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Assessing Climate Change Impacts on Hydropower Generation in the Myitnge River Basin, Myanmar

Min Khaing Director, Department of Hydropower Implementation, Ministry of Electric Power, Myanmar

ABSTRACT

Climate parameters such as temperature and precipitation are expected to change in the future and could significantly impact the hydrology of river basins. Changes in amount, timing and frequency of streamflow influence hydropower generation. This study was conducted to assess the climate change impacts on streamflow and hydropower generation in the Myitnge river basin. The Myitnge river basin is located in the northern part of the Republic of the Union of Myanmar and covers a total area of 30,800 km2. There are two numbers of reservoir type hydropower plants in this basin, namely, Yeywa, installed capacity 790MW and Upper Yeywa, installed capacity 280MW. Future climate parameters were projected from five general circulation models of CMIP-5 archive for RCP4.5 and 8.5. The hydrologic model (HEC-HMS) was applied in the study of the future changes in hydrological regimes especially in the annual and monthly inflow. The long term-average annual discharge is projected to decrease in GFDL-CM3 and MIROC-ESM-CHEM for all time periods. But MIROC-ESM, MPI-ESM- LR and MPI-ESM-MR show mixed trends. The intra-annual (monthly) changes in the river discharge are greater as compared to the annual discharges changes. HEC-ResSim, the reservoir simulation model was applied to examine how hydropower generation can vary for future climate scenarios. Future power production of the basin is expected to decrease under GFDL-CM3 and MIROC-ESM-CHEM. However, other three climate models have mixed trend in future hydropower production. The remarkable decreasing trend is observed in RCP8.5 of GFDL- CM3 with the ranges 13~25% and 18~23% in Yeywa and Upper Yeywa respectively. The analyses show that adaptation to the future climate scenarios is essential to optimal hydropower production in this river basin.

1. INTRODUCTION

Hydropower as a renewable and sustainable electric energy provider is closely linked to the hydrological situation of a certain region (Koch et al. 2011). Hydropower is the most vulnerable energy source to change in global and regional climate because of its direct dependence on the magnitude and timing of streamflow (Jamali et al. 2013). Therefore, the assessment of climate change impact on hydropower generation to the future climate scenarios is crucial.

Myanmar is one of the most vulnerable countries to climate change in Asia and Pacific in term of the indicator values of exposure such as change in temperature and precipitation and adaptive capacity such as poverty (ADB, 2009). One of the most important concerns related to climate change impact on streamflow in Myanmar is the implication of hydropower generation. The country relies mainly on the renewable energy, and hydropower sector is responsible for more than 55% of Myanmar’s electric power generation. Therefore, the impacts of climate change on the hydropower production and adaptation strategies are critical research area of Myanmar.

2. STUDY AREA AND DATASETS 2.1 BASIN DESCRIPTION

The Myitnge River, one of the tributaries of the Ayeyarwady River, originates from Mount Loi Swang at an elevation of 1,460 m on the northern Shan Plateau. The Mytinge river basin is located approximately between the latitude 20°51’N ~ 23°48’ N and the longitude 96°23’E ~ 98°22’ E (Fig.1). It covers the north-west part of the Shan state and touches the Mandalay division at the downstream part near the confluence of the Ayeyarwady River.

The Myitnge river basin is characterized by two seasons: a rainy season from Mid of May to October and a dry season from November to Mid of May. In particular, the period from November until February is cold and from March until April is hot. Monsoon climates, southwest and northeast ones, distinguish the climate of the basin area between wet and dry seasons. The southwest monsoon brings most rainfall of the basin from June until October; however, its effect on the basin is considerably moderated by its passage across the coastal hill ranges. The dry season, from November until Mid May, derives from the northeast monsoon and rainfall during this period comes Hydropower'15

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to practically zero (0). During the rainy season, the rainy days last consecutively for 90 to 120 days, resulting in the flood occurrence with long duration in the Myitnge River.

Fig.1: Myitnge river basin and location of Yeywa and Upper Yeywa hydropower plants

2.2 AVAILABLE DATA

For the Myitnge river basin, a digital elevation model (DEM) derived from U.S Geological Survey Global Data Explore (http:// www. gdex.cr.usgs.gov/) with 30m resolution. Soil parameters were derived from the Digital Soil Map of the world (Version 3.6) of the Food and Agriculture Organization (FAO). Land use data was collected from the Joint Research Center of European Commission website (http://bioral.jrc.ec.europa.eu/products/).

OBSERVED HYDRO-METEOROLOGICAL DATA Daily precipitation data, for the period of 1981-2005, of eleven stations was collected from the Department of Meteorology and Hydrology, and Department of Hydropower Implementation, Myanmar. Minimum and maximum temperature data were only available for Hsipaw, Kyaukme, Kyaukse, Lashio, Mandalay and Naungcho stations. Daily discharge data from Salin (Yeywa) gauging station was obtained from two departments. The daily time series discharge for both stations were available for the period 1981 to 2005.

CLIMATE MODEL DATA For this study five general circulation models (GCM) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were selected to study the uncertainty in the climate change projections. Daily precipitation, maximum temperature and minimum temperature of selected GCMs were downloaded from the CMIP5 data portal (http://pcmdi9.llnl.gov/). In this study the RCP4.5 and 8.5scenarios were used for all 5 GCMs to cover the low, medium, and high end of possible future climate projections.

Table -1: Selected Global Climate Models from CMIP5 archive Emission Climate model Institute/ organization Duration scenario 1981~2005 GFDL-CM3 Geophysical Fluid Dynamics Laboratory RCP 4.5, 8.5 2006~2100 Atmosphere and Ocean Research Institute (The 1981~2005 MIROC-ESM RCP 4.5, 8.5 University of Tokyo), National Institute for 2006~2100 MIROC-ESM- Environmental Studies, and Japan Agency for 1981~2005 RCP 4.5, 8.5 CHEM Marine-Earth Science and Technology 2006~2100 1981~2005 MPI-ESM-LR RCP 4.5, 8.5 2006~2100 Max Planck Institute for Meteorology (MPI-M) 1981~2005 MPI-ESM-MR RCP 4.5, 8.5 2006~2100 Hydropower'15

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RESERVOIR AND HYDROPOWER DATA

The Yeywa reservoir and hydropower plant is located on the downstream stretch of the Myitnge River, about 50km southeast of Mandalay, the second largest city of Myanmar in air-distance. Average annual inflow into the reservoir is 15231MCM/year and the gross storage and effective storage capacity of the reservoir are 2630MCM and 1630MCM respectively. Yeywa hydropower plant is located just downstream of the reservoir and the installed capacity is 790 MW and the total annual generating capacity is 3550 GWh. The reservoir operation data were collected from the Department of Hydropower Implementation and Hydropower Generation Enterprise under the Ministry of Electric Power, Myanmar. The first unit operation was started in July2010 and the electricity is connected to the national grid. The power plant was commissioned in December 2010. But the daily operating data such as inflow, water level, spill, turbine discharge, power production, energy generation and operating hours were collected from July 2010 to the end of 2013.

The Upper Yeywa reservoir and hydropower plant is located in the Kyaukme District of the Northern Shan State and about 1300km upstream of Yeywa reservoir and hydropower plant. Average annual inflow into the reservoir is 11479MCM/year but the gross storage and effective storage capacity of the reservoir are only 341MCM and 196MCM respectively. Main objective of the project is the hydropower generation only and the installed capacity is 280 MW and the total annual generating capacity is 1409 GWh. At present, the project is under construction stage; therefore, the data for reservoir operation and hydropower production are not available. Nevertheless, the data of design stage such as reservoir capacity curve, inflow, calculated hydropower production and others can be collected.

Table -2: Salient features of Yeywa and Upper Yeywa reservoir Particular Yeywa Upper Yeywa Annual inflow 15231MCM 11479MCM Reservoir Full tank level EL 185m EL 395m Gross storage capacity 2630MCM 341.44MCM Effective storage capacity 1630MCM 196.18MCM Minimum operation level EL 148m EL 385m Features of dam Dam type RCC Dam Concrete Dam Crest elevation EL 197m EL 398m Dam height and length 132 m and 690 m 97 m and 247 m Features of power plant Installed capacity 790 MW (187.5 MW x 4 units) 280 MW (70 MW x 4 units) Commissioning date December, 2010 Under construction

3. METHODOLOGY 3.1 CLIMATE MODEL DOWNSCALING

General circulation models and regional climate models are the important tools to project the expected future scenarios of climatic parameters. But the spatial resolutions of GCMs are too coarse for basin scale hydrologic modeling. Therefore, it is necessary to do downscaling the climate variables. Linear scaling method (Teutschbein & Seibert, 2012) for temperature and local intensity scaling method (Teutschbein & Seibert, 2012) for precipitation are utilized for downscaling of the climate variables.

For the linear scaling of temperature, firstly, changes in the monthly GCM data between a base period (1981-2005) and future period were calculated for each month. Linear scaling factor was calculated using Eq. (1). And then, time series data of temperature simulated from GCM/RCM is corrected using Eq. (2) for historical period and Eq (3) for future period. The maximum and minimum temperature was all downscaled using this linear scaling method. Scaling factor = {µm (Tobs(d))} - {µm (This(d))} (1) T*his (d) = This (d) + [{µm (Tobs(d))} - { µm (This(d))}] (2) Hydropower'15

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T*scen (d) = Tscen (d) + [{µm (Tobs (d))} - { µm (This (d))}] (3)

The downscaling of precipitation using local intensity scaling has three steps and this method can adjusts mean as well as wet-day frequency and intensity (Teutschbein & Seibert, 2012). Firstly, precipitation threshold of historical run (Pth,his) is determined by calibrating the number of days from RCM/GCM historical run exceeding this threshold equal to the number of days of observed data with precipitation higher than 0 mm. Then the number of events of precipitation for both historic and scenario run are corrected by applying Pth,his. It is the correction of the days with precipitation less than Pth,his as the dry days with precipitation 0 mm. In second step, the scaling factor is calculated based on the mean wet-day intensities predicting from long-term monthly data. But it is important that only wet-days is taken into account to calculate the intensity scaling factor. So, the observed days with precipitation larger than 0mm and RCM/GCM simulated days with precipitation larger than Pth,his are taken to calculate the scaling factor. In the third step, the time series data of future scenario run are corrected by multiplying the intensity scaling factor and redefined RCM/GCM scenario run data at the first step.

3.2 HYDROLOGIC MODEL SET-UP AND CALIBRATION

Runoff of the basin was simulated using the Hydrologic’s Engineering Center’s Hydrologic Modeling System (HEC-HMS), version 3.5, developed by the United States Army Corps of Engineers. It is able to reproduce runoff on a daily basis with high efficiency and has been used in various sizes of catchments all over the world. The user can select a variety of methods in loss, direct runoff, base flow, and channel flow routing methods. Therefore, the semi-distributed hydrologic model, HEC-HMS is chosen with these advantages. The HEC-HMS contains three steps to set-up 1) a basin model, 2) a meteorological model, 3) control specification. The physical representation of watershed such as sub-basins, stream network, reaches and outlet points were developed in the basin model. These characteristics were created using HEC-GeoHMS10.1 that works with ArcView GIS 10.1. As this hydrologic model is needed to run for long period the deficit and constant loss method (continuous loss model) was selected. In this method, the loss is accounted as continuous changes in soil moisture content. Clark unit hydrograph method was selected as transform model and exponential recession method was selected to calculate the base flow of watershed. Muskingum method was used as channel flow routing to transfer the flow in the reaches. The meteorological model was developed with the Thiessen Polygon Weight method. Control specification such as start date and time, end date and time and time interval for each run were also set-up in the model.

Calibration of the model is an important step in which the parameters were adjusted to get a good agreement between observed discharge and simulated discharge for a particular location or station. A slip sample method was selected to arrange the time period of calibration and validation process. In this method, the calibration and validation periods do not overlap and separate with each other. The observed discharge of Salin (Yeywa) station was collected for the period 1981 to 2005. The hydrologic model was calibrated for Salin station. The period of 1981-1995 was used for model calibration whereas the period of 1996-2005 was used for validation. Coefficient of determination (R2), root mean square error (RMSE), the Nash-Sutcliffe efficiency (E), percent volume error (PVE), percent peak error (PPE) and percent bias (PBIAS) were used to check the model performance in simulating the hydrologic process and runoff.

Table -3: Statistical performance of hydrological model Item R2 RMSE (m3/s) E PBIAS PVE (%) PPE(%) Calibration 0.849 145 0.846 -5.82 -2.72 -1.44 Validation 0.906 108 0.901 3.2 -4.72 -5.0

3.3 RESERVOIR SIMULATION MODEL SET-UP AND CALIBRATION

Reservoir system simulation model, HEC-ResSim is developed by U.S. Army Corps of Engineers and it is useful in projecting the behaviour of reservoir systems in water resource management field. There are three main modules in this model: watershed setup, reservoir network and simulation module. The main function of watershed setup is to create and define the watershed within a common framework. The reservoir network schematic can be created in the second module, reservoir network module. Moreover, the physical features of reservoir network and operation data can be described in there. The last module, simulation module can be used to simulate the alternative. It is necessary to specify simulation time window and computation interval in this module. In this study, HEC-ResSim model was set-up based on three steps. Hydropower'15

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After setting HEC-ResSim model for reservoir system, the model was run for calibration purpose to check the reliability. Since, the first machine of Yeywa hydropower plant was operated in July, 2010 and the hydropower plant was commissioned in December, 2010, the observed hydropower data for four unit operation was only available for January 2011 to December 2013. Therefore, 2011 to 2012 was selected as calibration period and year 2013 was selected for validation. The calibration and validation processes were done by comparing time series data of actual daily releases and model outputs of Yeywa reservoir. By taking the time series data of release discharge from model simulation and observed data, the statistical parameters the coefficient of determination (R2), root mean square error and the Nash-Sutcliffe efficiency (E) were calculated and the results are summarized in Table-4. The efficiency indices indicate that the observed and simulated releases have a reasonable agreement and the performance of model is acceptable. Furthermore, the simulated hydropower generations of calibration and validation period were also compared with observed monthly hydropower generation. The reservoir operation model generated a well agreement in power production for calibration and validation period.

Table-4: Summary of statistical parameters for calibration and validation Item R2 RMSE (m3/s) E Calibration 0.849 145 0.846 Validation 0.906 108 0.901

4. RESULTS AND DISCUSSIONS 4.1 IMPACTS OF CLIMATE CHANGE ON STREAMFLOW

The impact of climate change on streamflow at the Salin (Yeywa) station was analyzed. Firstly, calibrated hydrologic model was run for future scenarios and the percentage change in annual and intra-annual (monthly) discharge for each scenario run relative to base period (1981 to 2005) was calculated for two emission scenarios and three time windows, 2020s (2011 to 2040), 2050s (2041 to 2070) and 2080s (2071 to 2100).

The impacts on streamflow were analyzed with two conditions, analysis on annual and intra-annual discharge. The relative changes of annual discharge projected by CMIP5-GCMs at each time period for Salin station (Yeywa reservoir) are presented in Fig.2. Considering RCP4.5 scenario, the annual discharge change ranges from a 28% increase to a 29% decrease depending upon time periods and climate models. The long term-average annual discharge is projected to decrease in GFDL-CM3 (CM3), MIROC-ESM-CHEM (MEC) and MPI-ESM-LR (MLR) for all time periods. It can be seen that annual discharge will increase in all time periods for MIROC-ESM (MES) and MPI-ESM-MR (MMR). Considering a high-emissions scenario (RCP8.5), annual discharge is projected to increase in MPI-ESM-MR and to decrease in GFDL-CM3, MIROC-ESM-CHEM and MIROC-ESM. In case of MPI-ESM-LR, the climate model projections indicate a decrease of 10% for 2020s and increase of 19% and 33% for 2050s and 2080s respectively.

40 40 (a) 30 30 (b) 20 20 10 10 0 0

discharge (%) discharge -10 -10 discharge(%) -20 -20 Projected change of annual of change Projected

-30 2020s 2050s 2080s Projected change annualof -30 2020s 2050s 2080s -40 -40 CM3 MEC MES MLR MMR CM3 MEC MES MLR MMR

Fig.-2: Projected changes of annual discharge in 2020s, 2050s, 2080s relative to the base period (1981~2005) under (a) RCP4.5 (b) RCP8.5

The variation of intra-annual (monthly) discharge projected by two RCP scenarios for the period 2020s, 2050s, 2080s are presented in Fig.3. For 2020s the median values of discharge for each month projected under both RCP Hydropower'15

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scenarios for five GCMs are slightly less than the monthly discharge of base period. For 2080s, however, the median values of monthly discharge, especially in the wet season, projected under RCP8.5 scenario are slightly higher than the monthly discharge of base period. The variation in simulated monthly and annual discharge between global climate models used in this study is significant, as it shows the uncertainties in the direction and magnitude of the change. Moreover, the intra-annual (monthly) changes in the river discharge are greater as compared to the annual discharges changes. These results suggest that it is important for the hydropower planners to keep in mind the monthly changes and trends for the future planning.

1400 1400 Climate range 2020s Climate range 2020s 1200 1200 Climate median Climate median 1000 1000 Base period Base period 800 800 600 600 400 400 200 200 Monthly discharge (m3/s) 0 Monthly discharge (m3/s) 0

1400 1400 2050s Climate range 2050s 1200 Climate range 1200 Climate median Climate median 1000 1000 Base period 800 Base period 800 600 600 400 400 200 200 0 0 Monthly discharge (m3/s) Monthly discharge (m3/s)

1400 1400 Climate range 2080s Climate range 2080s 1200 1200 Climate median Climate median 1000 Base period 1000 Base period 800 800 600 600 400 400 200 200 0 0 Monthly discharge (m3/s) Monthly discharge (m3/s)

(b) (a) Fig.-3: Monthly discharge of base period (1981~2005) and variation of monthly discharge projected by (a) RCP4.5 (b) RCP8.5 for the period 2020s, 2050s and 2080s

4.2 IMPACTS OF CLIMATE CHANGE ON HYDROPOWER GENERATION ANALYSIS FOR YEYWA RESERVOIR

The comparison of changes of hydropower production from Yeywa reservoir under RCP4.5 and RCP8.5 scenarios are presented in Fig.4. From the Intercomparison point of view, the first observation is that the annual power Hydropower'15

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production projected by both scenarios is expected to increase under MPI-ESM-MR. This increase is projected for three time periods and varies from 3~21% under RCP4.5 and 5~23% under RCP8.5 scenario. For the other models, the annual hydropower generation is expected to decrease under GFDL-CM3 and MIROC-ESM-CHEM. However, the power production from MIROC-ESM shows increasing trend under RCP4.5 but decreasing trend under RCP8.5 scenario for three time periods. Moreover, MPI-ESM-LR gives mixed trend and varies depending on the time periods and RCP scenarios. The remarkable decreasing trend is observed in GFDL-CM3 and MIROC-ESM-CHEM with the ranges 13~25% and 15~26% respectively.

40 40 30 (a) 2020s 2050s 2080s 30 (b) 2020s 2050s 2080s 20 20 10 10 0 0 -10 -10 production (%) -20production (%) -20 -30 -30 Projected change powerof Projected change powerof -40 -40 CM3 MEC MES MLR MMR CM3 MEC MES MLR MMR Fig.-4: Projected changes in power production during three future periods relative to the base period for Yeywa reservoir under (a) RCP4.5 (b) RCP8.5

ANALYSIS FOR UPPER YEYWA RESERVOIR

Fig.5 shows the changes of future hydropower generation of Upper Yeywa reservoir relative to the base period (1981 to 2005). Similar to the findings in Yeywa reservoir, future hydropower generation is expected to decrease in GFDL-CM3 and MIROC-ESM-CHEM ranging 11~21% and 16~17% under RCP4.5 scenario and 18~23% and 12~16% under RCP4.5 scenario. However, other GCMs have mixed trend in future hydropower production. The largest increase of hydropower production is observed as 16% at 2080s of MIROC-ESM under RCP4.5 while the largest reduction is found in 2020s of GFDL-CM3 under RCP8.5. Results indicate that large uncertainties exist in the projected future hydropower generation due to differences between the climate model projections.

40 40 30 (a) 2020s 2050s 2080s 30 (b) 2020s 2050s 2080s 20 20 10 10 0 0 -10 -10 production (%) -20 -20production (%) -30 -30 Projected change powerof Projected change powerof -40 -40 CM3 MEC MES MLR MMR CM3 MEC MES MLR MMR Fig.-5: Projected change of power production of Upper Yeywa reservoir for (a) RCP4.5 (b) RCP8.5

5. CONCLUSION

This study presents the analyses of climate change impacts on hydropower generation of two reservoirs located in Myitnge river basin focusing on 1) downscaling of climate models 2) the hydrologic model setup and calibration for the watershed using HEC-HMS 3) the reservoir simulation model setup and calibration using HEC-ResSim 4)the impacts of future climate changes on the streamflow 5) the impacts of climate change on the hydropower generation of two reservoirs located in the Myitnge river basin. The following finding and conclusions were drawn from the analysis: Hydropower'15

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• The projected change of streamflow is not unidirectional: it varies depending on climate models, emission scenarios and time periods. The streamflow projection under GFDL-CM3 and MIROC-ESM-CHEM shows the decreasing trend in future whereas the projection under MPI-ESM-MR shows the increasing trend. • The hydropower generation of two reservoirs is predicted to increase under MPI-ESM-MR. However, a serious hydropower generation deficit is expected under GFDL-CM3 and MIROC-ESM-CHEM. • The results reflect that the appropriate adaption measure to the future climate change conditions and revision of the operation rule curves and design specifications are essential to optimal hydropower operations in the basin.

However, this study still has some limitations which can be addressed in future research. Since it is the first study of climate change impacts in Myitnge river basin, further studies are recommended using multiple hydrologic models and climate models which will provide more precise results. This study has not considered the changes in land use/ land cover due to socio-economic development in the future. Hence, further studies are recommended to quantify future change in streamflow and sedimentation load in the reservoir as well as its implication on future hydropower generation from Myitnge river basin.

REFERENCES

ADB (2009). Building Climate Resilience in the Agriculture Sector in Asia and the Pacific.Mandaluyong City, Philippines. Jamali, S., Abrishamchi, A., Madani, K., (2013). Climate change and hydropower planning in the Middle East: Implications for Iran’s Karkheh hydropower systems. Journal of Energy Engineering, 139, 153-160. Koch, F., P rasch, M., Bach, H., Mauser, W., Apple, F., Weber, M. (2011). How Will Hydroelectric Power Generation Develop under Climate Change Scenarios? A Case Study in the Upper Danube Basin. Energies 2011, 4, 1508-1541; doi:10. 3390/en4101508. Teutschbein, C. & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456–457 (2012) 12–29.