1. SETTING UP OF AN EXPERIMENTAL BASIN AND DEVELOPMENT OF A CELL-BASED MODEL TO DERIVE DIRECT RUNOFF HYDROGRAPHS FOR UNGAUGED BASINS
J. M. P. B. Hunukumbura
Department of Civil Engineering,
University of Peradeniya,
Sri Lanka.
Supervised by Dr. S. B Weerakoon
March 02, 2006

2. Overview of the Presentation
Introduction
Objective of the study
Scope of the study
Data collection
Development of a new model for ungauged basins
Application of the developed model to the experimental basin
Compare the results of the new model
Conclusions
Recommendations

3. Need of stream flow prediction in ungauged basin
Introduction
Need of stream flow prediction in ungauged basin
Most of the river basins in the world are ungauged
Water resources development potentials with respect to water supply , hydropower and etc. exist mostly in ungauged river basins
Even for gauged basins, calibrated hydrological models using past data can not be expected to simulate the basin correctly at present as the basin hydrological properties have changed due to climate change, land use changes, etc

4. Available Methods for Stream Flow Prediction in Ungauged Basin
Using regional model parameters
Using synthetic unit hydrographs
Using the relationships between model parameters and basin physical properties
Using calibrated model parameters for similar gauged basin to the ungauged basin
Multiplying the measured stream flow of a similar gauged basin by area ratio and annual rainfall ratios

5. Objective of the Study
Collect reliable high resolution data to study on stream flow prediction in ungauged basin
Develop a new model to obtain direct runoff hydrograph for ungauged basin

6. Scope of Study
Setting up of an experimental basin and acquisition of high-resolution hydrological data
Develop a cell-based model to obtain direct runoff hydrograph for ungauged basins
Test the developed computer model using the data gathered from the experimental basin
Compare the prediction of the new model

7. Setting up of an Experimental Basin
Upper Kotmale basin, a tropical mountainous basin, was selected as the experimental basin to test the developed hydrological model under this study
High resolution, reliable rainfall data set is needed
Available data is daily rainfall data and it is not adequate
New data will be useful for hydrological research in SL

8. Experimental Basin Basin area = 304 km2 Elevation = 1200m -2500m
Nanu Oya
Agra Oya
Basin area = 304 km2
Elevation = 1200m -2500m
Annual rainfall = 2200 mm mm
Available past data:
Ten rainfall measuring stations with daily rainfall data
Two stream flow stations with hourly measurements
One pan evaporation gauge

9. Horton
Nuwara Eliya
Thalawakelle

10. Land Use Map Tea 44% Forest 36% Built up Land 7% Grass 5%
Cash Crops 5%

11. Analysis of Available Rainfall Data
Reliability analysis was carried out for ten stations in the Upper Kotmale basin for the period of thirty years from 1964
Data inconsistency at five stations namely Haggala Botanical Gardens, Sandringham estate, Holmwood estate, Seethaeliya farm and Dunsinan estate was observed
After checking the station log files and field visits to each site, it is found that the Holmwood estate data is unreliable. Data at other stations was corrected

12. Adequacy of the Existing Rain Gauge Network
Only four rain gauges are situated within the basin
No rainfall station situated at the middle part of the basin
Spatial distribution of the stations is not uniform
Temporal resolution of the existing rainfall data in the basin is one day
Therefore, existing rain gauge network in the experimental basin is not adequate

13. Installation of Stream Flow Gauges and High Resolution Rainfall Gauges
Agra Oya
Nanu Oya
Dambagasthalawa Oya
Kothmala Oya
Six Stations
Three Stations
0.1 mm least count

14. Location and data available periods of new rain gauges
Collected data was processed and the hourly rainfall-duration curves for each gauging station was obtained

15. Exceedence probability vs. hourly rainfall for
all stations

16. Exceedence probability vs. hourly rainfall for Ambewela farm

17. Summary of hourly rainfall duration curves

18. It can be observed that the hourly rainfall with particular probability of exceedance decreases with increase of the elevation of the gauging location

19. Application of Different Hydrological Models to the Experimental Basin
Three different hydrological models, HEC-HMS, SHER and Tank model, were calibrated and verified for the experimental Upper Kotmale basin
All these three models demand stream flow data for model parameter estimation. Therefore, it is not possible to use these models directly for stream flow prediction in ungauged basins

20. Development of a New Model (CellBasin Model)
A new model which capable of predicting direct runoff hydrograph for ungauged basins using basin’s rainfall, topography, land use, soil and stream network data was developed
Special care has been taken to make the model user friendly and simple by using parameters that can be obtained from physical information of the basin

21. Model development The basin was divided into several grids
Total travel time taken to flow water from each grid cell to the basin outlet is calculated
The S-curve for the basin is derived from the total travel time distribution
Unit hydrograph of the basin is obtained from the S curve

22. Assumptions
The basin consists of number of grid cells and each grid cell behaves as a sub basin of the main basin
Each grid cell is assumed as rectangular plane with uniform bottom slope and uniform surface roughness
Water flows through a grid cell only towards the maximum slope direction of the grid cell
Water from one grid flows only to one of the eight neighboring cells
Overland flow roughness depends only on land use type and it is constant for particular land use type
Overland flow through grid cell is steady, uniform and fully turbulent sheet flow

23. Flowchart

24. Computer model - “CellBasin"
A computer model with a Graphical User Interface (GUI) was developed using Visual Basic programming language
Input file path
File select dialog box

25. Case study
The CellBasin model was applied to the experimental Upper Kotmale basin using the data collected under this study. The model predictions were compared with the observed stream flow data at basin outlet at Thalawakelle

26. Data Preparation Source: USGS <URL: http://seamless.usgs.gov/ >
90 m SRTM - DEM
Source: USGS
<URL: >

27. 90 m Flow accumulation grid
90 m Slope grid
90 m Flow accumulation grid
90 m Flow direction grid

28. Data Preparation Contd.
Prepared using the land use digital maps produced by the Survey Department
90 m Land use grid

29. Observed and predicted direct runoff hydrograph
Results
Observed and predicted direct runoff hydrograph
Event 7/13/ :00

30. Observed and predicted direct runoff hydrograph
Results Contd.
Observed and predicted direct runoff hydrograph
Event 4/21/ :00

31. Observed and predicted direct runoff hydrograph
Results Contd.
Observed and predicted direct runoff hydrograph
Event 5/16/ :00

32. Results Contd.
One hour unit hydrograph of the basin

33. Comparison of the CellBasin Results
With derived unit hydrograph from observed data
The direct runoff at Nth time step (QN) due to the effective rainfall (P) of M pulses can be written as following discrete convolution equation. U represents the ordinates of the unit hydrograph.

34. Comparison of the CellBasin Results
Initial values were assumed for the ordinates of the unit hydrograph and then optimised the ordinates to minimise the error with the observed direct runoff hydrographs
A computer programme is written using MATLAB and the “fminicon” routing, which can handle inequality constrains is used for optimisation.

35. Comparison of the CellBasin Results
Comparison of the CellBasin Model Derived Unit Hydrograph with the Unit Hydrograph Derived form the Observed Data

36. Comparison of the CellBasin Results
2. With the results of the Snyder’s unit hydrograph model
Assuming the Upper Kotmale basin is ungauged , the Snyder’s unit hydrograph for the basin is obtained using the regional parameters
the Snyder’s unit hydrograph are given bellow.
Basin Area = 304 km2
Length of the main stream = 31.5 km
Distance from the centroid of the basin to the basin outlet = 13.3 km
Ct = 1.13 and Cp = 0.48

37. Snyder’s unit hydrograph for the Upper Kotmale basin
Comparison of the CellBasin Results
Snyder’s unit hydrograph for the Upper Kotmale basin

38. Observed and predicted direct runoff hydrograph
Comparison of the CellBasin Results
Observed and predicted direct runoff hydrograph
Event 7/13/ :00

39. Observed and predicted direct runoff hydrograph
Comparison of the CellBasin Results
Observed and predicted direct runoff hydrograph
Event 5/16/ :00

40. Observed and predicted direct runoff hydrograph
Comparison of the CellBasin Results
Observed and predicted direct runoff hydrograph
Event 4/21/ :00

41. Conclusions
Six tipping bucket type automatic high-resolution rain gauges and three stream flow-measuring stations were installed in the basin. Data collection has been carried out from these stations.
A cell based model, which is capable to derive direct runoff hydrograph for ungauged basins using basin’s topographical, soil, land use, stream network and rainfall data, was developed.
The developed model was applied to the mountainous Upper Kotmale basin using hourly data collected under this study.

42. Conclusions Contd.
The unit hydrograph obtained from the model was compared with the unit hydrograph derived from the observed data. Both unit hydrographs show similar in shape.
The CellBasin model predictions for the Upper Kotmale basin is compared with the predictions obtained from the Snyder’s synthetic unit hydrograph of the basin developed using regionalized parameters.
The CellBasin model predictions showed good agreement with the observed data than the Snyder’s unit hydrograph predictions.
CellBasin model is a useful tool to obtain direct runoff in ungauged basins.

43. Recommendations Continue the data collection in the experimental basin
Apply the CellBasin model for different basins and compare the results with the observed data
Develop the model further incorporating the overland flow depth variations with the flow accumulation values

44. Publications on this study
International conference, Asia Oceania Geosciences Society (AOGS) – 1 Paper (Accepted)
International Conference on Sustainable Water Resources Management in Changing Environment of the Monsoon Region – 2 Papers
International Summer Symposium, Japan Society of Civil Engineers (JSCE) – 1 Paper
Peradeniya University Research Sessions (PURSE) – 1 papers
Sri Lanka Association for the Advancement of Science (SLAAS) - 1 Papers

45. Thank You

46. Introduction Rainfall Surface water Hydrometry in Sri Lanka
Southwest monsoon – May to Sep.
Northeast monsoon – Dec. to Feb.
Inter monsoon – Mar.to Apr & Oct. to Nov.
Surface water
Sri Lanka has 103 river basins and their sizes vary from 10 km2 to km2
about 50 km3 volume of water Flows annually to the sea
Hydrometry in Sri Lanka
About 600 rainfall stations, 35 Pan evaporation stations and 35 stream flow measuring stations available in the country

52. represent the optimum number of stations, acceptable error percentage in average rainfall calculation and the coefficient of variation of the rainfall values at existing gauges (in per cent) respectively

54. 90 m Flow accumulation grid
GIS data used for the model
90 m Slope grid
90 m Flow accumulation grid
90 m SRTM - DEM
90 m Land use grid
90 m Flow direction grid

55. Overland flow
Overland flow can be modeled using Manning's equation by assuming the flow is steady, uniform and fully turbulent (Chow, 1988, Nurunnisa and Yilmaz, 2002). In this model Manning’s equation is used to calculate the flow velocity through each grid cell assuming steady, uniform, fully turbulent flow.
The overland flow through a grid cell can be assumed as a flow through a wide canal and hence the value of the hydraulic radius ( R) is taken as the flow depth (Y) in calculating the flow velocity through a grid cell. As the final target is to get the unit hydrograph for the basin, the flow depth (Y) is calculated due to constant rainfall rate of 1mm/hr. Considering the continuity and Manning’s equation , Y (m) can be written as
Flow Velocity through a grid cell is ;

56. Source: USDA, 1986, Nurunnisa and Yilmaz, 2002
Land use type
Manning’s roughness
coefficient
Water body
0.01
Build up land
0.011
Tea
0.17
Forest
0.8
Grass land
0.24
Other crop land
Source: USDA, 1986, Nurunnisa and Yilmaz, 2002

57. Canal flow
Measured cross section details and the condition of the canals are used to estimate a reasonable value for the hydraulic radius and roughness coefficient of the particular section of the canal. In this regard, starting point and ending point of the canal are defined by the cell value of the FAccGrid.
For the canal section,
If [Upstream FAccGrid value] < [(cell value of FAccGrid]< [Downstream FAccGrid value], then;
Where, MnReach and HRadius represent Manning’s roughness coefficient and hydraulic radius of the canal respectively

58. Source: USDA, 1986, Connecticut, 2001
Flow accumulation value
Hydraulic radius
Manning’s roughness coefficient
Effective flow depth
According to Table 1
0.32
0.04
0.71
0.05
>12000
0.9
0.06
Source: USDA, 1986, Connecticut, 2001

59. Source: USDA, 1986,
Connecticut, 2001

60. Snyder’s coefficients for Sri Lanka