Modelling and Simulation of Floods in Barsa River near Pasakha, Bhutan. Kirtan Adhikari

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1 Modelling and Simulation of Floods in Barsa River near Pasakha, Bhutan Kirtan Adhikari College of Science and Technology, Rinchending, Royal University of Bhutan Correspondence: Abstract Floods in Barsa River (Bhutan) have been identified as the most hazardous events of varying magnitudes at Pasakha industrial area located close to the river. There have been two devastating floods in 1996 and 2000, and some small ones during the recent past. Pasakha is one of the major areas selected for industrial development in Bhutan so it is important to protect this zone from any major natural hazards. This research analyses the dynamics of water flow in a section of Barsa River in the vicinity of Pasakha industrial area. The modelling and simulation of floods was performed in HEC-RAS software, HEC-GeoRAS was used to data interchange between HEC-RAS and ArcGIS, and Goole Earth Pro was used for Geo-Visualisation of the results. The flood scenarios were generated for a successive water discharge interval of 200 cumecs from 800 to 2000 cumecs. It was observed that the inundation area extends up to 125 meters from the centre of the river at 800 cumecs discharge and it increases up to 350 meters at 2000 cumecs discharge. The depth of water in the centre of the river was found 12 meters at 800 cumecs and meters at 2000 cumecs discharge. There is a strong positive relationship between velocity of water flow and shear stress with the levels of water discharge but it is not perfectly linear. The results of the research provided a good Geospatial base for developing a flood management framework addressing early-warning, emergency response and recovery planning. Key words: ArcGIS, DEM, Flood Modelling, HEC-GeoRAS, HEC-RAS, Mapping, TIN. Introduction Pasakha industrial area is situated in the foothill zone of Chukha district in the southern part of Bhutan. It is about 17 km eastward from Phuentsholing, the major town of Chukha district. Due to its location close to the Indian border, this area is being developed as one of major industrial zones in Bhutan. However, being near to the bed of Barsa River, the Pasakha industrial area also turns out to be a flood prone zone. Similar to most of the Himalayan rivers, Barsa River also has high water discharge during the rainy season, which also causes floods sometimes. In a previous study, Dorji et al. (2014) estimated the water discharge during a flood event amounting to cubic meters/second (cumecs) and used this for simulating a flood scenario in HEC-RAS software (USACE, 1986) for identifying flood hazard zone along Barsa River. However, they did not calculate velocity and shear stress of the flood water which are integral parts of a flood simulation. Both these aspects are also important for identifying the spatial coverage of flood, assessing the pattern of expected damage as well as the magnitude of flood disaster. Various functionalities of Geographical Information System (GIS), such as georeferenced input, spatial analysis and Geo-visualisation are essential steps in flood modelling and simulation (Fosu et al., 2012). Goel et al., (2005) carried out flood hazard mapping in Chindwin River Basin of Myanmar for different points of time and estimated return period. The flood water discharges have been computed using flood frequency analysis and HEC-RAS software was used to determine the mixed flow regime with different water levels defined as a boundary condition. Alaghmand et al. (2010) generated flood hazard map of Sungai Kayu Ara river basin in Kuala Lumpur, Malaysia using HEC-RAS, HEC-HMS and GIS Model. It is suggested that HEC-RAS 79

2 model has a high efficiency in calculating water level profile and flood zoning of the area. Kambedast & Esfandiar, (2011) studied the flood zone of Karkhed River using HEC-RAS hydraulic model and ArcGIS. They stated HEC-RAS and GIS were efficient tools to study flood prone area. Dawod et al., (2011) used GIS tools for mapping flash flood in Makkah city and pointed out the advantages of using GIS, such as cost-effective, digital output and ability to rerun with different conditions. This study carries out flood modelling for a part of the Barsa River in the vicinity of Pasakha industrial area (Map 1). The Basin of Barsa River is situated between N latitude to N latitude and from E longitude and between E longitudes. The total catchment area of the river is around 54.1 km 2, most of which is covered under forest (MST, 2005). The river has three main tributaries namely Kamji, Ganglakha and Jumja and their catchment area is 9.8 km 2, 24.9 km 2 and 11.7 km 2 respectively. Materials and Methods ASTER GDEM of 30 m spatial resolution has been used as the base for generating several sets of data used in this study. The modelling and simulation process involved generating water surface profile in HEC-RAS, creating geometric data in HEC-GeoRAS (USACE, 2011), and drawing river cross-sections in ArcGIS. The HEC-GeoRAS facilitated data exchange between ArcGIS and HEC-RAS software. Manning s Roughness Coefficient was computed based on field investigation and it was compared with the standard values (Chow, 1973; Parhi, 2013). The primary observations of water flow were recorded with the help of Float Method for determining the average velocity of water flow in the selected part of the river. The average velocity was calculated as 0.04 and this was used for calculating Manning s Roughness Coefficient. Map 1. Location of Pasakha Industrial Area The HEC-RAS system was used to generate critical flow profiles. The flood scenarios were developed for various levels of river discharge i.e. 800, 1000, 1200, 1400, 1600, 1800 and 2000 cumecs. The depth, velocity and shear stress of the water flow were simulated according to each level of river discharge. The flood model was developed and simulated with the combination of spatial, hydrologic and hydraulic datasets. Elevation was required as a base input for simulation and HEC- RAS performs better with TIN elevation model. Due to this, a TIN was generated from the ASTER GDEM and used as base elevation input in HEC-RAS. 80

3 Geo-Visualisation of the modelled extent of inundation areas was performed by overlaying the results on Google Earth Pro. Due to the availability of very high resolution images on Google Earth Pro, it is possible to see inundated areas and their land use land cover. This is very helpful in assessing the extent and magnitude of potential damage in the affected areas as well as for planning early-warning, rescue and recovery operations. Results and Discussion 1. River Discharge and Water Depth The results of the modelling of water depth at different levels of river discharge are presented in Table 1. The simulation shows maximum distance of inundation from the centre of river was about 125 m at 800 cumecs discharge and about 350 m at 2000 cumecs discharge (Maps 2a and 3a). The depth of water at the centre of the river varied from 12 m at 800 cumecs discharge to m at 2000 cumecs discharge. A very high increase in the depth, i.e m, was observed when the river discharged increased from 800 cumecs to1200 cumecs whereas this increase was between 0.20 m and 0.26 m at further levels of river discharge. This is because the slope of the river banks is steep in the lower parts and it decreases upward. Initially, the water rises more vertically than horizontally within the river bed and then it starts spreading out horizontally as the banks become wider as well as beyond the river banks. River Discharge (cumecs) Maximum Depth Flood Water (mts.) (+0.77) (+0.26) (+0.22) (+0.23) (+0.21) (+0.20) Table 1. Depth of Flood Water in the Centre of the River at Different Levels of River Discharge 2. River Discharge and Flood Water Velocity A positive relationship was found between the levels of water discharge in the river and the velocity of water flow (Figure 1). The minimum velocity of 5.74 meters/second (m/s) was found at 800 cumecs discharge and maximum velocity of 7.71 m/s at 2000 cumecs discharge. The rise in the velocity at successive levels of water discharge increases from 0.36 m/s at 1000 cumecs to 0.48 m/s at 1400 cumec and, thereafter, it decreases up to 0.25 m/s at 2000 cumecs. The spatial pattern of the velocity shows that it is higher in the in the upstream areas and lower in the downstream areas (Maps 2b and 3b). The river bed in the upstream areas is narrow and the slope is also steep and both these factors force a higher velocity of water flow in these areas. In the downstream areas, the river bed is relative wide and the slope is also less thus the velocity of water flow reduces in these areas. 3. River Discharge and Shear Stress The results of shear stress show a positive relationship with the levels of water discharge (Figure 2). The minimum shear stress is Newtons /square meter (N/m 2 ) at 800 cumecs discharge and the maximum is N/m 2 at 2000 cumecs discharge. The rise in shear stress is observed between N/m 2 and N/m 2 up to 1400 cumecs discharge, and between N/m 2 to N/m 2 from 1600 cumecs to 2000 cumecs discharge. 81

4 The distribution of shear stress shows that it is similar to the patter on velocity i.e. higher in the upstream areas and lower in the downstream areas (Maps 2c and 3c). A comparison of Map 2b with Map 2c, and of Map 3b with Map 3c shows that there is a strong positive correlation between velocity of water flow and shear stress at various levels of water discharge. Figure 1. Flow Velocity at Different Levels of River Discharge Figure 2. Shear Stress at Different Levels of River Discharge Conclusion The rivers in the foothill zone of the Himalayas experience floods of various magnitudes, especially during the rainy season. Sometimes, these floods cause natural disasters leading to loss of lives, property and infrastructure. This study focussed on the modelling and simulation of floods in Barsa River located in the southern Bhutan and Geo-Visualisation of the inundation scenarios at different levels of water discharge in the river. It was found that a water discharge of 800 cumecs will cause inundation of the areas falling within 125 meter distance from the centre of the river and this distance will increase up to 350 meters if the discharge increases to 2000 cumecs. The maps showing the patterns of velocity and shear stress help in identifying the critical areas for constructing flood control structures of various strengths. It can be concluded that HEC-RAS software is capable in generating Geospatial scenarios of floods, water velocity and shear stress at different levels of water discharge. However, it is not capable of generating all the required data which necessitates use of other data generation and transfer interfaces. It would be desirable to enhance the functionalities of HEC-RAS software and make it capable of generating and visualising Geospatial scenarios of floods. 82

5 Map 2a. Depth at 800 cumecs Map 3a. Depth at 2000 cumecs Map 2b. Velocity at 800 cumecs Map 3b. Velocity at 2000 cumecs Map 2c. Shear Stress at 800 cumecs Map 3c. Shear Stress at 2000 cumecs 83

6 Acknowledgements The author expresses gratitude to Mr. Om Kafley (Dean, Academic Affairs), Dr. Cheki Dorji (Director) and Mr. Tshering (Dean, Research and Industrial Linkages) at the College of Science and Technology, for providing necessary support and materials required for completing this study successfully. Sincere thanks to the Austrian Development Cooperation Coordination Office (ADA), Thimphu, Bhutan and Commission for Development Research (KEF), Austria for supporting the organisation of this conference as well as a faculty development workshop. The author is grateful to Dr. Shahnawaz (Department of Geoinformatics - Z_GIS, University of Salzburg, Austria) for his valuable suggestions that helped in improving this paper and motivating me to include it as part of this volume. Congratulations to the Centre for Rural Development Studies, College of Natural Resources, Lobesa for publishing this volume, this will serve as a good source for GIS learning and teaching processes. References Alaghmand, S., Abdullah, R. B., Abustan, I., Vosoogh, B. (2010). GIS-based River Flood Hazard Mapping in Urban Area (A Case Study in Kayu Ara River Basin, Malaysia). International Journal of Engineering and Technology Vol.2 (6), Dawod, G. M., Mirza, M. N., & Al-Ghamdi, K. A. (2011). GIS-Based Spatial Mapping of Flash Flood Hazard in Makkah City, Saudi Arabia. Journal of Geographic Information System, Dorji, C., Adhikari, K., Tobgay, K., Wangmo, C., Wangchuk, K. (2014). Flood Modeling and Hazard Zonation of Barsa River in Bhutan. Complex Disaster Around The World (pp. 5-7). Tokyo: Wesada University. Fosu, C., Forkuo, E. K., Asare, M. Y. (2012). River Inundation and Hazard Mapping - a Case Study of Susan River Kumasi. Global Geospatial Conference. Québec City, Canada. Goel, N. K., Than, H. H., Arya, D. S. (2005). Flood Hazard Mapping in the Lower Part of Chindwin River Basin, Myanmar. International conference on innovation advances and implementation of flood forecasting technology. Kamanbedash, A., Esfandiar, Y. (2011). Investigation And Study of Morphological Changing of River With Using HEC-GeoRAS and Mike 11 Software. World Applied Sciences Journal, 13(5), MST (2005). Report on the Study of the Catchment Area of Barsa Chhu and it's Future Impact on the Downstream Industries at Pasakha, in and Around Penden Cement Factory and New Industrial Estate Locations. Department of Geology and Mines, Royal Governmenet of Bhutan, Thimphu. Parhi, P. K. (2013). HEC-RAS Model for Mannnig s Roughness: A Case Study. Open Journal of Modern Hydrology, Chow, V. T. (1973). Open Channel Hydraulic. Mc-Graw-Hill, Singapore. pp Stanchev, H., Palazov, A., Stancheva, M. (2009). 3D Model For Flood Risk Assessment of Varna Bay Due to Extreme Sea Rise. Journal for Coastal Research, USACE (2011). HEC-GeoRAS User's Manual. US Army Corps of Engineers. < Accessed 1st September USACE (1986).HEC-RAS River Analysis System. < Accessed 1September