The average ratio over all postapplication events was 0.72.

View larger version (38K): [in this window] [in a new window]   Fig. 4 Measured and predicted atrazine concentrations in runoff solution by GLEAMS, Opus, PRZM2ß and PRZM3 during 2 yr of corn growing seasons from two mesoplots on the Tifton loamy sand from 1992 to 1993

PRZM2ß and PRZM3 use very different methods for calculating chemical transfer to runoff and predicted different chemical concentrations in runoff. For PRZM2ß, the average of the ratios of predicted to the measured atrazine concentrations in runoff was 5.84 for first posttreatment events and the average was 6.72 over all runoff events. Thus, PRZM2ß generally overpredicted atrazine concentrations. Since the mixing depth for atrazine transfer to runoff was the same for GLEAMS and PRZM2ß and since both models adequately predicted surface water runoff, the overpredictions by PRZM2ß suggest that the extraction coefficient for the chemical transfer to runoff, a value that is fixed specifically for a group of chemicals in the model, is too large. A sensitivity analysis for PRZM2ß for the mixing depth demonstrated that the model would still significantly overpredict atrazine runoff when the mixing depth was reduced by 2-fold to 5.0 mm (not shown). This supports the conclusion that the model's default extraction coefficients were too large.

For PRZM3, the average of the ratios of predicted to the measured atrazine concentrations in runoff was 1.46 for the first posttreatment events and varied between 0.35 and 5.48 over all runoff events with an average of 1.73. Therefore, PRZM3 adequately predicted atrazine concentrations and the nonuniform mixing submodel employed in PRZM3 appears to have considerably improved predictions as compared with the uniform mixing submodel in PRZM2ß.

Measured seasonal atrazine runoff load averaged over the four plot-years was 2.7% of the amount applied. The maximum in any plot-year was 3.3%. These values are consistent with field-scale studies (Wauchope, 1978; Basta et al., 1997). Predicted seasonal loads were 2.1% by GLEAMS, 2.5% by Opus (with equilibrium adsorption), 15.0% by PRZM2ß, and 3.4% by PRZM3. When compared for individual runoff events between observations and model predictions, the errors in atrazine mass loss predictions are similar in trend to those in concentrations (Fig. 5) . Except for PRZM2ß, these are excellent results. Measured atrazine runoff from the first posttreatment events averaged 89% of the seasonal total. It is clear that agricultural management practices desired for controlling pesticide runoff losses should focus on these early runoff events. GLEAMS, Opus (with equilibrium adsorption), PRZM2ß, and PRZM3 predicted that 96, 85, 84, and 83% of the total atrazine runoff were in the first post-application events, respectively.

View larger version (34K): [in this window] [in a new window]   Fig. 5 Measured and predicted atrazine mass losses in runoff solution by GLEAMS, Opus, PRZM2ß and PRZM3 during 2 yr of corn growing seasons from two mesoplots on the Tifton loamy sand from 1992 to 1993

View larger version (45K): [in this window] [in a new window]   Fig. 6 Measured and Opus-predicted atrazine concentrations in runoff solution with equilibrium adsorption submodel as compared with uncalibrated and calibrated kinetic sorption submodel

	Summary and conclusions

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Model description Materials and methods Results and discussion Summary and conclusions REFERENCES

 
Research supported by USDA-CSREES-NRI.

¹ Simulations were also run for a previous version of PRZM, PRZM2 (Mullins et al., 1993). PRZM2 predicted different water runoff than did PRZM2ß (Ma et al., 1998), which essentially predicted the same runoff as PRZM3.

Received for publication June 21, 1999.

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