Estimation of Run-Of-River Hydropower Potential in the Myitnge River Basin

https://doi.org/10.20965/jdr.2020.p0267 Estimation of Run-of-River Hydropower Potential in the Myitnge River Basin

Paper: Estimation of Run-of-River Hydropower Potential in the Myitnge River Basin

Kyu Kyu Thin∗,†, Win Win Zin∗, Zin Mar Lar Tin San∗, Akiyuki Kawasaki∗∗, Abdul Moiz∗∗, and Seemanta Sharma Bhagabati∗∗

∗Department of Civil Engineering, Yangon Technological University Gyogone, Insein Road, Yangon 11011, Myanmar †Corresponding author, E-mail: [email protected] ∗∗Department of Civil Engineering, The University of Tokyo, Tokyo, Japan [Received July 31, 2019; accepted February 20, 2020]

The need for electricity is rapidly increasing, espe- 1. Introduction cially in developing countries. There is vast hydropower potential existing globally that has not yet A rapid increase in population and global urbanization been explored. This could be the only solution to solve has placed enormous pressure on global natural resources. future global power shortage. Hydropower is a clean The increase in the demand for energy, especially from and renewable source of energy because it does not renewable and sustainable sources, accelerates the scope exploit the use of water. However, using the conven- for development of small hydropower plants and enhances tional approach to harness hydropower results in sev- investment in new survey studies. There are various re- eral challenges. It is difficult to identify suitable sites newable energy options including wind, solar, and hy- and assess site potential during the planning stage of dropower. Hydropower is apparently the most common hydropower projects. In this study, run-of-river hy- and well-established form of renewable energy option [1]. dropower potential for the Myitnge River Basin was Preliminary hydropower survey studies are usually sub- estimated by intergrating a Geographic Information ject to huge uncertainties regarding the technical, eco- System (GIS) and Soil & Water Assessement Tool nomic and environmental feasibility of the undeveloped (SWAT) model. A GIS based tool was developed us- potential [2]. The main principle of hydropower is con- ing Python to spot the potential locations of the hy- verting the potential energy of water to mechanical en- dropower plants. The hydrological model (SWAT) was ergy, by flowing from a higher elevation to a turbine at a designed in order to obtain the values of monthly dis- lower elevation. This produces mechanical energy, which charge for all potential hydropwer sites. The flow is subsequently converted to electrical by the generator duration curves at potential locations were developed that is rotated by the turbine above. and the design discharge for hydropower was iden- There are various sizes of hydropower plants based on tified. Forty-four run-of-river (ROR) type potential the available water head, ranging from small, medium and hydropower sites were identified by considering only high head. Small hydropower has been gaining ground as the topographic factors. After simulation with SWAT a renewable energy source that could play a significant model, twenty potential sites with a hydropower gen- role in reducing fossil fuel use in both developed and de- eration potential of 292 MW were identified. Cur- veloping countries [3]. While few studies discuss the ad- rently, only one 790 MW Yeywa Hydropower Plant, ditional social consequences of small hydropower, those which is the largest plant in Myanmar, exists in the that do indicate several benefits, including the ability to Myitnge River Basin. The amount of estimated power bring small hydro to rural environments and reduce the generated from ROR may increase the existing power population displacement and cultural loss that is often as- system of Myitnge Basin by 36%. This study will associated with other forms of energy production. In ad- sist stakeholders in the energy sector to optimize the dition, small hydro has been associated with fewer point available resources to select appropiate sites for small sources of pollution, fewer downstream hazards, and a re- hydropower plants with high power potential. duced impact on crop yields and overall quality of life. In addition, it requires low initial funding, smaller project site area, shorter planning and construction period, local Keywords: Myitnge, hydropower, GIS, SWAT capacity, indigenous material, and lower power genera- tion cost in comparison to that of large power projects. This study presents a cost-effective system to assess run- of-river (ROR) hydropower potential and identify suitable project sites using Soil & Water Assessment Tool (SWAT) hydrological model and Geographic Information System

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© Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/). Thin,K.K.etal.

Source: M. Min Khaing, 2015 [6] Source: World Energy Council, 2013 [7] Fig. 1. Contribution to the electricity supply system. Fig. 2. Location of planned and existing hydropower projects.

(GIS). Global energy demand projections indicate an increasing trend, with annual consumption estimated to reach approximately 778 Etta Joule by 2035 [4]. This will present major challenges in the future for the energy production sector in particular areas. Myanmar is also facing a shortage of energy that has resulted in frequent power failures and load shedding throughout the country for the past several years. At present, hydropower comprises two-thirds of the country’s energy mix, with 3,151 MW of the installed capacity stemming from 25 operational projects. In addition, a further 46 GW of technically feasible potential has been derived, and these projects mentioned are now under construction or at the advanced planning stage [5]. Across its four major rivers and numerous tributaries, it is an estimated 40,000 MW of exploitable hydropower potential in Myanmar. It is crucial to pur- sue sustainable, alternate and non-polluting energy resources assuming top priority for self-reliance in regional energy supply [5]. Hydropower controls 71.4% of grid- connected electricity in Myanmar. Due to instability in system base load, insufficiency in power supply occurs Source: World Energy Council, 2013 [7] in summer. The contribution of different sources to the Fig. 3. Location map of study area. electricity supply system in current and future scenarios are illustrated in Fig. 1. The maximum power demand in Myanmar will vary from 9,100 MW to 14,542 MW in 2030 [5]. To combat the power shortage and the increased 2. Study Area future power demand, the MOEE (Ministry of Electricity and Energy) plans to develop the new hydropower plants The Myitnge River originates from the Ayeyarwaddy- as shown in Fig. 2. According to a recent estimate from Thanlyin watershed and flows westwards through the the MOEE, more than 100 GW of hydropower potential northern Shan Plateau of eastern Myanmar and eventually can be developed in Myanmar but only 3 GW has cur- flows into the Ayeyarwady at Amarapura. The area be- rently been established [6]. This study aims to contribute ing studied is 55,000 km2 and its outlet is located near the towards the development of a platform that can aid and Mandalay Division, which is the third capital of Myanmar support decision-makers and hydropower planners with as displayed in Fig. 3. It is located at 20◦484.44N, making more objective and quantified decisions. It pro- 23◦4821.60N and 95◦80.03E, 98◦2927.60E. The cli- vides a framework for robust and informed preliminary mate of this area is semi-arid and the annual precipi- hydropower planning. tation is approximately 1,412 mm. The daily tempera-

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ture ranges from 27–29◦C in summer and 18–25◦Cin hydropower potential can be written as Eq. (1) [12]. In winter. Myitnge has both abundant water resources and Eq. (1), the two parameters, Q and H, need to be calcu- steep slopes. The annual discharge at the Shwesaryan lated. If Q and H are known for a given segment of a station is 5788.3 m3/s. If harvested and utilized prop- stream, the hydropower potential can easily be estimated erly, the energy produced can help to meet the local de- for that segment [12]. mand and simultaneously raise the local standard of liv- P = γ QH, ...... (1) ing. At present, there is only one hydropower plant called Yeywa having an installed power of 790 MW in the My- where, itnge River Basin. This is the largest power plant in the P = Power [W] country. According to the DEPP (Department of Elec- γ = Speciﬁc weight of water [N/m3] tric Power Planning), there are six potential hydropower Q = Discharge [m3/s] plants in the Myitnge River Basin [8]. H = Head [m]

The methodological framework of the study is dis- 3. Methodology played in Fig. 4. Widely used GIS software ArcGIS 10.4 was used to process the satellite-derived Digital Elevation The Geographic Information System (GIS) concepts Model (DEM). Other ArcGIS run-of-river site selection and technologies contribute largely while conducting ac- tools were used to mark proposed sites and calculate their tivities related to water resource engineering planning and elevations [13]. The discharge analysis aims to plot the design. Recently, this technology is being extensively uti- flow duration curve (FDC) and calculating 40, 50, and 60 lized for hydropower potential assessment. In this study, percentile discharges (Q40, Q50, and Q60, respectively) GIS and hydrological modelling tools were used to assess using the historical flow data. The SWAT model was used the hydropower potential of various sites. The GIS analy- to calculate the discharge at various potential sites. The sis consists of various criteria that are used to identify an flow data from the two available gauges (Shwesaryan and apt site, such as the order of stream, gradient and the min- Hsipaw) in the Myitnge River were further manipulated imum distance between two hydropower stations. Only to determine the flow at ungauged sites. Finally, the out- 5th order or higher streams were considered; the gradient comes of the above process were fed into Eq. (1) to cal- was set as 1:50 or 2% and the minimum hydropower dis- culate the hydropower potential of proposed sites. tance between adjacent hydropower schemes was speci- fied as 3000 m. Satellite data like ASTER Digital Ele- vation Model was used to obtain the elevation difference 3.1. Head Determination by Using Run-of-River between power plants and diversion site of the potential Site Selection Tool sites. The Head (H) is the vertical distance between two SWAT is an extremely flexible and robust river basin points (diversion point and power house). It can also hydrological model that can stimulate wide variety of wa- be defined as the pressure created by the elevation be- tershed scenarios [9]. The SWAT model works in con- tween the diversion point and power house. In case of junction with ESRI’s Arc GIS environment. Among ex- run-of-river hydropower plant, the space for water stor- isting hydrological models, the SWAT model is one of age is not required. In this study, satellite elevation data, the most comprehensive models to simulate processes ASTER (Advanced Spaceborne Thermal Emission and prevailing at the surface of a watershed. SWAT is a Reflection Radiometer) GDEM (Global Digital Elevation conceptual-distributive model, which was initially de- Model) was used to obtain the elevation information of signed for large basins but gradually been expanded to the selected diversion point and power house. various applications [10]. The SWAT model is applied for In this study, a GIS-based tool analyses the topogra- flow assessment rate. The model is initiated and evaluated phy of the target basin to identify suitable run-of-river hy- for the area being studied. The flow rate is assessed by ap- dropower schemes [12]. This tool makes it possible to plying the hydrological model at the sites determined by survey large watersheds to obtain promising hydropower DEM. The flow duration curve is drawn up for those indi- sites, which would otherwise be a rather cumbersome vidual locations. After the flow rates have been assessed, task. potential sites are located within the criteria setup and the The tool has been developed so that site selection can hydropower potential is computed for these sites [11]. be achieved using two different approaches. It can be Run-of-river hydroelectric plants manipulate the flow based on either (i) only topographical factors or (ii) both of water and elevation drop (i.e., head) of streams to gen- topographical and hydrological factors. In approach (i), erate power. These facilities are built using a low dam the sites are selected by maximizing the gross head be- that diverts water from the main water channel to a con- tween the diversion site and the hydropower plant, as well veyance canal or pipeline. The penstock further directs as the catchment area at the diversion site, whereas the the flow to a turbine [12]. The power potential of flowing length of the waterway is minimized. Subsequently, the water is a function of the discharge (Q), specific weight hydropower potential is evaluated at the selected sites us- of water and difference in the head (H) between the in- ing the flow duration curves generated from the runoff take point and turbine. The mathematical expression for simulated by distributed hydrological modelling. How-

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Fig. 4. Methodological framework.

ever, in approach (ii), the hydropower potential of each by land use and soil properties and generates the runoff at alternative is evaluated initially by using the simulated various points. The runoff generated at the outlet of the runoff, and then, sites are selected by maximizing the hy- basin was compared with the observed discharge for the dropower potential and minimizing the length of the wa- calibration and validation of the model [12]. terway [13]. Generally, only a single sub-basin is used to vali- date the discharge at a particular location. However, to 3.2. Hydrological Modelling fully utilize the capabilities of a distributed hydrologi- The hydrological model was set up to assess the dis- cal model, a database should be constructed, so that the charge at various hydropower potential points. A SWAT model simulates the discharge for every sub-basin-ﬂow model was used in this study. It simulates the hydro- interval combination and stores it in that database. Us- logical process after considering precipitation, evapora- ing this database allows us to integrate the model into any tion, snowmelt and different losses, which are governed distributed framework without having to run the model

270 Journal of Disaster Research Vol.15 No.3, 2020 Estimation of Run-of-River Hydropower Potential in the Myitnge River Basin

Fig. 5. DEM of Myitnge River Basin. Fig. 6. Landuse map of Myitnge River Basin.

divided into 23 sub-basins and 94 HRUs by setting all the multiple times [12]. estimated points from the GIS tool as outlet points. The major geospatial input data includes Digital Ele- vation Model (DEM), soil data, land use and stream net- 3.3. Model Performance Evaluation work layers. DEM is the geospatial raster data containing The Model performance is evaluated at the Shwesaryan the continuous elevation values a topographic surface by streamflow station. The hydrological performance was the array of cells or pixels. It is used in ArcSWAT to cre- checked with the efficiency criteria and error parameter ate the watersheds and river networks and streams, sub- computations. The model performance was calculated basins, and parameters for the Hydrological Response by using the coefficient of determination (R2)andthe Units (HRUs) analysis. In this study, 30-meter reso- Nash-Sutcliffe efficiency (NSE) and measured at daily lution of ASTER GDEM is processed in a GIS envi- and monthly time scales. ronment using Watershed Delineation and HRUs anal- The simulation result is best when R2 is closer to 1 ysis. Fig. 5 shows the DEM of Myitnge River Basin. and NS is larger than 0.75. The simulation results are Land use and land cover data were acquired from the satisfactory when NS is larger than 0.36 and smaller SERVIR MEKONG LAND COVER image (http://servir- than 0.75; simulation results are not good when NS is rlcms.appspot.com/, n.d.). This data is used for HRU def- smaller than 0.36. The R2 and NSE values can be cal- inition, and then allocated a Curve Number (CN) in order culated by the following Eqs. (2) and (3): to land areas for the estimation of runoff and hydrological 2 ∑(Qmi − Qm)(Qsi − Qs) analysis. Fig. 6 shows the landuse and landcover map of R = , 2 2 .....(2) the Myitnge River Basin. ∑(Qmi − Qm) ∑(Qsi − Qs) The soil data for the study area was extracted from FAO (Food and Agricultural Organization of the United 2 ∑(Qm − Qs) i Nations) Digital Soil Map of the World (DSMW) NSE = − 1 2 ...... (3) (http://www.fao.org/soils-portal/soil-survey/soil-maps- ∑(Qmi − Qm) and-databases/en/, n.d.). This soil data was also used where Qm is the measured discharge value; Qs is the sim- for the HRU definition and hydrological analysis. Fig. 7 ulated discharge; Qm is the average measured discharge; illustrates the soil map of the Myitnge River Basin. and Qs is the average discharge simulated [13]. The list of spatial data is shown in Table 1. The daily weather variables used in this study are rainfall, and minimum and maximum air temperature for the period 3.4. Flow Duration Curve of 2005–2013. These are collected from the Department The monthly flow data is used to construct flow dura- of Meteorology and Hydrology. The watershed was tion curves (FDC) for each hydropower point. Flow du-

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Table 2. Distribution of potential hydropower sites by gross head.

Available gross head [m] No. of potential sites 43–60 11 61–80 13 81–100 7 101–150 7 151–200 3 201–316 3 Total 44

4. Results and Discussions

4.1. Potential Location of Run-of-River Hy- dropower Sites ROR hydropower plant utilizes the elevation drop along the stream. In order to select the potential sites, the following three criteria were set up: 1) Minimum Slope: Po- tential sites should have an average slope of 1:50, i.e., 2% along the river bed to ensure sufficient gross potential head to be available for the hydropower plant; 2) Distance Fig. 7. Landuse map of Myitnge River Basin. between Weir & Power House: The minimum distance between weir/diversion dam and the power house should not be less than 2 km, while the maximum distance should Table 1. Spatial data. not be more than 5 km. The criteria of maximum distance is set in order to No. Data Spatial resolution Source limit the length of conveyance canal/pipe; 3) Order of Digital elevation Stream: All streams having a minimum drainage area 1 30 m resolution ASTER 2 model of 100,000 km have been considered in the potential site selection of the ROR type hydropower plant. The target Servir 2 Land use map 90 m resolution scale of development for the purpose of this study is ROR Mekong so the threshold has been set in this study; as discussed 3 Soil map 1 km resolution FAO with Department of Hydropower Planning and Implemen- tation. However, these values vary from region to region and should ideally be decided by the hydropower planner ration curves are simply a plot of the discharge ranked in or decision-maker. After meeting these criteria of gross descending order against the number of days in a year. It head, catchment area and length of the waterway, this tool tells us how likely it is that the discharge will be equal locates the potential power house site and weir/diversion to or exceed a particular value, for a certain percentage site. It produces the output in GIS shape file format and of the time. Using flow duration curves, it is possible to text file format along with other necessary information. calculate certain statistical values that indicate the avail- Using only these topographic factors (slope, length of the ability of water and can be used as design variables. The waterway and catchment area), a total of 44 schemes have equation representing a flow duration curve is as follows, been identified using the tool in this study. Thus, a total M of 44 sites were determined as potential run-of-river sites. P = 100x , ...... (4) n + 1 Gross head ranging from 43–316 m were observed in these potential sites. Among the 44 potential sites, 11 of where P is flow exceedance, M is rank of discharge, and them have gross head in the range of 43–60 m. Only 3 hy- n is defined as a number of events in a period. Using flow dropower sites have gross head greater than 200 m. The duration curves, we can accurately capture and represent distribution of the sites by available gross head is pre- the amount of runoff available for hydropower produc- sented in Table 2. The spacing between hydropower sites tion, both in space and time [12]. is considered as 3,000 m in order to obtain a precise result. Fig. 8 depicts the location of the hydropower potential sites according to gross head.

272 Journal of Disaster Research Vol.15 No.3, 2020 Estimation of Run-of-River Hydropower Potential in the Myitnge River Basin

Fig. 9. Result of daily calibration of year 2008.

Fig. 8. Location of run-of-river type hydropower potential sites by considering topographic factors. Fig. 10. Monthly validation discharge at Shwesaryan.

4.2. Model Calibration and Validation The calibration of the parameters of this model has Table 3. Calibration and validation statistical model results. been performed by using the observed daily data for the Statistical parameter R2 NSE year 2008 from the Shwesaryan station. Sensitive param- Daily calibration (2008) 0.83 0.78 eters, such as the threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN.gw), Monthly validation (2005–2013) 0.72 0.63 Manning’s n for overland flow (OV N.hru), average slope steepness (HRU SLP.hru), Manning’s “n” value for the main channel (CH N2.rte), threshold depth of water in 4.3. Power Potential Estimation Considering Hy- the shallow aquifer for “revap” to occur (REVAPMN.gw), drological Factor and the average slope length (SLSUBBSN.hru) have been The designed flow for a hydropower plant is based on adjusted. the flow duration curve. The flow duration curve was The comparison of the model simulated values with prepared from the daily flow data from the year 2005– the observed values determines how well a model could 2013, which was simulated from the calibrated SWAT simulate the hydrological behavior of the area being stud- model. Flow duration curve for the average monthly flow 2 ied [14]. The NSE and R values are used to compute the was prepared for all the potential ROR hydropower sites. performance of the model. The statistical analysis results Mostly installed capacities of the hydropower sites are de- for the performed calibration were “very good” for NSE signed for 40–60% flow exceedance. Fig. 11 shows the 2 2 (0.75 ≤ NSE ≤ 1.00), and R (R ≤ 1.00) value [15]. The flow duration curve at the outlet of the basin. result of the daily calibration of data from the year 2008 After considering the hydrology of the basin, 20 poten- with fitted parameter is as shown in Fig. 9. The valida- tial hydropower sites were identified through the analy- tion process is executed using the observed discharge data sisasshowninFig. 12. The gross power potential of all from 2005–2013 taken in monthly time steps. Fig. 10 il- these potential sites was calculated using the power equa- lustrates the observed and simulated monthly discharge at tion. Table 4 presents the total installed capacity based on Shwesaryan for the validation period. The result obtained the different percentage flow exceedance. In this study, for the statistical evaluation criteria that should be used to 60% exceedance is considered for the calculation of hy- check model performance is presented in Table 3. dropower potential. The power generated power by 20 potential sites is shown in Table 5. In sites 48 and 45, the highest power generated has values above 50 MW because the flow and head difference is high at these sites.

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Table 4. Run-of-river type power potential of Myitnge Basin. Percent Exceedance Total Installed Capacity [MW] 40% 1,098 50% 517 60% 292 95% 9.85 'LVFKDUJH PV Table 5. Generated power by each potential site. 3HUFHQWDJHH[FHHGHQFH No Site Generated Power [MW] 1 1 2.68 Fig. 11. Flow duration curve of the Myitnge River Basin. 2 2 3.96 3 3 2.372 4 6 5.287 5 15 7.782 6 16 40.857 7 17 6.517 8 22 19.421 9 23 5.874 10 26 18.758 11 30 8.338 12 33 26.665 13 36 7.611 14 37 12.015 15 42 1.979 16 43 0.006 17 45 50.546 18 46 8.605 19 47 10.264 20 48 52.739

of free satellite data, the project is cost effective and effi- cient. It can also be replicated in other parts of the country in order to achieve rapid hydropower assessment. In this approach, each point is examined manually. The short- Fig. 12. Location of run-of-river type hydropower potential est waterway length and maximum head difference from sites by considering topographic and hydrological factors. among these points is selected to optimize the design. This research is novel because it is the first GIS based hydropower estimation of the Myitnge River Basin. Fu- ture scope of this study involves upgrading the utilization 5. Conclusion of further veracious input. This might involve addition of cross-section data instead of a free global DEM data This tool is capable of automatically resolving conflicts source, thereby improving the accuracy of the DEM data. among various projects and can be used to survey large ar- In addition, sequential streamflow routing should be per- eas. The potential run-of-river sites were identified based formed to achieve an optimal design. on the criteria of distance, head, and drainage area. This also allows the hydropower planner to optimize the hydropower potential for a target scale of hydropower de- Acknowledgements velopment. The efficiency of such a computer-aided site The research was supported by the Japan Science and Technol- identification approach has shown promising results. ogy Agency (JST, JPMJSA1407)/ Japan International Coopera- The results involved 20 ROR hydropower schemes hav- tion Agency (JICA), and the Science and Technology Research ing 292 MW in the Myitnge River Basin. The total basin Partnership for Sustainable Development Program (SATREPS). power potential is predicted to increase 36% of the exist- We are grateful to the following institutions for providing us with ing power system. Since the proposed approach availed the necessary data support from the Department of Meteorology

274 Journal of Disaster Research Vol.15 No.3, 2020 Estimation of Run-of-River Hydropower Potential in the Myitnge River Basin and Hydrology (DMH), Department of Hydropower Planning and Implementation (DHPI), and The University of Tokyo (UTokyo). Name: Win Win Zin

References: Affiliation: [1] OECD/IEA, “Hydropower Essentials,” 2010. Professor, Department of Civil Engineering, [2] “Shell energy scenarios to 2050,” Shell International BV, 52pp., Yangon Technological University 2008. [3] H. Liv, D. Masera, and L. Esser (Eds.), “World Small Hydropower Develoment Report 2013,” UNIDO and ICSHP, 2013. [4] A. Rafiee and K. R. Khalilpour, “Renewable Hybridization of Oil and Gas Supply Chains,” K. R. Khalilpour (Ed.), “Polygeneration Address: with Polystorage for Chemical and Energy Hubs,” pp. 331-372, Academic Press, 2019. Gyogone, Insein Road, Yangon 11011, Myanmar [5] A. B. H. Dim, M. Rutten, W. W. Zin, and O. A. Hoes, “Estimation of Brief Career: Hydropower Potential in Myanmar,” Global J. of Engineering and 1996- Assistant Lecturer, Yangon Technological University Technology Review, Vol.2, No.4, pp. 78-85, 2017. 2001- Lecturer, Yangon Technological University [6] M. Khaing, “Myanmar’s Hydropower Strategy and its Implication 2009- Associate Professor, Yangon Technological University on Regional Development,” World Hydropower Congress 2015, 2017- Professor, Yangon Technological University 2015. Selected Publications: [7] World Energy Council, “World Energy Resources: 2013 survey,” • “River Flood Inundation Mapping in the Bago River Basin, Myanmar,” p. 11, 2013. Hydrological Research Letters, Vol.9, No.4, pp. 97-102, 2015. [8] Department of Hydropower Planning and Implementation. • “Long-term Changes in Annual Precipitation and Monsoon Seasonal [9] J. G. Arnold, R. Srinivasan, R. S. Muttiah, and J. R. Williams, Characteristics in Myanmar,” Hydrol. Current Res., Vol.8, No.2, Article “Large Area Hydrologic Modeling and Assessment Part I: Model No.271, 2017. Development,” J. Am. Water Resour Association, Vol.34, No.1, • “Flood Hazard Assessment of Bago River Basin, Myanmar,” J. Disaster pp. 73-89, 1998. Res., Vol.13, No.1, pp. 14-21, 2018. [10] P. W. Gassman, M. R. Reyes, C. H. Green, and J. G. Arnold, “The soil and water assessment tool: Historical development, applica- Academic Societies & Scientific Organizations: tions, and future research directions,” Trans. ASABE, Vol.50, No.4, • Myanmar National Committee on Large Dam (MNCOLD) pp. 1211-1250, 2007. • Myanmar Engineering Society (MES) [11] B. C. Kusre, D. C. Baruah, P. K. Bordoloi, and S. C. Patra, “Assess- ment of hydropower potential using GIS and hydrological modeling technique in Kopili River basin in Assam (India),” Applied Energy, Vol.87, No.1, pp. 298-309, 2010. [12] J. R. Pudashine, “Assessment of Hydropower Potential using GIS and Hydrological Modelling under Current and Future Climate in Name: Dudh Koshi Basin, Nepal,” M.Eng. Thesis, Asian Institution of Zin Mar Lar Tin San Technology, 2013. [13] A. Moiz, A. Kawasaki, T. Koikeb, and M. Shresthac, “A systematic decision support tool for robust hydropower site selection inpoorly Affiliation: gauged basins,” Applied Energy, Vol.224, pp. 309-321, 2018. Professor, Civil Engineering Department, Yan- [14] J. E. Nash, and J. V. Sutcliffe, “River flow forecasting through con- gon Technological University ceptual models, Part I: A discussion of principles,” J. of Hydrology, Vol.10, No.3, pp. 282-290, 1970. [15] C. T. Haan, D. E. Strom, T. Al-Issa, S. Prabhu, G. J. Sabbagh, and D. R. Edwards, “Effect of parameter distributions on uncertainty analysis of hydrologic models,” Trans. ASAE, Vol.41, No.1, pp. 65- 70, 1998. Address: Gyogone, Insein Road, Yangon 11011, Myanmar Brief Career: 2001- Assistant Lecturer, Technological University (Pathein) Name: 2004- Lecturer, Technological University (Meikhtila) Kyu Kyu Thin 2008- Associate Professor, Technological University (Mandalay) Selected Publications: • Affiliation: “Rainfall-Runoff-Inundation (RRI) Model Application in the Bago River Basin,” 6th AUN/SEED-Net Conf. Proc., 2014. Ph.D. Candidate, Department of Civil Engineer- ing, Yangon Technological University Academic Societies & Scientific Organizations: • Myanmar Engineering Society (MES)

Address: Gyogone, Insein, Yangon 11011, Myanmar Brief Career: 2013- Civil Engineer, Myanmar Koei International Ltd. Selected Publications: • “Rainfall-Runoff Modelling of Myitnge River Basin using SWAT Model,” J. of Emerging Technologies and Innovative Research, Vol.7, No.1, 2020. Academic Societies & Scientiﬁc Organizations: • Myanmar Engineering Society (MES)

Journal of Disaster Research Vol.15 No.3, 2020 275 Thin,K.K.etal.

Name: Name: Akiyuki Kawasaki Seemanta Sharma Bhagabati

Afﬁliation: Afﬁliation: Project Professor, Department of Civil Engineer- Project Researcher, Department of Civil Engi- ing, The University of Tokyo neering, The University of Tokyo

Address: Address: 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Brief Career: Brief Career: 2008-2009, 2019- Visiting Scholar, Harvard University 2010-2012 Master Degree program, Asian Institute of Technology, 2010-2015 Visiting Associate Professor, Asian Institute of Technology Thailand (AIT) 2012-2014 Research Associate, Asian Institute of Technology, Thailand 2010-2017 Project Associate Professor, The University of Tokyo 2014-2018 Doctoral Degree program, The University of Tokyo 2018- Project Professor, The University of Tokyo 2017-2018 Technical Assistant, The University of Tokyo Selected Publications: 2018- Project Researcher, The University of Tokyo • A. Kawasaki, G. Kawamura, and W. Z. Win, “A local level relationship Selected Publications: between floods and poverty: A case in Myanmar,” Int. J. of Disaster Risk • “A cooperative framework for optimizing transboundary hydropower Reduction, Vol.42, pp. 151-159, 2020. development,” Water Int., Vol.42, No.8, pp. 945-966, 2017. • A. Kawasaki, P. Koudelova, K. Tamakawa, A. Kitamoto, E. Ikoma, K. • “Consideration of the rainfall-runoff-inundation (RRI) model for flood Ikeuchi, R. Shibasaki, M. Kitsuregawa, and T. Koike, “Data Integration mapping in a deltaic area of Myanmar,” Hydrological Research Letters, and Analysis System (DIAS) as a platform for data and model integration: Vol.11, No.3, pp. 155-160, 2017. Cases in the field of water resources management and disaster risk reduction,” Data Science J., Vol.17, doi: 10.5334/dsj-2018-029, pp. 1-14, 2018. • A. Kawasaki, M. Henry, and K. Meguro, “Media preference, information needs, and the language proficiency of foreigners in Japan after the 2011 Great East Japan Earthquake,” Int. J. of Disaster Risk Science, Vol.9, No.1, pp. 1-15, 2018. Academic Societies & Scientific Organizations: • Sustainability Science, Editor • Turkish Journal of Geographical Sciences, Editorial Advisory Board • Science Council of Japan (SCJ)

Name: Abdul Moiz

Afﬁliation: Department of Civil Engineering, The University of Tokyo

Address: 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Brief Career: 2015-2017 Masters Degree Program, The University of Tokyo 2017- Doctoral Degree Program, The University of Tokyo Selected Publications: • “A systematic decision support tool for robust hydropower site selection in poorly gauged basins,” Applied Energy, Vol.224, pp. 309-321, 2018. Academic Societies & Scientiﬁc Organizations: • American Society of Civil Engineers (ASCE) • American Geophysical Union (AGU)

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