Regional Analyses of Pesticide Runoff from Turf

Chapter 12

Regional Analyses of Pesticide Runoff from Turf 1

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Downloaded by UNIV OF GUELPH LIBRARY on June 29, 2012 | http://pubs.acs.org Publication Date: September 12, 2008 | doi: 10.1021/bk-2008-0997.ch012

Douglas A . Haith , Matthew W . Duffany , and Antoni M a g r i

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Biological and Environmental Engineering, Cornell University, Riley-Robb Hall, Ithaca, NY 14853 New York State Department of Environmental Conservation, 317 Washington Street, Watertown, NY 13601-3787 ESRI, 380 New York Street, Redlands, C A 92373-8100 2

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Pesticide runoff loads from turf can vary dramatically with chemical properties and application regime, geographic location, irrigation rates and turf surface. Given the limited availability of field data, it is difficult to realistically consider the range of these variations in exposure assessments. The TurfPQ pesticide runoff model was combined with several other models and data bases to provide a general framework for efficient estimation of turf pesticide runoff loads on both a yearly and daily basis. The process was used to investigate differences in MCPP, fenarimol, iprodione and carbaryl runoff from fairways at four U.S. locations with widely differing climatic regions. Factors which accounted for the observed differences included pesticide properties and application amounts, irrigation applications and growing season runoff. The simulations indicated that runoff loads of a particular pesticide could vary by as much as an order of magnitude among the locations.

© 2008 American Chemical Society In The Fate of Nutrients and Pesticides in the Urban Environment; Nett, M., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2008.

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204 One of the significant difficulties in managing the environmental impacts of turf pesticide runoff is the immense variability in transport and fate characteristics. One pesticide may be easily washed from grass surfaces by small amounts of runoff while another resists movement, even with extreme storms. Some chemicals persist in the turf and soil for months while others are degraded within days or even hours. These variations are further compounded by differences in weather patterns between geographic locations. As a result, a program for controlling the runoff of one pesticide at one site is not likely to be adequate for another chemical and site. A classic approach for elucidating such differences is through controlled field experiments. Given the large number of available turf pesticides and the many different weather regimes seen in an area as large as the continental U.S., this approach has limited practicality. Fortunately, many of its features can be duplicated in simulation experiments. Mathematical models are used to describe weather and runoff, and the effects of a variety of site conditions and management options can be efficiently evaluated. Nevertheless, simulation experiments of pesticide runoff are challenging. Available models often require many input parameters whose values are difficult to estimate. Information on the rates and timing of pesticide applications for a particular location may be particularly difficult to obtain. The research described herein had two objectives. The first was to develop a general protocol for simulation studies of pesticide runoff from turf. The protocol is built around the TurfPQ pesticide runoff model (1,2), and U S C L I M A T E weather generator (3), but the methods should be applicable to other models as well. The second objective was to demonstrate the protocol through a simulation experiment designed to study the regional differences in runoff of several pesticides applied to fairways.

Simulation Protocol A simulation protocol consists of the design of the simulation experiment's scenario (pesticide selection, site description, length of simulation run), the specification of appropriate models, estimation of input parameters, and selection of methods for summarizing and interpreting results.

Scenario Four pesticides were simulated: the herbicide M C P P (2-(2-Methyl-4chlorophenoxy) propionic acid), two fungicides, fenarimol (a-(2-Chlorophenyl)a-(4-chlorophenyl)-5-pyridinemethanol) and iprodione (3-(3,5-Dichlorophenyl)-

In The Fate of Nutrients and Pesticides in the Urban Environment; Nett, M., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2008.

205 N-(l-methylethyl)-2,4-dioxo-l-Imidazolidinecarboxamide), and the insecticide carbaryl (1-Naphthyl-N-methylcarbamate). The sites are identical, hypothetical golf fairways in Atlanta, G A ; Fresno, C A ; Madison, WI; and Olympia, W A . Weather characteristics for these sites are given in Table I. Temperatures and precipitation are 1971-2000 means (4). Growing seasons are based on median freeze/frost dates (5).


Table I. Weather Characteristics of Simulation Sites

Location Atlanta, G A Fresno, C A Madison, WI Olympia, W A

Annual Temperature (°C) 16 17 7 10

Annual Precipitation (mm) 1290 270 785 1285

Growing Season Apr-Oct Mar-Nov May-Sep May-Oct

It can be seen from Table I that the four sites have substantially different weather characteristics. Atlanta and Fresno both have warm climates, but Fresno is much drier and would require significant irrigation to maintain turf surfaces. Madison and Olympia are cooler and have shorter growing seasons than the other cities. Although Olympia's annual precipitation is comparable to Atlanta's, it is differently distributed. Atlanta precipitation is relatively uniformly distributed throughout the year, but Olympia has little growing season moisture. Unlike field experiments, simulations can be of any duration. It is typically as easy to make 500-year runs as 5-year ones. In general, runs should be long enough to provide reliable estimates of the phenomena of interest. In the current study, regional differences were evaluated by comparison of annual and monthly means and 1 in 10-year extreme events, and these variables could be reasonably estimated from 100 years of daily results. This does not imply that the experiments modeled 100 years of fairway operations. Rather, the 100-year run should be interpreted as producing 100 different estimates of one-year of pesticide runoff.

Simulation Models The TurfPQ model computes Runoff volume Pesticide in turf

model was used in this study to simulate pesticide runoff. The water and chemical mass balances on a one-day time step. is determined through a modified curve number equation. foliage and thatch is partitioned into adsorbed and dissolved



206 components which are assumed to be decayed in a first order biodegradation process. In addition to decay, dissolved pesticide is removed from the system by runoff or leaching into the soil. Volatilization is neglected. In addition to daily precipitation and temperatures and pesticide application rates, the model requires four input parameters - biodegradation half-life, organic carbon partition coefficient, runoff curve number, and organic carbon content of the turf. In a validation study of 52 runoff events in four states involving 6 pesticides, TurfPQ explained 65% of the observed variation in pesticide runoff. Mean predicted pesticide runoff was 2.9% of application, compared to a mean observation of 2.1% (7,2). The U S C L I M A T E software package, which was used to generate daily weather data for the TurfPQ model, produces daily precipitation, minimum and maximum air temperatures and a solar radiation record for arbitrary userspecified locations in the continental U.S. Precipitation is based on a Markov chain of occurrence (wet/dry days) and a mixed exponential distribution for precipitation amount. Temperatures are described by an autocorrelation model conditioned on wet or dry days. The generated weather data are processed in several ways to produce the daily records of precipitation and temperatures required by TurfPQ. Solar radiation data are discarded and the software's March to April sequences are converted to January to December. Daily temperatures are obtained by averaging the minimum and maximum temperatures.

Input Data

Weather Depending on the nature of the site, the weather records may be further modified to reflect the addition of irrigation. This would generally be the case for golf course turfs. In this study, irrigation was based on comparison of 3-day cumulative precipitation and potential evapotranspiration during the growing season. Whenever the 3-day precipitation is exceeded by 3-day potential evapotranspiration as computed by the Hamon equation (6), irrigation is added to make up the deficit. This produces a new weather record in which precipitation entries for any day are replaced by precipitation plus irrigation.

Turf Properties Turf properties required for the simulations are runoff curve number for average antecedent moisture conditions (CN2) and the organic carbon content of


207 the grass and thatch. Both of these parameters depend on grass height and thatch thickness, which were assumed to be 11 and 8 mm, respectively, as in Haith and Rossi (7). Using the procedures given in Haith (1), these values produce a curve number of 67 and organic carbon content of 10,200 kg/ha. The curve number selection also assumes a hydrologic group C (relatively poor drained) soil.


Pesticide Characteristics The two pesticide properties required by TurfPQ, bio-degradation half-life and partition coefficient, are relatively easily obtained. The partition coefficient is computed from turf organic carbon content and K , the organic carbon partition coefficient. Half-lives and K values are available from general databases (8,9,10). Application amounts and timing are also required for the simulations, and these can be quite difficult to obtain. Although application rates are specified by labels (11), a wide range is often given, corresponding to use against different pests. Because it is likely that the chemicals will typically be used against a variety of pests, the median or mid-range label value, converted to g/ha of active ingredients, was used in the simulations. Timing, or frequency of applications, is less straightforward. Publicly available application records are very rare, and we know of no general databases. In the absence of other information, we based simulation applications on label suggestions of prophylactic applications at regular intervals to control multiple pests. These applications will almost certainly be more frequent than those used by many turf managers, particularly those following integrated pest management programs. The major determinants were pesticide type (herbicide, fungicide, insecticide), growing season, as shown in Table I, and application intervals and annual or seasonal limits specified by the labels. Generally, longer growing seasons result in more applications of a pesticide, unless limited by label. Herbicides are divided into pre-emergent and post-emergent. The former is applied as a single application on the first day of the growing season. Postemergent herbicides such as M C P P are assumed to be applied in the middle of each of the first two months of the growing season and once in the last or next to last month of growing season if allowed by the label. Fungicide applications were based on preventative control of diseases such as dollar spot, summer patch, brown patch, and leaf spot. Applications were generally started in the middle of the second growing month, and if permitted by label, continued every 15 days through the middle of the next to last growing month. Otherwise, label limits applied, as was the case with fenarimol, which was applied every 30 days. As with fungicides, repetitive preventive applications are assumed for insecticides, which are used to control a range of pests (grubs, chinch bugs, o c

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208 cutworms, webworms, billbugs) which occur mainly in late Spring and Summer. For insecticides such as carbaryl, this suggests a mid-month application starting in the second growing season month and continuing through September. Pesticide properties, rates and application frequencies for the four simulated chemicals are given in Tables II and III.


Table II. Pesticide Properties and Application Rates

Pesticide MCPP Fenarimol Iprodione Carbaryl

Rate per Application (g/ha) 860 760 4580 8000

Half-Life (d) 10 840 50 17

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^ (cm /g) 20 760 670 290

Application rates and frequencies differ markedly for these four chemicals. For example, the total annual pesticide application for the Atlanta site ranges from 2580 g/ha for M C P P to 40,000 g/ha for carbaryl. Application frequency is lowest for Madison because of its short growing season. This produces much lower inputs of the 2 fungicides than seen at the other sites. The large number of fungicide applications for Fresno may seem inconsistent with its dry climate, which would not typically favor plant diseases. However, the regular irrigation inputs needed to maintain Fresno fairways produce the warm, humid conditions required for disease development. The pesticides differ markedly in their persistence and adsorption characteristics (half-lives and K ) . M C P P is an ephemeral chemical that is only weakly adsorbed, and unlikely to remain long in the turf. Carbaryl is similarly short-lived, but more strongly adsorbed and thus less readily leached. Both fungicides are relatively strongly adsorbed, and fenarimol is very long-lived. oc

Table III. Pesticide Application Frequency Pesticide MCPP Fenarimol Iprodione Carbaryl

Atlanta 3 5 6 5

Number ofApplications Fresno Madison 3 3 7 3 6 3 6 4

Olympia 3 4 5 4


209 Organization of Results


Each simulation experiment produces 100 years of daily estimates of water volumes and pesticide mass loads in fairway runoff. The information was summarized by annual and monthly means and by the annual maximum daily load ( A M D L ) of pesticide runoff. The A M D L is the largest one-day runoff load produced in a year. The 100 values of A M D L s are then used to assign return periods to these extreme event. Thus the 1 in 10 year A M D L would be expected to be exceeded on the average of once in 10 years, or 10 times in 100 years.

Simulation Results

Annual Water Balances Mean annual water inputs and runoff from the 100-year simulations are given in Table IV. Overall, regional differences in weather and hydrology for these sites are rather substantial. Runoff was minimal for Fresno because most water input was from the regular addition of moderate irrigation amounts rather than large precipitation events. Runoff was 3-4% of total water inputs at the other sites, and 40-50% of the runoff occurred during the growing seasons at Atlanta and Madison. Although Olympia had significant annual runoff, very little occurred during the growing season, when pesticides were being applied.

Table IV. Mean Annual Fairways Water Inputs and Runoff

Location Atlanta Fresno Madison Olympia

Precipitation Irrigation 1281 272 789 1304

435 771 307 330

Total —mm 1716 1043 1096 1634

Year Runoff

Growing Season Runoff

11 2 32 65

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Regional Analyses of Pesticide Runoff from Turf

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