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Title
The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Author
Chen, Jun

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