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Es i
$ so i * À Observed
X brinda
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1
TR, A
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cou TY T T T T Y. T T T T T T T T 1
Q0 20 40 60 80 1C0 120 14C i50 180 280
Time (3-h interval)
Figure 2. Simulated runoff hydrograph for the rainfall
event on 21-6-96
approach gave good results in respect of watershed response
against rainfall as compared to the lumped approach. The model
simulated the heterogeneity of catchment characteristics and
provided reasonable prediction, although the spatial distribution
of rainfall is only given by five recording raingauge stations.
Further, degree of spatial variability in a watershed can be
represented by number of unique combinations of soil type and
land use in the watershed but this methodology may fail if
different combinations of soil and land use result in virtually
equivalent curve numbers (Manguerra and Engel, 1998).
However, this problem can be eliminated by using the curve
number as the final measure of the watershed's spatial
variability.
For evaluation of grid size on the runoff depth, spatially
distributed curve numbers were generated for different grid
sizes of 23, 46, 92, 184, 368, 736 and 1472 m. and the resulting
runoff for these grid resolutions was shown as figure 3. From
the figure it is observed that the difference between simulated
and observed runoff increases with the increase of grid size
beyond 184 m. It may be observed that with increased grid
resolution, response of watershed to hydrological process tend
to be lumped. However, simulation of watershed with small grid
size is more complex as spatial variation of hydrological
parameters is high. Increasing grid size helps in simpler analysis
with increased assumptions at the cost of accuracy in the results.
Hence, sensitivity analysis of effect of grid size on runoff depth
is a complex phenomenon and needs to make balance between
computational time and accuracy. An analysis more detailed
than manual methods is possible using a GIS integrated with
distributed hydrological model offering crucial insight into
effects of cell size. Thus, the grid cell size should be chosen
such that the flow-path lengths in the drainage network are
closely approximated. The modelling approach is capable of
continuously simulating flow in distributed fashion for
analyzing the impact of land use changes and as well as climate
variability. Further, this model can be used to evaluate a
futuristic water availability scenario for an agricultural
watershed in eastern India.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
0s
055 -
OB. a
045 or
E os Resolution(m)
2 035 ay
$ o3. PU
5 225 une OY)
z 02 ma 184 |
0.15 + 388 :
od 788
di m 1472
Od : '
2 so 100 150
Rainfall depth (mm)
Figure 3. Effect of grid resolution on runoff coefficient
5. CONCLUSIONS
The main advantage of distributed modeling is that the spatial
variation of parameters is incorporated into the model response.
Runoff prediction is a major component of watershed
hydrologic modelling whether for resource conservation or
environmental protection. In the present study, the distributed
hydrological modelling approach considered the heterogeneity
of catchment characteristics and provided reasonable prediction,
although the spatial distribution of rainfall is only given by five
recording raingauge stations. Advances in continuous time,
distributed parameter hydrologic modeling as well as its
integration with Geographical Information Systems (GIS) have
led to the development of powerful tools for predicting runoff
from watersheds. Particularly, GIS allowed the combination of
remotely sensed data with spatial data forms such as
topography, soil maps, and hydrologic variables such as rainfall
distribution and soil moisture. This study has described the
importance of parameterization issues involved when predicting
watershed stream runoff.
6. REFERENCES
Abbott, MB, Bathurst, JC, Cung JA., O’Connell, PE,
Rasmusses, J., 1986. An introduction to the European
Hydrological System-Systeme. Hydrologique European SHE 2:
Structure of a physically based distributed modelling system. J.
Hydrolo, 87, 61-77.
Beven, KJ. 1985. Distributed Model, In: MG Anderson and TP
Burt (eds.), Hydrological Forecasting, Wiley
Beven, KJ,, Kirby, MJ., Schofield, N., Tagg AF., 1984. Testing
a physically based flood forecasting model (TOPMOEL) for
three UK catchments, J Hydrol. 69:119-143.
Arnold, JG., Williams, JR., Srinivasan, R., and King, KW.,
1995. SWAT: Soil Water Assessment Tool, Texas A&M
University, Texas Agricultural Experimental Station, Blackland
Research Center, 808 East Blackland Road, Temple, Texas.
Beasley, DB., Huggins, LF., and Monke, EJ., 1980.
ANSWERS: A Model for watershed planning. Transactions of
the ASAE. 23(4):938-944.
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