ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
39 
re method of rapidly 
lally, the digital maps 
i other information in a 
ters for mathematical 
lion potential (Mark et. 
obert, 1993; Mark et. al. 
:kols et. al., 1996). 
on the hydrologic and 
idscape. Few models 
tions are capable of 
.ionality (John, 1991). 
lonstrate how terrain 
l pesticides pollution 
:tion capabilities and 
0 assemble the input 
f model was used to 
¡chniques. The terrain 
le runoff flow direction 
) photogrammetrically 
flow direction as an 
1 the pesticides runoff 
watershed (Latitude: 
-80°.995), which has 
itario, Canada (Fig.1). 
is 1,288 ha. The 
similar highly erodible 
¡tailed Kintore Creek 
(d in Table 1. Both 
sub-watersheds originate in swampy headlands that provide a 
year round source of water. Kintore Creek flows into the Middle 
Branch of the Thames River, which drains the corn belt of 
Ontario, before discharging into Lake St. Clair. It is one of most 
heavily used pesticides in the Great Lakes area. 
Table 1. Kintore Creek sub-watersheds characteristics 
Conventional 
tillage (West 
Kintore Creek) 
Conservation 
tillage (East 
Kintore Creek) 
Size of sub watershed 
6.61 sq. km 
6.42 sq. km 
Soil types 
silt loam 
silt loam, 
sandy loam & 
muck 
Soil erosion potential 
medium to high 
medium to 
high 
Area under study 
653 ha 
635 ha 
Area tile drained 
55 
30 
Total forest cover 
78 ha 
175 ha 
Total crop area 
473 ha 
333 ha 
In the western sub-watershed, landowners employed 
conservation techniques, which included the mulch-finishing of 
row crops, the planting of forage and cover crops, no-till and 
reduced till practices, the installation of sediment control basins, 
slope stabilization along stream banks, and tree planting. In the 
eastern sub-watershed, landowners used the conventional 
tillage practice of fall moldboard ploughing of a 
corn-wheat-alfalfa rotation. 
DATA DEVELOPMENT 
In this paper, color infrared airphotos purchased from the 
Information Center of Natural Resources Ontario, were used to 
generate a mosaic of color orthoimages and a DEM. The 
grid-DEM created in this study was used to provide basic input 
data for the pesticides losses runoff model. The several input 
parameters of pesticides model were derived by using desktop 
ArcView 3.1 GIS. A grid of flow accumulation was produced. 
Slope and aspect grids were also computed from DEM based 
on the terrain analysis and relative hydrological parameter 
determination methods (Baillard et. al., 1998, 2000). 
For the study of a paired watershed of Kintore Creek to examine 
the effects of farm conservation practices on pesticides 
transport to surface water was carried out by Environment 
Canada and the Upper Thames River Conservation Authority, 
some relative meteorology, agriculture and pesticides data 
were also obtained. 
Table 2. Parameters of the pesticides losses runoff model 
SCS curve number 
Surface condition constant 
Land slope 
Slope shape factor 
Field slope length 
Channel slope 
Channel sideslope 
Roughness coefficient 
Cropping factor 
Aspect 
Soil texture 
Gully source indicator 
Channel indicator 
Soil erodibility factor 
Practice factor 
These data are helpful to accompany with the derived 
parameters to input the pesticides losses runoff model for 
assessing the reliability and efficiency of the methods proposed 
in this paper. The pesticides losses runoff model used in this 
paper was developed for concerning the potential effects of 
pesticides pollution on surface water quality and quantitatively 
examining these effects. This model used a square grid cell 
system, which has 320 grid cells and each 280m by 400m 
(0.043 mi 2 ), to represent the spatial variability of catchment 
properties. About one-third of the input parameters required by 
the model are terrain-based and could be obtained directly or 
indirectly from remote sensing data (Table 2). In this paper, only 
some of these parameters, including cell numbers, cell 
connectivities, aspects (flow directions), land and channel 
slopes, slope lengths, slope shapes, and upslope contributing 
areas, were carried out by the terrain analysis for this project is 
an undergoing. For the conventional nonpoint sources pollution 
model, all terrain input data must be entered into the input file by 
hand, along with the other soil and land use data, which is a 
time consuming process. While, the advantage of GIS, here is 
the terrain analysis, are appeared. Therefore, some terrain 
analysis techniques were applied to obtain the important input 
parameters for the runoff model. 
Flow directions based on digital elevation models are needed in 
hydrology to determine the paths of water and pesticides 
residues movement. Two important distributed quantities that 
depend on flow directions are the upslope area and specific 
catchment area. Upslope area, A, is defined as the total 
catchment area above a point or short length of contour. The 
specific catchment area, a, is defined as the upslope area per 
unit width of contour, L, (a = AA.) (Moore et. al., 1991) and is a 
distributed quantity that has important hydrological, 
geomorphological and geological significance (Tarboton, 1997). 
The specific catchment area contributing to flow at any 
particular location is useful for determining relative saturation 
and generation of runoff from saturation excess in models such 
as Topmodel. Specific catchment area together with other 
topographic parameters has also been used in the analysis of 
processes such as erosion and landslides. Upslope area is 
commonly used for the automatic demarcation of channels 
relying on the notion of a critical support area. From the number 
of recent papers there is considerable hydrologic interest in the 
effect of grid scale and procedures for computation of specific 
catchment area. It is therefore important that flow directions and 
specific catchment areas be accurately determined free from 
grid artifacts. 
• Slope/Aspect 
The computation of slope/aspect for each surface cell was 
made from some number of neighboring elevation values in 
four or nine adjacent windows but was used as if it represents 
the surface angles for only the central cell. It is often assumed 
that the computed surface angles actually represent a cell size 
twice as large as the original grid cell (Hodgson, 1995). The 
most common algorithms use either four or eight of the 
neighbors in a three by three window centered on the cell in 
question (Fig. 2). When using all eight neighbors, variations in