192 A. Huber et al. Agriculture, Ecosystems and Environment 80 2000 191–204
1997; Fenelon and Moore, 1998; Frede et al., 1998. Nevertheless it is evident that experimental findings
cannot be easily extrapolated to unmonitored sites with different soils, climate or application practices.
In the past few years an increasing number of studies dealt with the creation of regional scale maps of pesti-
cide migration risk, mostly leaching potential Petach et al., 1991; Loague et al., 1996; Tiktak et al., 1996;
Zhang et al., 1996; Diaz-Diaz et al., 1998; Soutter and Musy, 1998. Regional scale pesticide loss via surface
runoff has been studied to a lesser extent than leach- ing, e.g. Mizgalewicz and Maidment 1996 estimate
the atrazine transport in Iowa-Cedar River basin us- ing discharge concentrations regressions. At present,
no reports about regional scale assessment of pesti- cide input via spray drift are known to the authors of
this paper.
In this study an approach was developed to estimate regional importance of tile drains, surface runoff and
spray drift to total non-point source pollution of sur- face waters with pesticides in Germany. Pesticide loss
on each pathway was estimated separately for the en- tire country based on models which account for spatial
variability of governing factors for pesticide transport to surface water. The study does not deal with risk as-
sessments by basing model assumptions on the worst possible case of pollution but aims to depict the actual
situation instead.
2. Materials and methods
2.1. Pesticide application data A crucial parameter of any model applied in this
study is the amount of active ingredient sprayed on a crop at a specific growth stage. Current geographic in-
formation system GIS technology offers the oppor- tunity to provide regionally differentiated data sets on
pesticide use and therefore enables the user to model non-point loss of an active ingredient under those soil,
climatic and crop conditions under which it is really applied. To determine typical application periods in
different regions, all available plant protection rec- ommendations published by the local branches of the
agricultural extension services were evaluated with regard to recommended spraying times for the most
important crops. Varying application periods due to climatic differences were also considered with apple
trees in blossom as an indicator for growth of crop cover and beginning of herbicide treatments in spring.
Fig. 1 shows the development of crop cover in cool and warm climate zones as assumed in this study to-
gether with the typical length of application period ac- cording to spraying recommendations. This temporal
frame was then linked with the results of a representa- tive market survey among 3500 farmers on the use of
active ingredients in German agriculture in 19931994 Produkt and Markt, 1997.
CORINE-Land-Cover data Statistisches Bunde- samt, 1997 and results of agricultural census at
the community level Statistische Landesaemter, 1992–1995 were taken to enhance the spatial reso-
lution of the application pattern Huber et al., 1998. Beside mean application rates in each federal state,
application probability is an essential parameter to consider both the market position of an active in-
gredient and the intensity of infestation. Hence, for the 42 active ingredients most sold in Germany 1994
their application probabilities to field crops, vineyards and orchards were calculated on 11 reference dates
of treatment. The application probabilities are based on the relationship between the area treated with a
pesticide in a determined period and the total area cropped with the target culture in each state.
2.2. Modeling pesticide leaching and loss via tile drains
2.2.1. Leaching model Pesticide leaching is a complex and long-lasting
process which requires the application of a mech- anistic simulation model to account for unsaturated
water flow as well as for sorption, transformation and volatilization of the compound after application. Ev-
ery model run requires detailed data about soil profile, weather conditions after application and the pesticide
itself. In the scale of this study no such information was available for every raster cell 100 m×100 m used
in the modeling. Chemical transport with percolation water was therefore calculated for unique factor com-
binations which were considered to be representative for German agriculture.
In this study PELMO PEsticide Leaching MOdel; Klein, 1995 was used to conduct 31,360 simula-
tion runs for different soils and climate character-
A. Huber et al. Agriculture, Ecosystems and Environment 80 2000 191–204 193
Fig. 1. Crop cover and length of pesticide application periods in cool A and warm B climate zone as assumed in the study.
194 A. Huber et al. Agriculture, Ecosystems and Environment 80 2000 191–204
istics, factor combinations of chemical properties of active ingredients, application days, target crops
grain, row crops, soils sand, organic carbon con- tent OC=10 g kg
− 1
; sandy loam, OC=7 g kg
− 1
; loam, OC=14 g kg
− 1
; sandy loam, OC=24 g kg
− 1
; silt loam, OC=14 g kg
− 1
and climate characteristics eight weather records with mean precipitation from
542 to 1181 mm and mean annual temperature from 6 to 10.4
◦
C. Each run started with an application of 1 kg ha
− 1
on one out of eleven application days and applying a full year weather record with daily precip-
itation and temperature data. Chemical leaching was then calculated until the concentration of the original
active ingredient had fallen below an arbitrarily cho- sen limit of 0.01 mg l
− 1
at 0.8 m soil depth. At the end of each run pesticide loads given as fraction of ap-
plication rate leached below 0.8 m were summarized together with annual percolation volumes. A detailed
description of the leaching model is given in Huber et al. 1999.
2.2.2. Linkage between simulation results and spatial information
To regionalize simulation results, regression func- tions were derived between PELMO-results and in-
put parameters of leaching scenarios grain, r
2
= 0.88;
row crops, r
2
= 0.87. The set of input parameters in-
cluded spatially distributed data such as mean annual percolation rates, soil types and regional application
periods. Pesticide properties, i.e., sorption coefficient and soil half-life, were assumed to be spatially con-
stant. Application rates were corrected for treatment probabilities and real doses according to the plant pro-
tection data base and crop cover Fig. 1. No foliar washoff was considered in this study because pesticide
degradation on plant surface is very fast and hence it was assumed that foliar washoff would not contribute
to a considerable extent to total leaching loss. Pesti- cide mass leached below 0.8 m was then assumed to
enter tile drains, and thus to be transported to the sur- face water system depending upon tile drain density
in a specific raster cell.
While a fairly good data set on tile drain density is available for former East Germany IGB, 1998 an
extensive survey among local branches of agricultural extension service was conducted in the Western part
of the country to compile a map of average tile drain density in agroecological zones. Agroecological zones
Bundesamt fuer Naturschutz, 1997 were chosen as the area units because most of the relevant landscape
parameters integrated within the concept of agroeco- logical zoning like occurrence of poorly drained soils,
intensity of plant production, mean crop yields, land- scape morphology may also be seen as determining
factors for tile drain density. It is important to note that occurrence of soils with certain hydrologic prop-
erties alone does not allow for conclusions about tile drain densities as most of the arable land in Germany
is owned by small farmers who are strongly influ- enced by economic factors when they decide whether
to drain a field or not.
2.3. Modeling pesticide loss via surface runoff Calculation of runoff losses include neither pesti-
cide residues nor foliar washoff. For model simpli- fication it is assumed that pesticides on foliage are
resistant against washoff Wauchope and Leonhard, 1980; Willis and McDowell, 1987 and dissipate
rapidly after application respectively Burgoa and Wauchope, 1995. Unlike chemical transport with
percolation water, pesticide loss with surface water is highly event-specific. Thus storm timing is the
critical determinant of pesticide runoff losses. The time between application and a significant rainfall
event is random in contrast to other parameters like application day, applied dose, and crop cover which
are either known or assumed to be constant such as pesticide half-life. That means, the time between ap-
plication and runoff event can only be described in a stochastic way. A frequently used probability dis-
tribution for time between significant rainfalls is the Gumbel function Gumbel, 1958.
T
v
= exp
v
e
− uw
1 where T
v
is mean return period for a rainfall event exceeding a given precipitation volume v
e
. The German Meteorological Service DWD, 1996 provi-
ded nationwide datasets with parameters u and w for various rainstorm durations in a 10 km×10 km grid.
In this study u and w have been fitted for 24 h events. Based on the average length of time between sig-
nificant rainfalls Fig. 2, Mills and Leonard 1984 developed a probability density function for the
amount of pesticide c available for transport at the
A. Huber et al. Agriculture, Ecosystems and Environment 80 2000 191–204 195
Fig. 2. Probability density function for time between significant rainfalls for tree different locations in Germany data source: DWD,
1996.
time when runoff begins. The area under the function represents probabilities of occurrence. Integration and
solution for mean probability p=0.5 yield
c = 0.5
βα
c 2
where c is the initial amount of pesticide that is sus-
ceptible to runoff, i.e. is applied to soil surface, α the reciprocal of T
v
and β the breakdown coefficient which depends upon pesticide half-life Dt
50
. β =
ln 2 Dt
50
3
Table 1 Basic drift values used for risk assessment in German pesticide registration procedures Ganzelmeier et al., 1995
Distance from field edge m Spraydrift deposition of application rate
Field crops boom Tall growing crops air blast sprayer application
sprayer application Grapevine
Grapevine Fruit trees
Fruit trees early stage
a
late stage
a
early stage
a
late stage
a
1 1.39
b
2 0.58
c
3 0.41
2.39 4.99
18.16 9.18
5 0.24
0.75 2.68
12.02 4.92
10 0.11
0.21 0.91
5.74 1.84
20 0.04
0.06 0.24
1.90 0.51
30 0.02
0.03 0.11
0.89 0.22
a
Earlylate stage: growth stage tillafter blossom time.
b
Application with a dosage of 1 kg ha
− 1
to a field crop results in a mean spraydrift deposition of 1.39 ≈ 1.4 mg m
− 2
on the surface of an adjacent water body with 1 m distance from edge of field
c
Values indicated in italics were used in this study.
Chemical transfer to surface runoff was estimated following the approach of GLEAMS Leonard et al.,
1987, where a functional relationship between dis- tribution coefficient K
d
and an empirical extraction coefficient was developed. Computation of runoff
volume was based on the SCS-curve number method McCuen, 1981 which was modified for Central
European conditions by Lutz 1984. A detailed de- scription of the derivation of the runoff model is given
in Huber et al. 1998.
2.4. Modeling pesticide loss to surface waters via spray drift
The extent to which active ingredients are deposited beyond the boundaries of the treated fields depends
upon spraying equipment, meteorological conditions and growth stage of the crop. Basic drift tables have
been developed in Germany Table 1; Ganzelmeier et al., 1995 facilitating estimates of spray drift losses
for boom sprayer applications to field crops and for air blast applications to vineyards and orchards aircraft
application is not relevant in Germany at different leaf stages and for various distances between the edge
of field and the deposition area. The drift values are based on field trials at 25
◦
C, and a maximum wind speed of 5 m s
− 1
. Applying mean drift values to all treatments, the
contribution of spray drift to surface water pollution
196 A. Huber et al. Agriculture, Ecosystems and Environment 80 2000 191–204
would only be dependent upon application rates. How- ever, the probability of occurrence of watercourses at
field edge is the decisive factor with regard to the re- gional importance of spray drift as a pathway for pes-
ticide input into surface water. In general frequency of surface waters per unit area can be described by
drainage density in km km
− 2
Horton, 1932. In this study drainage density was first computed on the ba-
sis of the digital surface water net 1:200.000; HAD, 1997 using GIS techniques. Due to cartographic gen-
eralization the digital surface water net is represented incompletely, therefore real values were extrapolated
by regression analysis taking measured drainage den- sities in 147 different agroecological zones Huber,
1998.
2.5. Other non-point sources of surface water contamination with pesticides
Some non-point sources were not modeled because they are not considered to be relevant on a national
scale such as atmospheric deposition or wind erosion. For the following two reasons sediment-bound pesti-
cide loss was also neglected: 1. The spatial databases required for erosion calcula-
tion are not available for Germany, so modeling of pesticide input is limited to the dissolved phase.
2. With Germany’s prevailing weather, soil and land use conditions, surface runoff occurs frequently
causing no or hardly any soil erosion, e.g. 5 to 15 times per year according to model calculations.
Heavy rainstorms including higher erosion only oc- cur once or twice per year, mainly effecting row
crops. In this respect the situation differs signifi- cantly from the ones in other countries, especially
in North America. Thus pesticide transport in so- lute phase is assumed to be the dominant runoff
component.
Burgoa and Wauchope 1995 also stated that sed- iment constitutes such a small fraction of runoff fol-
lowing even highly erosive storms, that it is likely that the bulk of active ingredient will be lost in the water
phase.
Some authors reported a notable pesticide flux from groundwater to surface water Squillace and Thurman,
1992. However, concentrations in groundwater are generally very low Leistra and Boesten, 1989. In
this study it was therefore not assumed that pesticide concentrations in surface water, which are usually
higher than those in groundwater, are caused to a considerable extent by groundwater discharge.
3. Results and discussion
