Data Output Formats
The distributed modelling approach
adopted for the study was capable of estimating daily upstream flow at any
desired point of the drainage network. However, the model calibration required
flow to be predicted at the flow gauging locations in order to make comparison
with the historical flow records. Further, provisions were made in the model to
estimate composite flow values at the identified 32 sub catchments in UMCA. In
addition to the real time series of flow data generated from the model, it
provided the display facilities representing the spatial distribution of runoff
on thematic maps at any desired time period of interest.
Spatiotemporality in GIS
SPANS GIS menu driven functions can be run using
equivalent command mode codes. The advantage is that a series of SPANS functions
can be programmed into a batch file recognised as an audit file and run on the
command mode. Further, operating system (OS/2) commands also can be run on the
command mode. This provides a facility to handle iterative procedures very
efficiently.
In order to include the temporal dimension into hydrological
modelling, command mode functions were used extensively. The entire methodology
depends on the format and thematic details of the input data and map files.
Having prepared daily rainfall data in monthly tables with a column of data
series for each day, it was possible to use only one set of equations for a
month, incrementing the file pointer to read the data in different columns. One
equation file was designed for each year incorporating a series of monthly
equations.
In addition to the equation files, command files were required
to call the relevant equations for map modelling. The command filing system was
organised in such a way that each file contains executable files for each month.
The REXX procedures, the available programming language in OS/2 were set up so
that they could produce monthly values of weighted average of spatial
distribution of runoff at each subcatchment. They also created maps showing
spatial distribution of monthly runoff on the thematic scale according to the
user-defined classification scheme. Cumulative monthly totals of the other
hydrological parameters such as evaporation, interception and soil moisture were
also calculated whenever required.
Discussion And Conclusions
Limitations for Spatiotemporal Modelling in GIS
Hydrological
modelling efforts in GIS are generally hampered by the limitations of time
representation in spatial data structures. As such, it is not possible to
readily model the evolution through time of spatial variations in a phenomenon
with GIS and such variations are often needed in hydrology.
However, the continuous development of the conceptual framework
for spatiotemporal modelling confirms that the goal of fully functional temporal
GIS is close to realisation. Nevertheless, it was found that provisions are made
within the existing software architecture for the time varying modelling at
discrete temporal resolutions through iterative procedures. This study shows how
time dimension could be implicitly incorporated into the existing GIS modelling
algorithms in order to employ time variant modelling while maintaining the
integrated spatial dimension.
Model Performance
A comprehensive statistical evaluation was made to
compare the observed flow data with the simulated flow series of the modelling
exercise. The statistical summary of the modelling results is listed in Table
01. It is apparent that there is a great deal of agreement between the measured
and simulated flow time series.
Table 1. Statistical Summary of Spatiotemporal Modelling Results
| Sub CatchmentsStatistical Parameters |
Talawakele |
Kotmale |
Peradeniya |
Victoria |
Randenigala |
| Mean (mm) |
Measured |
105.12 |
148.14 |
163.11 |
89.51 |
119.8 |
| Runoff |
Simulated |
96.94 |
137.39 |
156.62 |
84.82 |
102.48 |
| STD (mm) |
Measured |
68.8 |
141.82 |
111.72 |
83.34 |
116.42 |
| Runoff |
Simulated |
88.69 |
129.18 |
131.88 |
83.62 |
119.59 |
| Coefficient of Determination |
0.84 |
0.92 |
0.83 |
0.90 |
0.96 |
| Cross Correlation Coefficient |
0.71 |
0.84 |
0.69 |
0.81 |
0.92 |
| Lag 01 Correlation |
0.49 |
0.23 |
0.22 |
0.33 |
0.46 |
| Coefficient of Efficiency |
0.15 |
0.69 |
0.22 |
0.62 |
0.82 |
| Residual Mass Curve Cof. |
-0.40 |
-1.20 |
0.21 |
0.32 |
0.35 |
In addition, the sensitivity of the model for the defined
hydrological parameters, spatial resolution and land use changes were also
assessed. The model is obviously sensitive to land use changes in the catchment
and it shows 15 - 35% increase of annual runoff when forests are converted to
grasslands.
References
- Calder I. R. (1986). A Stochastic Model of Rainfall
Interception, Journal of Hydrology, Vol. 89: pp 65-71.
- Gunawardene, E.R.N. (1996). Approximations for Fog
Interception, UP-OFI Project Report, University of Peradeniya, Sri Lanka.
- Roberts G. & Harding, R.J. (1996). The Use of Simple
Process Based Models in the Estimate of Water Balances for Mixed Land Use
Catchments in East Africa, Journal of Hydrology, Vol. 180, pp. 251-2