Impact and Risk Assessment of Climate Change on Hydropower. Production: A Case Study of the Upper Tamakoshi Project in Nepal by. Ajay Ratna Bajracharya.
Impact and Risk Assessment of Climate Change on Hydropower Production: A Case Study of the Upper Tamakoshi Project in Nepal
by
Ajay Ratna Bajracharya
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management
Examination Committee:
Nationality: Previous Degree:
Scholarship Donor:
Dr. Sangam Shrestha (Chairperson) Prof. Mukand S. Babel Dr. Sarawut Ninsawat
Nepalese Bachelor of Engineering in Civil Engineering Tribhuvan University Nepal AIT Fellowship
Asian Institute of Technology School of Engineering and Technology Thailand May 2015
Acknowledgment I would to extend my deepest appreciation and gratitude to my advisor, and the committee chair Dr. Sangam Shrestha for guiding me and providing valuable suggestions throughout the study period of my thesis work. I would like to express my sincere gratitude to Professor Mukand Singh Babel and Dr. Sarawut Ninsawat for giving valuable suggestions and remarks to improve the quality of the research. I am thankful to Department of Hydrology and Meteorology (DHM), Nepal for providing necessary hydrological and meteorological data. I really appreciate the help from Er. Bimal Gurung and Er. Rajesh Bhai Shilpkar of NEA for giving necessary information and data regarding the project. I would like to extend my appreciation to Mr. Jaya Ram Pudashine for helping in my thesis whenever required. Special thanks goes to Ms. Pajee Trakanpasakul for being helpful in all the administrative work and schedules. I am really humbled by the continuous support and help I got from my friends in AIT and friends back home in Nepal. Above all, I am thankful to my loving parents who supported me financially and morally to pursue my master’s degree in AIT and have always been the source of inspiration in every steps.
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Abstract Climate change and its impact on river hydrology has been a major concern to hydropower development in a mountainous watershed. This study investigates the impact of climate change in the hydropower production of one of the under construction hydropower project in Tamakoshi basin and the risk associated with the production of energy in the future period due to climate change. MIROC-ESM, MRI-CGCM3 and MPI-ESM-M GCMs, which are used in the Coupled Model Intercomparison Project phase 5 (CMIP5) based on Representative Concentration Pathways (RCP) scenarios were used for the future projection of temperature and precipitation. The projection of precipitation for the future period are not certain and varies seasonally according to different GCMs without any trend. The average annual precipitation in the future period is expected to vary between -8% to +24.8%. Minimum and maximum temperature are projected to increase by up to 6.33 ºC and 3.82 ºC respectively during 2090s. The analysis of the streamflow using SWAT model for the future timeline shows significant change in the discharge of the river at the intake dam site. Discharge at the intake dam site is expected to vary between minimum change of -9.8% to maximum increase in average annual discharge by +47%. Due to change in the streamflow, annual energy production is also expected to change accordingly. The future average annual energy production is expected to vary between -7.6 % to +15.8 %. By using three GCMs, the sensitivity of the risk associated with the annual hydroelectric production under altered runoff were analyzed. The risk percentage in the future period shows mild risk varying from 0.69 % to 6.63% with MPI-ESM-M expecting higher percentage of risk for energy production during the future period, when compared with the baseline energy production of 1963 GWh. Mild to moderate risk can be expected when the energy production in the future period is compared with the baseline energy production of 2281 GWh.
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Table of Contents CHAPTER
TITLE Title Page Acknowledgment Abstract Table of Contents List of Figures List of Tables List of Abbreviations
PAGE i ii iii iv vi ix xi
1
Introduction 1.1 Background 1.2 Rationale 1.3 Objectives 1.4 Scope of the study 1.5 Limitation of the Study
2
Literature Review 2.1 Hydropower 2.2 Climate Change 2.3 Climate Change in Nepal 2.4 Impact of Climate Change in Hydropower 2.5 Climate Models 2.6 Climate Change Scenarios 2.7 Downscaling of GCM 2.8 Hydrological Modeling 2.9 Flood Frequency Analysis 2.10 Risk Analysis
4 4 8 10 11 14 15 16 19 20 21
3
Methodology 3.1 Study Area 3.2 Data Collection 3.3 Methodological Overview 3.4 Selection of GCM 3.5 Downscaling of GCM Data 3.6 Hydrological Modeling 3.7 Evaluation of Performance of SWAT Model 3.8 Assessment of Climate Change Impact on Tamakoshi HEP 3.9 Flood Frequency Analysis 3.10 Risk Analysis
22 22 23 28 29 29 30 32 33 34 35
4
Results and Discussion 4.1 Extreme Climate Indices 4.2 Bias correction 4.3 Bias correction of Precipitation and Temperature
37 37 39 39
iv
1 1 2 3 3 3
4.4 4.5 4.6 4.7 4.8 4.9 5
Future projection of precipitation and temperature Hydrological Modeling Impact of Climate Change on Streamflow Flood Frequency Analysis Impact of Climate Change on Hydropower Production Risk Analysis
42 50 56 60 63 69
Conclusion and Recommendations 5.1 Conclusion 5.2 Recommendations
76 76 77
References Appendix A Appendix B Appendix C Appendix D
78 84 91 93 95
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List of Figures FIGURE 2.1 2.2 2.3
2.4
2.5 2.6 2.7 2.8 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4.1 4.2
4.3 4.4 4.5 4.6 4.7 4.8 4.9
TITLE
PAGE
Overview of classification of hydropower Multiple complementary indicators of a changing global climate Comparison among flow duration curves for observations and the current scenario and for the future and current scenarios of the Alcantra River basin, Sicily, Italy Change in water level in reservoir during power generation for different operation time, climate scenario and future time frame with respect to guide curve An RCM domain embedded in a GCM grid RCP Scenarios Schematics of general downscaling approach Quantile mapping technique for bias correction Location map of Tamakoshi basin showing hydrological and meteorological stations Historical annual rainfall of Jiri station from 1977 to 2013 Historical annual rainfall of Charikot station from 1977 to 2013 DEM of Tamakoshi basin and hypsometric elevation distribution Land use map and Soil map of Tamakoshi basin Land use re-classification and soil map re-classification obtained from SWAT model of Tamakoshi basin Methodology overview flowchart Methodological framework for hydrological modeling in SWAT Comparison of monthly mean precipitation of Jiri Station before and after bias correction of GCMs Comparison of monthly mean maximum temperature and monthly mean minimum temperautre of Jiri Station before and after bias correction of GCMs Future projected annual precipitation anomaly of Jiri station with respect to baseline period (1975-2005) for different GCMs Future projected annual precipitation anomaly of Charikot station with respect to baseline period(1975-2005) for different GCMs Percentage change in future precipitation at Charikot station with respect to baseline period (1975-2005) for different GCMs Percentage change in future precipitation at Jiri station with respect to baseline period (1975-2005) for different GCMs Range of change in seasonal mean precipitation for different scenarios and GCMs for Jiri station Range of change in seasonal mean precipitation for different scenarios and GCMs for Jiri station Range of change in seasonal mean maximum temperature for different scenarios and GCMs for Jiri station vi
4 9 11
12
14 15 16 19 22 24 24 25 26 26 28 31 40 41
42 43 43 44 45 46 58
4.10 4.11
4.12 4.13 4.14 4.15 4.16
4.17 4.18 4.19 4.20 4.21 4.22
4.23 4.24 4.25 4.26 4.27
4.28
Maximum average annual Temperature Anomaly of Jiri station with respect to baseline period (1975-2005) for different GCMs Future projected change in maximum temperature and minimum temperature with respect to baseline period (1975-2004) of Jiri station based on different GCMs Daily observed and simulated streamflow at the outlet of the watershed after calibration for 2004-2008 Daily observed and simulated streamflow at the outlet of the watershed after validation for 2000-2001 Daily observed and simulated streamflow at the intake dam site after calibration for 2001-2004 Daily observed and simulated streamflow at the intake dam site after validation for 2005-2006 Relative change in streamflow at Lamabagar intake site for future period based on (a) MRI-CGCM3 (b) MIROC-ESM and (c) MPIESM-M relative to baseline period (1975-2004) Comparison of annual average discharge with baseline period at Lamabagar intake site for future period using different GCMs Maximum probable flood at intake dam site of Lamabagar for different scenario and period projected by MIROC-ESM Maximum probable flood at intake dam site of Lamabagar for different scenario and period projected under MRI-CGCM3 Maximum probable flood at intake dam site of Lamabagar for different scenario and period projected under MPI-ESM-M. Mean monthly energy vs mean monthly flow at Lamabagar intake dam site for baseline period Comparison of energy generation from Upper Tamakoshi hydroelectric project between observed condition and GCM simulated condition for different GCMs Expected annual average energy production for future period under RCP 4.5 and RCP 8.5 scenarios Risk Vs annual energy production during 2030s under RCP4.5 and RCP 8.5 scenarios of different GCMs Risk Vs annual energy production for the period of 2030s, 2060s and 2090s for RCP 4.5 and RCP 8.5 scenarios of MRI-CGCM3 Risk Vs annual energy production for the period of 2030s, 2060s and 2090s for RCP4.5 and RCP 8.5 scenarios of MPI-ESM-M Percentage risk associated with hydropower production in future period relative to baseline of 1963 GWh for different GCMs and RCP scenarios Percentage risk associated with hydropower production in future period relative to baseline of 2281 GWh for different GCMs and RCP scenarios vii
49 49
52 52 53 53 56
57 60 60 61 64 66
68 70 70 71 72
73
4.29 4.30
4.31
Risk levels based on changes in performance indicators Risk in terms of loss in USD in millions associated with hydropower production in future period relative to baseline of 1963 GWh and 2281 GWh for different GCMs and RCP scenarios Averaged percentage risk associated with different level of installed capacity of hydropower plant for different GCMs
viii
74 75
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List of Tables TABLE 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 4.1 4.2 4.3 4.4
4.5 4.6
4.7
4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15
TITLE
PAGE
Major hydropower plants in operation in Nepal Under Construction Hydropower Plants in Nepal Summary of recent studies on impact of climate change on hydropower production RCP scenarios Meteorological stations in Tamakoshi basin used in the study Hydrological stations in Tamakoshi basin used in the study Salient features of Upper Tamakoshi hydroelectric project GCMs, their sources, resolutions, vintage and data sources The mean values of the monthly distribution coefficients (bt) based on historical simulated energy production Temperature indices for Jiri station based on historical data from 1975 to 2013 Precipitation indices for Charikot and Jiri station based on historical data from 1975 to 2013 Evaluation of bias correction for precipitation of MIROC-ESM, MRI-CGCM3 and MPI-ESM-M GCMs for Charikot Station Evaluation of bias correction for precipitation and temperature of MIROC-ESM, MRI-CGCM3 and MPI-ESM-M GCMs for Jiri Station Calibrated parameters, sensitivity analysis and their default values for SWAT model simulations Model performance of daily streamflow during calibration period (2004-2008) and validation period (2000-2001) for outlet at Busti station Model performance of daily streamflow during calibration period (2004-2008) and validation period (2000-2001) for outlet at intake dam site at Lamabagar station General performance rating for recommended parameters for streamflow Annual average water balance components of the basin based on calibration period at intake site of Lamabagar from 2001 to 2004 Percentage change in the future projected discharge at Lamabagar intake site with respect to baseline period based on MIROC-ESM Percentage change in the future projected discharge at Lamabagar intake site with respect to baseline period based on MRI-CGCM3 Percentage change in the future projected discharge at Lamabagar intake site with respect to baseline period based on MPI-ESM-M Percentage change in maximum probable flood at intake dam site for future scenarios compared to baseline using different GCMS Comparison of future projected energy generation relative to baseline energy production of 1963 GWh for different GCMs Comparison of future projected energy generation relative to baseline energy production of 2281 GWh for different GCMs
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7 7 13 15 23 24 27 29 36 37 38 39 40
51 54
54
54 55 58 58 59 62 63 64
4.16 4.17 4.18 4.19 4.20
Expected energy production (GWh) from Upper Tamakoshi hydroelectric project for future period based on MIROC-ESM Expected energy production (GWh) from Upper Tamakoshi hydroelectric project for future period based on MRI-CGCM3. Expected energy production (GWh) from Upper Tamakoshi hydroelectric project for future period based on MPI-ESM-M The risk values (%) associated with the mean annual energy production of 1963 GWh for different GCMs and scenarios The risk values (%) associated with the mean annual energy production of 2281 GWh for different GCMs and scenarios
x
65 65 66 72 73
List of Abbreviations BCSD CGIAR CFC CMIP5 CO2 DEM DHM FAO GCM GHG GWh GIS IPCC KW NEA NSE MW PET PROR RCM RCP ROR SOTER SRES SRTM STO TRMM SWAT UNDP UNEP UNFCCC UTHEP WMO
Bias Correction Statistical Downscaling Consortium CEO Frank Rijsberman Chloro Fluoro Carbon Coupled Model Inter-comparison Project Phase 5 Carbon dioxide Digital Elevation Model Department of Hydrology and Meteorology Food and Agricultural Organization Global Circulation Model Green House Gases Giga Watt Hour Geographic Information System Intergovernmental Panel on Climate Change Kilo Watt Nepal Electricity Authority Nash-Sutcliffe Simulation Efficiency Mega Watt Potential Evapotranspiration Peaking Run of the River Regional Climate Model Regional Concentration Pathways Run Off River Soil and Terrain Database Programme Special Report on Emission Scenarios Shuttle Radar Topography Mission Storage Tropical Rainfall Measuring Mission Soil Water Assessment Tool United Nations Development Programme United Nations Environment Programme United nations Framework Convention on Climate Change Upper Tamakoshi Hydroelectric Project World Meteorological Organization
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CHAPTER 1 INTRODUCTION Background According to the fifth assessment report of IPCC, “Climate change is characterized as measurably significant variation in either the variability or mean condition of the atmosphere proceeding for a delayed period, for the most part decades or more”. Climate change and global warming has been one of the major concerns all over the world, as the effects of climate change and global warming are getting more intense and alarming in the past few decades. Anthropogenic causes of greenhouse gas emission is considered as the driving factor of the climate change though there are several other factors, which are causing the climate to change (Stocker et al., 2013). Greenhouse gases can be produced from different anthropogenic sources such as industries, transportation, burning of fossil fuels and use of nitrogen based fertilizers (Watson, 1992). The impact due to climate change is being more severe and visible in the past few decades around the globe. The impact of climate change is even more severe in poor and developing countries with poor technology and economic development. Climate change will change the hydrologic cycle, which are affected, by the change in climate variables such as precipitation, evaporation, runoff, relative humidity, solar radiation, greenhouse gas concentrations, etc. The glaciers in the Himalayan region and the ice of Artic region are retreating and disappearing in an unprecedented rate due to climate change and global warming. Hydropower is the most generally utilized renewable source of energy and assumes an imperative part in the financial and social improvement around the globe (Schaefli et al., 2015). The production of greenhouse gases from hydropower is very low compared to other sources of energy and has a very long useful life as well. Hydropower can be a contributor in bringing down the level of greenhouse gases by replacing the other sources of energy production emitting high concentration of methane and carbon dioxide gases. However, hydropower is also one of the sectors, which is affected by the impact of climate change, as the power production is dependent on the discharge of the river contributed by runoff from precipitation and melting of the glaciers. Because of climate change, the volume and river flow timing are affected together with the change in reservoir evaporation, which can cause significant effect in the hydropower production. Climate change is a global issue and its impact in water resources and hydropower generation cannot be ignored. There are certain risk associated with the impact of climate change in different sectors including the risk associated with the energy production from hydropower. In the connection of climate change, the intensity and frequency of extreme events, for example, surges and dry seasons will build which could put colossal difficulties in water assets administration in the future period (Devkota, 2014). Changing atmosphere variables, for example, temperature, precipitation, and the number of wet days demonstrate a positive association with surge and extreme events where it can harm the physical foundations, loss of farming and agricultural generation, land, and expand water borne maladies (Devkota, 2014).
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Rationale Nepal is rich in water resources. Most of the surface water in Nepal are in the form of rivers, which are contributed by the monsoon precipitation and melting of the glaciers and snow from the higher Himalayas. Himalayan region is one of the vulnerable regions that are affected by the climate change. Most of the glaciers, which are the source of the runoff river are retreating in an alarming rate due to climate change. The phenomena of snowmelt process also plays a significant role in the hydrological cycle. The impact of the climate change in the rivers is also significantly affected by the change in the precipitation pattern. Changes in the precipitation pattern are likely to have impact on the hydrology of the river basins. Several study carried out in the watershed of Nepal and around the globe shows significant warming and rise in temperature in the future period. The average trend of rise in temperature for Nepal is 0.06 ̊C per year (Shrestha and Aryal, 2011). The warming rates are even more significant at high elevation locations, where the snowfall and snowmelt processes affect the streamflow (Shrestha and Aryal, 2011). Climate change and its impact on river hydrology has been a major concern to hydropower development. Fluctuations in river discharge can have significant impact on the production of hydropower energy. The production of energy from hydropower plant depends upon the seasonal flow of the river. Change in the hydrological cycle and climate variables like precipitation and temperature will have a major effect on the season flow of the river and hence in the production of the hydropower energy. Both storage type of hydropower plant and run of the river hydropower plant are affected by climate change. Run of the river type of hydropower are more sensitive to the impact of climate change as the storage type of hydropower plant provides more flexibility in the operation of the hydropower energy generation by providing storage (Hamududu et al., 2012). Tamakoshi basin is one of the sub-basin of Koshi basin in Nepal. Tamakoshi basin is one of the important basins for hydropower generation. The river and streams in the basin are both glacier fed as well as rain-fed and runoff. As the climate change is causing a huge impact in change in the glacier meltdown and change in the precipitation pattern, it will also affect the production of hydropower in the basin. This research tries to quantify the impact of climate change in one of the hydropower plant of the basin. Upper Tamakoshi Hydroelectric Project (456 MW) is Nepal’s largest hydropower which is under construction and expected to be commissioned in 2016. This study is important, as no such research has been carried out to access the impact of climate change and risk analysis in such a big hydropower project in Nepal. The study of climate change in Upper Tamakoshi hydroelectric project along with the risk associated will be important for the policy makers and engineers to develop adaptation measures to cope with the impact of climate change in the hydropower production. Hydropower plant structures are designed for certain flood return period. The return period are calculated based on the historical data. However, due to impact of climate change and change in river flow, the return period are expected to change. Changing atmosphere variables can have a positive association with surge and extreme events where it can harm the physical foundations, loss of farming generation, land, and expand water borne maladies (Devkota, 2014). It is hence important to carry out the flood analysis for the future period so that necessary precautions and modifications can be implemented to safeguard the hydropower structure.
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Objectives The general objective is to access the risk to hydropower production due to climate change in the future period based on GCM data. The specific objectives of the study are as follows: To project the future climate scenario for the study basin based on selected GCMs. To study the impact of climate change on the streamflow at the intake dam site. To evaluate the impact of climate change on the power generation of Upper Tamakoshi Hydro Electric Project. To evaluate the risk analysis of climate change on hydropower production of Upper Tamakoshi Hydroelectric project. Scope of the Study The scope of the research are as follows: Download of GCMs output from IPCC data distribution center. Selection of suitable GCM for the study of the basin after statistical analysis. Downscaling of temperature and precipitation data from selected GCMs to predict the future temperature and precipitation. Setting up model to predict future runoff of the basin using SWAT. Calibration and verification of SWAT model. Analysis of change in the hydropower production capacity of Upper Tamakoshi Hydroelectric Project under future projected temperature and precipitation from different GCMs. Gumbel distribution for flood frequency analysis. Risk analysis of impact of climate change on hydropower generation of Upper Tamakoshi hydroelectric project Limitation of the Study The limitation of the study are as follows: Only three GCMs and two RCP scenarios were used. The future land use change was not considered for the analysis. Climate variables other than rainfall and precipitation such as relative humidity, wind speed and solar radiation were not projected for future period.
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CHAPTER 2 LITERATURE REVIEW 2.1
Hydropower
2.1.1
Definition
Hydropower is defined as the energy, which can be generated by converting the potential energy of the flowing water into kinetic energy by allowing the water to fall through a certain head. The kinetic energy is later converted into mechanical energy first by passing it through turbine, which is later converted into electrical energy with the help of transformer or generator. Hydropower contributes around 16.3% of the total energy production globally (Steinhurst et al., 2012). 2.1.2
Classification
Hydropower plants can be generally classified according to following Criteria (Majumder and Gosh, 2013): a) Quantity of water available b) Available Head c) Nature of Load Hydropower
Quantity of Water Available
Available Head
Nature of Load
Run of the River Plants Peaking Run of the River Plants Reservoir Plants
Low Head (1000m)
Base Load Plants
Peak Load Plants
Figure 2.1 Overview of classification of hydropower (Majumder and Gosh, 2013).
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a) Classification According to Quantity of Water Available i.
Run of the River Plants Run of the river hydropower plants are completely dependent upon the flow of the river as no storage is required in this type of hydropower plant. ROR type of hydropower plant can generate maximum energy during wet season and low energy are generated during dry season.
ii.
Peaking Run of the River Plants In this type of hydropower plant, water is stored by creating a storage during the low demand periods and used during the peak periods when the demand is high.
iii.
Reservoir Plants Reservoir or storage type of hydropower plant requires construction of large dam to store water, which can be used for energy generation accordingly in dry and wet seasons.
b) According to Available Head According to available head hydropower can be classified into i) Low Head ii) Medium Head and iii) High Head (Majumder and Gosh, 2013). Majumder and Gosh (2013) classified a hydropower plant to be a Low Head hydropower plant if the head is very low head (less than 30 m) to produce electricity. Likewise, the classification shows the head to vary between 30 m and 300m to be a medium head hydropower plant and the head greater than 300 m is classified as high head hydropower plant (Majumder and Gosh, 2013). Medium and high head hydropower plants usually requires construction of high dams to divert or store water for electricity production. c) According to Nature of Load According to nature of load, hydropower plant can be classified as i) Peak Load Plant and ii) Base Load Plants (Majumder and Gosh, 2013). The peak hydropower plant supplies power during the period when there is peak demand, which requires construction of reservoir to store water whereas the base load hydropower plant can supply the power regardless of the demand (Majumder and Gosh, 2013).
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2.1.3
Hydropower Development in Nepal
Nepal is rich in water resources with huge amount of potential for hydropower generation due to its topography and landscape. Dr. Hariman Shrestha in his PHD study carried out the first technical analysis of hydropower potential in Nepal in 1966 estimating a theoretical capacity of 83,000 MW and economic capacity of 42,000 MW (Shrestha, 2015). A recent study has shown that the total capacity of hydropower generation based on run of the river basis at 40% exceedance and 80% efficiency to be 53,834 MW, with annual energy potential of 346,538GWh (Jha, 2010). The hydropower potential will increase considerably if storage type of hydropower plants are considered. The history of hydropower production in Nepal is more than 100 years old. However, the development of hydropower in Nepal is quite a setback as the current power production from hydropower is mere 718 MW with 43 hydropower projects under operation (DOED, 2015). Indeed, over the past few years there has been a severe electricity deficit in the country. Power cut down up to 16 hours per day in the winter of 2008 led to declaration of national energy crisis (Sharma and Awal, 2013). The country is still facing power cut of 12 hours to 14 hours per day during the peak dry season. The history of development of hydropower in Nepal is more than 100 years old with the operation of Pharping Hydropower Plant (500 KW) in 1911. The second hydropower plant was built only in 1934 with 600 kW of installed capacity. At that time, the hydropower were constructed only for private use by the rulers of the kingdom, and the public had no access to the produced electricity (Sharma and Awal, 2013) The construction of the 2.4 MW Panauti hydropower project took place in 1965 after which the public had general access to electricity. Kulekhani-I is the only one reservoir type hydroelectric project in Nepal with the installed capacity of 60 MW, which came into operation in 1982. After the political system was restructured in the country in 1990, it created an opportunity to the investors to invest in the hydropower sector. The hydropower sector was opened for private investment, which was otherwise managed by government-linked Nepal Electricity Authority (NEA) only (Sharma and Awal, 2013). Even after 1990, the investment from the private sectors was not satisfactory as the policies were at the new stage and there was no significant development in hydropower for more than a decade (Sharma and Awal, 2013). However, Nepal Electricity Authority were successful in construction of two big hydropower projects: 144 MW Kali Gandaki A and 70 MW Middle Marsyangdi, which were completed in 2002 and 2008 respectively. The major hydropower plants, which are under operation and construction, are shown in Table 2.1 and Table 2.2. There are several proposed and planned hydropower projects to be constructed in the future to increase the hydropower generation. Some of the hyped planned and proposed hydropower plants to be constructed in coming years are Budhi Gandaki (600 MW), Upper Seti (128 MW), Trishuli (128 MW) and Upper Modi A (42 MW) (Shrestha et al., 2014).
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Table 2.1 Major hydropower plants in operation in Nepal (DOED, 2015) Hydropower Project
Capacity (MW)
River
Kali Gandaki A Madhya Marsyangdi Marsyangdi Khimti -I Devighat Gandak Kulekhani-I Kulekhani-II Sun Koshi
144 70 69 60 14 15 60 32 10
Kali Gandaki Marsyangdi Marsyangdi Khimti Trishuli Narayani Kulekhani Kulekhani Sun Koshi
Trishuli Jhimruk Khola Modi Khola Upper Bhotekoshi Chilime Sipring Khola Lower Modi -1
24 12.5 14.8 45 22 10 10
Trishuli Jhimruk Modi Bhote Koshi Chilime Sipring Modi
Table 2.2 Under construction hydropower plants in Nepal (DOED, 2015) Hydropower Project Chamelia Trishuli 3-A Kulekhani III Rahughat Upper Tamakoshi Sanjen Upper Sanjen Rasuwagadhi Bhotekoshi
Capacity (MW) 30 60 14 30 456 14.3 42 102 111
Type ROR ROR STO ROR PROR ROR ROR ROR ROR
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2.2
Climate Change
The climate system generally consists of different sub-systems such as the atmosphere, the hydrosphere, the cryosphere, the land surface and the biosphere, induced by several external compelling components (Baede et al., 2001). According to IPCC “Climate is defined as the average weather, which is specially described statistical in term of mean and variability over a long period ranging from months to millions of years.” WMO has suggested the period of 30 years for analysis of the climate, averaging the climate variables. According to UNFCCC, “Climate change is defined as a change of climate, which is caused directly or indirectly by human activity, is responsible to change the composition of the global atmosphere, and is in addition to natural climate variability observed over analogous periods.” The change in climate can occur naturally as well as due to external causes such as the anthropogenic causes, which are created by human actions and activities affecting the natural system and climate. The climate of earth has been evolving and changing continuously over the period of millions of years. However, the scientist of today are more concerned with the rate of change of climate in the 21st century. The industrial revolution began in 1800s, which led to extensive use of fossil fuels and natural resources (Baede et al., 2001). The development trend progressed with the advancement in new technologies and new inventions with which began global rapid urbanization. Burning of fossil fuels will lead to release of greenhouse gases like carbon dioxide and methane into the atmosphere. Carbon dioxide emissions contributes more than 80% to global warming (Lashof and Ahuja, 1990). Since the 1800s, carbon dioxide concentrations have been increasing rapidly from 280 ppm to around 400 ppm, which is a direct result of the anthropogenic causes. According to WMO reports, for the first time, carbon dioxide levels reached a record-breaking milestone, its monthly concentrations topping 400 ppm in April throughout the Northern Hemisphere in 2014. The global surface temperature is expected to exceed 1.5 ºC relative to 1850 to 1900 for all RCP scenarios except RCP 2.5 by the end of the 21st century (Stocker et al., 2013). The IPCC report shows that the global temperature is expected to exceed 2 ºC for RCP 6.0 and RCP 8.5 scenarios. Climate change can have a tremendous impact in the global water cycle and precipitation pattern due to the rise in the global temperature. The difference in precipitation between wet and dry regions and seasons are expected to increase with exception to some regions of the world (Stocker et al., 2013).
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Figure 2.2 Multiple complementary indicators of a changing global climate (IPCC, 2013)
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2.3
Climate Change in Nepal
Nepal has a subtropical monsoon climate. Elevation of Nepal varies from 60 m in the South to 8850 m to the North. Temperature and precipitation including other climatic variables changes with the difference in elevation due to which Nepal has a diverse climate condition ranging from tropical climate in the South to alpine climate in the higher northern regions (Shrestha and Aryal, 2011). The temperature can be expected higher in the Southern plain upto 47° C, while the temperature in the hilly region ranges from 0°C to 28° C, and lower temperature can be experienced in the Himalayas at the north. Annual average rainfall of Nepal is around 1700 mm (Shrestha et al., 2000). Relative to the baseline period of 1960s, the temperature rise between 0.5 ̊C – 0.6 ̊C per decade were observed between 1977 and 1994 (Dhakal, 2003). Mean rainfall showed decreasing trend of 3.7 mm per month per decade, which are more noteworthy during the monsoon season (Dhakal,2003). Climate change causes change in the distribution and intensity of temperature and precipitation. A study carried out in Koshi basin shows increase in projected precipitation during 2030s and 2050s under A2 and B1 scenarios in most of upper watersheds. Lower sub watersheds are expected to experience decrease in precipitation during 2030s (Bharati et al., 2014). Due to change in temperature and precipitation pattern, it will directly affect the water resources and hydrology of the basin. A recent study in Bagmati river basin of Nepal shows the potential hydrological impact of future climate. The projection shows increase in temperature of the basin, with significant increase in summer temperature for A2 and B2 scenarios (Babel et. al, 2013). The projection shows increase in the average annual water availability of the basin in the future, which may rise up to 12.84%. The impact of climate change is even more evident in the Himalayan region where the glaciers are retreating at an alarming rate due to global warming. One of the study of the Imja glacier shows retreat of the glacier by 42 m per year from 1962 to 2000 (Bajracharya et al., 2007). The same glacier was found to retreat by staggering average rate of 74 m per year from 2001 to 2006 (Bajracharya et al., 2007). Most of the lakes have disappeared in the Dudh koshi basin of Nepal by 37% but the total area were found to increase by 21% (Bajracharya et al., 2007). Shea et al. (2014) carried out simulation of future glacier change in the Everest region using a glacier mass balance model. RCP 4.5 and RCP 8.5 scenarios of the CMIP5 models were used for projection of future temperature and precipitation. The study shows that the glaciers are affected by the change in temperature and equilibrium line altitude (ELA) (Shea et al., 2014). Volume of glaciers are projected to reduce up to -62% during the period of 2050s. Maximum loss in glacier volume can be expected by the end of the century up to -96% if the projection of temperature in the future is considered to be hold true (Shea et al., 2014).
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2.4
Impact of Climate Change in Hydropower
It is evident that the precipitation pattern and temperature including other climatic variables are changing and are expected to change in the future as well. Change in precipitation pattern and rise in temperature influence the discharge in the river. The generation of hydroelectric power is dependent upon the discharge of the river. Hence, the change in climate will have effect on hydropower generation. The operation of hydropower plant depends on climatic factors and is sensitive to change in climate variables (Robinson, 1997). Several studies have been carried out to access the impact of climate change in hydropower production and hydropower potential. Production of hydropower is directly dependent upon the discharge and flow in the river. It is evident through different research that climate change affects the discharge and flow in the river. Aronica and Bonaccorso (2013) accessed climate change effects on hydropower potential in the one of the basin in Sicily (Italy) due to the impact of climate change on the hydrology of the basin. The analysis is based on Monte Carlo simulations. The effects of climate change on hydropower generation is analyzed by detecting the change in the flow duration curve and utilization curves (Aronica and Bonaccorso, 2013). The impact of climate change on the hydropower potential was analyzed by studying the change in the flow duration curve. The research shows decrease in hydropower potential due to the impact of climate change for the future scenario.
Figure 2.3 Comparison among flow duration curves (left) for observations and the current scenario and (right) for the future and current scenarios of the Alcantra River basin, Sicily, Italy (Aronica and Bonaccorso, 2013).
12
In a study carried out in Vietnam, the hydroelectric production of Dak Nong wateshed of Vietnam was observed to decrease by 12% in the future period of 2030s due to the impact of climate change (Bang et al., 2013). However, the SWAT model simulated the streamflow during 2030s to be higher by 16.8% relative to the baseline period. The results also showed that the climate change has no impact on the safety of the hydropower dam (Bang et al., 2013). The study used the application of GIS and SWAT model to access the impact of climate change in hydropower production. The future analysis is carried out up to the period of 2030. A study carried out in Kulekhani Hydropower project (60 MW) of Nepal was carried out to study the impact of climate change in the river hydrology of the Kulekhani watershed and power generation from the project. The impact was accessed with reference to the baseline period of 27 years, which shows decrease in power production by up to 30% when the power plant is operated for 7 hours a day. The reduction in the hydropower production was found to decrease by up to 13% when the hydropower plant is operated for 10 hours a day during dry months and 3 hours a day during the wet months (Shrestha et al., 2014). HadCM3 GCM under A2 and B2 scenarios were used for future projection of temperature and precipitation.
Figure 2.4 Change in water level in reservoir during power generation for different operation time, current and future climate scenario with respect to guide curve (Shrestha et al., 2014).
13
Table 2.4 Summary of recent studies on impact of climate change on hydropower production Objective To study the impact of climate change in the river hydrology of the Kulekhani watershed and power generation from the project
Study Area
GCM/RCM and Conclusion References Scenarios Kulekhani HadCM3 A2 and B2 The result shows decrease in power production by Shrestha et al., 2014 Watershed, Bagmati Scenarios up to 30% when the power plant is operated for 7 River Basin, Nepal hours a day. The reduction in the hydropower production was found to decrease by up to 13% when the hydropower plant is operated for 10 hours a day during dry months and 3 hours a day during the wet months
To analyze the impact of California, USA climate change on hydropower generation at the high elevation area of California
GFDL-A2, PCM-A2
The impact of climate change on hydropower Madani et al., 2014 generation, reservoir storage along with economic impact was carried out in the study. The study shows increase in hydropower generation, energy spills, and revenues for wet scenario but opposite effect for dry scenario.
To analyze the impact of La Plata River Basin, PROMES-UCLM climate change on hydrology South America RCA-SMHI of the basin and on hydropower production
The hydropower generation is expected to change Popescu et al., 2014 in the future. The result shows increase in hydropower potential of the basin in the future period. However, the PROMES model shows decrease in hydropower potential in the northern and southern areas of the basin.
To present a global study of Europe, America, CGCM3,FGOALS,GF impacts of climate change on Africa, Australasia, DLCM2.0,GFDL hydropower generation Asia, Oceania CM2.1,GISSEH,HadC M3,HadGEM1,MIRO C3.2,MPEH5,MRICGCM2,CCSM3 A1B Scenario
The study shows positive impact due to climate Hamududu and change on the hydropower production in most of Killingtveit, 2012 the regions of the globe. Hydropower generation is expected to increase by 0.46 TWh annually during 2050. However, the both positive and negative changes are expected to occur for different countries and regions all over the world.
14
2.5
Climate Models
According to the Fifth Assessment Report – AR5 in 2013 of the Intergovernmental Panel of Climate Change (IPCC), “Climate models are the tools, which can be used to simulate the interactions among different components of the earth such as atmosphere, oceans, land surface and ice”. Climate models can be used to study the dynamics of the climate system and to study the projections of different future climate variables. The climate models are mostly based on temperature projection and increase in greenhouse gases in the atmosphere. Climate models are frequently used to project the past and future climate variables with higher level of confidence. However, the projection of precipitation has lower level of confidence with respect to temperature (Solomon et al., 2007). According to the Fifth Assessment Report of IPCC, “The models used in the climate research varies from simple energy balance models to complex Earth System Models (ESMs) which require high performance computing”. Global Circulation Model (GCMs) are widely used Climate Models. GCMs have coarser resolutions with hundreds of kilometers and it range from 300 km to 500 km (Wilby et al., 2004). GCM have quite low resolution and coarse in nature due to which the raw data from GCM cannot be used directly for the impact analysis of climate change on local and regional scale such as regional streamflow, hydrological processes and flood analysis (Teutschbein et al., 2011; Wilby and Wigely, 1997). Regional climate model (RCM) are frequently used to dynamically downscale the climate variables from GCMs for local geographical region to give more itemized information (Laprise, 2008; Stocker et al., 2013). Compared to GCMs, RCMs can resolve mesoscale forms over a restricted geological region of interest, with less processing expense and exertion (Laprise, 2008).
Figure 2.5 An RCM domain embedded in a GCM grid (Giorgi et al., 2008).
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2.6
Climate Change Scenarios
2.6.1
SRES Scenarios
The Intergovernmental panel on Climate Change (IPCC) developed long-term emissions scenarios in 1990 and 1992 called as SRES (Special Report Emission Scenarios) which were widely used in the climate change analysis, impact assessment and for developing adaptation options (Nakicenovic, 2000). The SRES scenarios cover a wide range of the main driving forces of future emissions, from demographic to technological and economic developments (Nakicenovic, 2000). There are altogether 40 SRES scenarios developed under four storylines. The four major storylines are A1, A2, B1 and B2 scenarios. According to the special report on emissions scenarios of IPCC, A1 scenario is based on the rapid growth of world economy and global population with technology getting more advanced and efficient, but predicts degradation of the environment whereas A2 storyline assumes people’s importance on family values, ethics and tradition with less concern to economic development. According to the report, B2 storylines assumes a convergent world with similar global population as A1 storyline, but with more economic development and clean technology innovation. B2 storyline assumes a world with intermediate population and economic growth with priority given to environmental protection and social equity (Nakicenovic, 2000). 2.6.2
RCP Scenarios
According to Fifth Assessment Report – AR5 of IPCC, “The Representative Concentration Pathways (RCPs) are the new set of scenarios used for the new climate model simulations carried out under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) of the World Climate Research Programme”. RCP scenario does not include the socioeconomic, emissions and climate projections scenarios like in SRES (Special Report Emission Scenario) scenarios. The projections are based on radiative forcing components (Moss et al., 2010). There are four RCP scenarios defined according to their total radiative forcing in 2100. Table 2.5. RCP scenarios (Moss et al., 2010)
Figure 2.6 RCP scenarios
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2.7
Downscaling of GCM
GCMs have very low resolution and coarse in nature. The climate change information required for the impact studies at local scale is much finer than that obtained from GCMs and RCMs (Wilby et al., 2004). Therefore, due to mismatching of the scales, coarse resolution GCMs output cannot be used directly for hydrological impact studies but requires to be downscaled (Nakicenovic et al., 2011).
Figure 2.7 Schematics of general downscaling approach (Wilby and Wigley, 1997) The fine scale climate variables required for local level impact studies can be obtained by downscaling of GCM simulations, which can be done either by Statistical downscaling or by a dynamical downscaling approach (Teutschbein et al., 2011). Downscaling results from Global atmosphere models to individual destinations or territories for effect studies will at present be obliged regardless of the possibility that the GCMs later on are run at high resolution (Wilby and Wigley, 1997). 2.7.1
Dynamical downscaling
Dynamical downscaling involves the use of Regional Climate model (RCM), in which the output from the GCMs are used as the boundary conditions for the RCMs (Teutschbein et al., 2011). Compared to GCMs, RCM are of higher resolution varying from 50 km to 100 km. Dyamical downscaling when done with RCMs has several advantages compared to the statistical method of downscaling (Salathe et al., 2007). However, this method is sensitive to the initial condition and takes lots of computation time and capacity (Teutschbein et al., 2011).
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2.7.2
Statistical downscaling
Statistical downscaling can be efficiently used to downscale the climate variable from GCMs to local scale. Temperature and precipitation are the most important variables to be considered to study the impact of climate change on water resources (Räty et al., 2014). Compared to dynamical downscaling, statistical downscaling are computationally more efficient and large set of climate variables and scenarios can be considered (Salathe et al., 2007). There are two commonly used statistical downscaling approaches used for various water planning applications; i) Delta Method Downscaling and ii) Bias Correction and Statistical Downscaling (BCSD). 2.7.3
Delta method downscaling approaches:
Delta method of downscaling is a simple and generally utilized methodology for water resources planning studies (Hamlet et al., 2010). In delta method, correction is applied to the GCM simulated data based on the difference in temperature and precipitation of the GCM simulated data and observed data of local scale. Future Precipitation and future temperature can be estimated according to equation 2.1 and 2.2 (Hamlet et al., 2010). Future precipitation (Pnew) can be estimated as: Pnew = Pobs * Pratio
(2.1)
Where, Pratio is the ratio of the future simulated mean precipitation from GCM to the historic period Future Temperature (Tnew) can be estimated as: Tnew = Tobs + Tdiff
(2.2)
Where, Tdiff is the difference of the future simulated mean precipitation from GCM to the historic period. Advantages and Limitations of Delta Method Delta method of statistical downscaling has advantage of preserving the temporal and spatial distribution of climate variables and makes it easier to compare between observations and future scenarios (Hamlet et al., 2010). However, in delta method, the potential changes in the time series behavior of Temperature (T) and precipitation (P) cannot be obtained (Hamlet et al., 2010). Delta change method cannot be used to correct the extreme biases. The method take cares of the correction of the mean while correction of the variability is not taken care of (Graham et al., 2007).
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2.7.4
Bias correction
Biases can be defined as the relative error or difference present in the GCM simulated climate variable with respect to the observed dataset of the study region. Biases in the GCM exists as the GCM are coarse in nature and cannot represent the climate variable at local scale. These errors may occur due to several factors such as flawed model representation (Maraun, 2012); inappropriate adjustment of the model, incomplete observed data for model parameterization and validation (Ehret et al., 2012). Correction of the biases present in the GCMs should be corrected before it can be used for the analysis of climate change and impact studies in local scale. The different methods of bias correction are as follows: 2.7.5
Delta Change Approach (Lenderink et al., 2007). Power Transformation (Terink et al., 2010; Leander et al., 2008). Multiple Linear Regression (Schoof and Pryor, 2001; Wilby et al., 2002). Constructed Analogue (van deden Dool et al., 2003). Local Intensity Scaling (Wilby and Wigley, 1997). Quantile Mapping (Hamlet et al., 2010; Themeßl et. al., 2012). Simple method of Bias Correction
In this method, the biases, which are present in the GCM output, are corrected based on mean and standard deviation. This method is mostly applicable for the bias correction of GCM simulated temperature and precipitation data with respect to the historically observed data of the study area. The future corrected GCM time series can then be constructed as follows (Smith, pers. comm. 2009): Tcorrected = Tobs + (Tmod – Tmod)
𝜎𝑇𝑜𝑏𝑠
(2.3)
𝜎𝑇𝑚𝑜𝑑
For rainfall, Pcorrected = Pmod x
𝑃𝑜𝑏𝑠
(2.4)
𝑃𝑚𝑜𝑑
Where, bar and sigma indicate the average and standard deviation respectively.
19
2.7.6
Quantile mapping technique
In statistical bias correction method, the biases in the climate variables are removed after comparison between the observed and GCM data (Wood et al., 2002). In this method, the quantile value of the cumulative distribution function of the temperature or precipitation is matched with the consequent quantile value of the cumulative distribution function of GCM simulated variable (Hamlet et. al., 2010). Quantile mapping is used to reduce biases at high quantiles and hence the projection of precipitation and temperature at higher quantiles are well-maintained and reliable (Themeßl et. al., 2012).
Figure 2.8 Quantile mapping technique for bias correction (Hamlet et. al, 2010) 2.8
Hydrological Modeling
Hydrological modeling is a necessary tool for studying the impacts of climate change on water resources, which is essential in analyzing and projection of potential impacts due to change in future climate scenarios (Praskievicz and Chang, 2009). Hydrological modeling can be used to simulate the hydrological regime of a watershed with different condition of climate change scenarios such as change in precipitation and temperature. Hydrological models are used frequently by the researchers for different purposes such as water resources planning and management, flood analysis, reservoir simulation, water supply, climate change impact analysis and for other several purposes. Recently, hydrological model are used extensively to study the impact of climate change in water resources planning and management. For hydrological modeling, different mathematical models are used widely such as HEC-HMS, SWAT, HBV, Drainmod, TOPKAPI etc. The selection of the model depends upon different factors such as the data availability, topography, model accessibility etc. SWAT (Soil Water Assessment Tool) is a semi distributed model which is widely used for hydrological modeling all over the world.
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2.8.1
SWAT
SWAT has been widely used for hydrological modeling in different watershed all around the world. One of the recent studies includes application of a SWAT model for hydrological modeling in the Xixian basin in China. The study was carried out to study the impact of climate change and increasing population in water resources and agricultural areas. The study evaluated the performance of SWAT for hydrological modeling in the Xixian basin. The results showed satisfactory performance of SWAT model in the Xixian River basin. The study shows the base flow as the major constituent of the streamflow and hydrological water balance. Evapotranspiration contributed more than 60% of the annual precipitation in the basin (Shi et al., 2013). The study recommended further research and analysis of impact of climate change and land use change in the water balance and suggested investigating the effect of different management scenarios on the water resources of the basin (Shi et al., 2013). In a recent study conducted in South Korea, SWAT was used to estimate the soil moisture and evapotranspiration from the mixed forest. The streamflow data was used to calibrate the model and the calibrated model was used to evaluate the soil moisture and evapotranspiration in the hilly watershed of 8.54 km2 area (Park et al., 2013). The uncertainty due to parameters in the model can be greatly reduced by measuring the evapotranspiration and soil moisture in its hydrological state and provided more realistic interpretation from the model, which helps in better understanding of the water balance of the watershed (Park et al., 2013). Bharati et al. (2012) carried out the analysis of impact of climate change on hydrology and water balance of Koshi basin in Nepal using SWAT model. The climate projections were used from four GCMs. The result shows greater ratio of runoff to evapotranspiration at the upper watershed whereas the ratio was found to be lower at the lower part of the watershed (Bharati et al., 2012). 2.9
Flood Frequency Analysis
Arnell and Gosling (2014) carried out the impact of climate change on flood risk at global scale based on the change in the frequency of flood extremes using hydrological model and climate change scenarios. The study shows increase in the flood extreme events and frequency in most of the regions of the world. The result shows occurrence of 100-year return period in every 50 years during the period of 2060s across most of the part of the globe (Arnell and Gosling, 2014). The change in the magnitude and frequency of the flood of 100-year return period was used to study the impact of climate change on flood risk. Detrembleur et al. (2015) carried out the impact analysis of climate change on flood frequency and flood risk assessment for the Meuse river basin in Europe. The flood risk analysis was carried out by accessing the impact of climate change on flood frequency, estimation of inundated land and estimation of economic damage due to inundation. Delta change method was used for the projection of temperature and precipitation. The discharge data required for the calculation of the return period of the flood was derived using statistical analysis of the rating curve obtained from the gauging station of the Meuse river basin (Detrembleur et. al., 2015).
21
The study shows increase in the 100-year return period of flood during wet seasons by 15% during the period of 2030 to 2060 and by 30% during the period of 2070 to 2100 (Detrembleur et. al., 2015). Mishra and Hearath (2014) studied the impact of climate change on projection of flood frequency in the Bagmati basin of Nepal. Bias correction of the GCM precipitation from MRI-GCM was done for the analysis. The analyses showed occurrence of extreme precipitation during the monsoon seasons whereas drier period showed occurrence of less precipitation (Mishra and Herath, 2014). The results showed significant increase in future flood event up to 40% in the flood event of 2 to 100 year return period. Extreme flood event of a basin is best represented by the change in the return period of the flood for a basin (Abbs et al., 2007). 2.10
Risk Analysis
There are several studies regarding the risk analysis in a construction projects but only few studies has been carried out to study the impact of climate change on risk analysis in renewable energy project like hydropower plants. Risk analysis can be carried out in general approach or specific project based approach. Kucukali (2011) carried out the risk assessment of run of the river type of hydropower plants by using fuzzy logic approach. In this study, fuzzy rating tool was developed for run of the river type of hydropower plant to calculate the Risk Index based on expert judgments. The risk factor was calculated for one of the hydropower plant in Turkey and concluded the environmental factor as the most significant risk factor. The methodology proposed in this study is believed to assist the policy makers and investors to make rational decisions to prevent cost and schedule over runs (Kucukali, 2011). Mimikou and Baltas (1997) assessed the impact of climate change on the reliability and risk assessment of storage type of hydropower plant using climate change scenarios. In this study, risk analysis was carried out by calculating the percentage of failure of risk based on certain criteria of energy production and volume of storage. A failure was considered to occur when the monthly volume or the energy production is less than expected or minimum guaranteed energy production (Mimikou and Baltas, 1997). The results showed that risk associated with the annual production of energy is expected to increase under the influence of future climatic condition. The study showed that increase of reservoir storage volume was required to maintain the hydropower production in the future at the current risk level of the baseline period.
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CHAPTER 3 METHODOLOGY 3.1
Study Area
The study was carried out in the Tamakoshi Basin of Nepal. Tamakoshi basin is one of the sub-basin of Koshi basin. It extends from 27° 37’ 42” to 28° 19’ 23” in the North and from 86° 0’ 9” to 86° 34’ 12” in the East. The basin has total area of 2926 km2 of which about 51% (1498 km2) of this area is situated in the Tibet, China (Khadka et al., 2014). Tamakoshi basin extends from High Himalayan to Siwalik Range ranging from elevation of 849 m to 7315 m (Khadka et al., 2014). Annual rainfall in the basin is around 2200 mm (DHM, Nepal).
TIBET
NEPAL NEPAL
Figure 3.1 Location map of Tamakoshi basin showing hydrological and meteorological stations The project area and Tamakoshi river is located in the eastern region together with neighboring rivers: Bhotekoshi (to the west) and Dudhkoshi (to the east). Tamakoshi is a tributary of the Sunkoshi River, which joins Arun and Tamor rivers to form the final Sapta Koshi section. Beyond the Indian border, the Sapta Koshi becomes a major tributary of the Ganges River, which is joined by the Brahmaputra River from the north, originating also in Tibet, and flows through Bangladesh to discharge into the Bay of Bengal.
23
Like other Trans-Himalayan catchments, the upper region of the Tamakoshi catchment are partially protected from monsoon rainfall in summer months. The rainfall map prepared by DHM shows annual isohyets varying from north to south between 1000 mm and 2000 mm across the project area. Temperature data obtained from Jiri station, which is the nearest temperature station to project area, indicate summer (June-August) maxima of 25º C and winter (December to February) minima of -2º C. In the river headwaters and across the Tibetan plateau, a dry and cold climate prevails. Upper Tamakoshi Hydroelectric Project (456 MW) is the largest hydropower project of Nepal to come under construction. The project is located in Dolakha district, Janakpur zone of Central development region at latitude 27º 50’ to 28º 00’ N and longitude 86º 10’ to 86º 15’ E. The project lies near the lower region of the higher Himalayas. The intake for the hydropower plant is located at Lamabagar VDC, which lies at a distance of 6 km south from the border of China. Upper Tamakoshi Hydroelectric Project is a peaking run-off-river hydropower (PROR) with limited capacity in the intake pond for daily peaking operation. The design discharge of the plant is 66 m3/s and the maximum gross head of 822 m will be utilized to produce hydroelectricity from four Pelton turbines. 3.2
Data Collection
Hydrological and Meteorological Data The meteorological data such as precipitation, minimum air temperature and maximum air temperature of at least 30 years are required for the analysis of hydrological balance of the study area using SWAT. The data required was collected from Department of Hydrology and Meteorology (DHM), Nepal. DHM has the recorded data of two meteorological stations of Tamakoshi basin: Charikot and Jiri. The meteorological data from these stations were used for the analysis in SWAT. The following stations of the Tamakoshi basin were used for the analysis of hydrological and meteorological data available from Department of Hydrology and Meteorology (DHM). Table 3.1 Meteorological stations of Tamakoshi basin used in the study Station Name Charikot Jiri Tsho Rolpa
Type of Station
District Latitude
Longitude
Precipitation Agro meteorology Temperature
Dolkha 27̊ 40'
86̊ 03'
Elevation (m) 1940
Dolkha 27̊ 38'
86̊ 14'
2003
Dolkha 27̊ 50'
86̊ 28'
4580
24
Start Year 1959 1961 1999
3000
Rainfall (mm)
2500 2000 1500 1000 500
2011
2013 2013
2009
2007
2005
2003
2001
1999
1997
Monsoon
2011
Annual Rainfall
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
Non-Monsoon
Figure 3.2 Historical annual rainfall of Jiri station from 1977 to 2013. 3000
Rainfall (mm)
2500 2000 1500 1000 500
Annual Rainfall
Monsoon
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
Non-Monsoon
Figure 3.3 Historical annual rainfall of Charikot station from 1977 to 2013. The discharge data of Busti station of the basin from year 1971, which is under DHM, Nepal were used for the analysis. The observed data were used for calibration and validation of the SWAT model at the outlet of the basin. NEA established hydrological station at Lamabagar, near the intake site of the project to take discharge measurement for 6 years from 2001 to 2006. Table 3.2 Hydrological Stations of Tamakoshi basin used in the study River Tamakoshi
Site Name Busti Lamabagar
Latitude 27̊ 38' 05" 27̊ 53' 50"
25
Longitude 86̊ 05' 12" 86̊ 16' 0"
Elevation (m) 849 1965
Start Date 1/14/1970 1/1/2001
Digital Elevation Model DEM was used to get the elevation information of any point in a given area at a specific spatial resolution. For this study, ASTER DEM of 30m resolution was used which was accessed from http://gdem.ersdac.jspacesystems.or.jp. The DEM was used to delineate the watershed and to generate the river and drainage pattern of the terrain. Basin Parameters such as slope length, slope gradient can be derived from DEM. The administrative map of the country was downloaded from http://www.diva-gis.org.
% Area Below Elevation
100 80 60 40 20 0
0
2000
4000
6000
8000
Elevation (m)
Figure 3.4 DEM of Tamakoshi basin (left) and hypsometric elevation distribution of the study area (right). Soil Data Soil textural and physio-chemical properties of different layers of each soil type are necessary for input in SWAT model. The soil data was obtained from SOTER (Soil and Terrain Database Programme) for Nepal and China at scale 1:1 million (http://www.isric.org/projects/soil-and-terrain-database-soter-programme). SOTER Nepal and SOTER China are generalized from the original Soils and Terrain database at scale 1:50,000 compiled by FAO and Survey department of Nepal. Land Use Data Land use map of the study area was obtained from the European Space Agency (http://due.esrin.esa.int/globcover/). The land use map can be obtained for the year of 2004 and 2009 with the resolution of 300m.
26
Figure 3.5 Land use map (left) and Soil map (right) of Tamakoshi basin.
Forest 22%
Agricult ural 13% Barren 1%
Range-Grasses Barren Forest
RangeBrush 5%
Clayey Loam Loam 14% 3% Silty Loam 10%
RangeGrasses 25%
Glacier 11%
Loamy Sand 62%
Water 34%
Glacier Silty Loam Clayey Loam
Water Agricultural Range-Brush
Loamy Sand Loam
Figure 3.6 Land use re-classification (left) and Soil map re-classification (right) obtained from SWAT model of Tamakoshi basin
27
Salient features of Upper Tamakoshi Hydro Electric Project (456 MW) The salient feature of Upper Tamakoshi Hydroelectric project can be obtained from the official Upper Tamakoshi Hydropower Limited website: http://www.tamakoshihydro .org.np/. Nepal Electricity Authority (NEA) as an autonomous company established Upper Tamakoshi Hydropower Limited (UTHKPL) in 2007 for the implementation of the project. The data required for the energy calculation was obtained from the site office. The expected annual generation of energy from the project is 2281 GWh with the design discharge of 66 m3/s. The construction of the project in still on progress with 80% of the civil work already completed. The project is expected to complete by 2016. Table 3.3 Salient features of Upper Tamakoshi hydroelectric project Title Type of Development Location:
Description Peaking Run-of-River (PRoR) Lamabagar, Dolakha, Nepal
Headwork Location: Powerhouse Location Maximum Output Annual Energy Gross Head Design Discharge Catchment Area Min. Mean Monthly Flow Mean Annual Flow Design flood Q(1,000) Diversion Dam Live Storage Settling Basins Headrace Tunnel Power House (Underground) Number of units Tailrace Tunnel Access Road Transmission line Construction Time Period
Lamabagar, Dolakha Gongar Gaon, Lamabagar, Dolakha 456 MW 2,281 GWh 822 m 66.0 m 3 /sec 1,745 km 2 14.1 m 3/sec. 67.2 m3/sec. 885.0 m3/sec 22 m x 60.0 m (H x L) 1.2 Million m3 2 No, L=225 m 7.9 km 142.0m x 13.0m x 25.0 m (L x B x H) 6 nos 2.9 km 68.0 Km from Charikot 220 kVA Double Circuit 5 Years
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3.3
Methodological Overview
The data required for SWAT model are DEM, land use map, soil map and climate data such as temperature, precipitation, relative humidity, wind speed, and solar radiation. The SWAT model was calibrated and validated using the observed discharge data at the intake dam site and at the outlet of the basin. Bias correction of the three GCMs: MIROC-ESM, MRICGCM3 and MPI-ESM-M was done using quantile mapping method. The bias corrected GCM data was used as input for calibrated and validated SWAT model to generate the future projection of discharge at the intake dam site for the future period of 2030s, 2060s and 2090s. The discharge data obtained from the SWAT model was used to study the impact of climate change on hydropower production. Risk analysis was carried out for the future energy production from Upper Tamakoshi hydroelectric project.
GIS Data
DEM data Land use Map Soil Map
Climate Data Observed Temperature and Precipitation GCM Data MIROC-ESM MRI-CGCM3 MPI-ESM-M
Bias Correction
Hydrological Modeling Setup SWAT model
Risk Analysis Calibration
Future energy production
Validation
Impact Analysis Future Projected Discharge at Intake Dam site
Quantile Mapping Future projected climate data (2030s, 2060s, 2090s)
Figure 3.7 Methodological overview flowchart
29
Streamflow Hydropower generation
3.4
Selection of GCM
Selection of GCMs for this research were based on few criteria. They were Vintage, Resolution, Validity and Representativeness of the result. GCMs were selected based on their recent development, finer resolution, validity of GCM based on the statistical analysis between the observed data and GCM data. Based on these criteria, three GCMs were selected for the research purpose. Table 3.4 GCMs, their sources, resolutions, vintage and data sources GCMs MIROC-ESM MRI-CGCM3 MPI-ESM-M
3.5
Resolution 2.81º × 2.81º 3.75º × 3.75º 1.87º × 1.87º
Institute University of Tokyo MRI, Japan MPI, Germany
Vintage 2010 2011 2013
References Watanabe et al., 2011 Yukimoto et. al., 2012 Giorgetta et. al., 2013
Downscaling of GCM Data
Downscaling is the technique of getting finer resolution data from coarse and low-resolution GCM data. There are two methods of downscaling technique. They are dynamic downscaling and statistical downscaling. Dynamic downscaling is complex in nature and requires time, effort and cost (Teutschbein et al., 2011). Due to these limitations, statistical downscaling was used in the study. Bias correction method using empirical quantile mapping method was used to downscale the precipitation and temperature of GCM in this study. Quantile mapping technique is based on matching the two cumulative distribution functions (CDFs) of the GCMs simulated and observed data. In this method, the quantile value of the cumulative distribution function of the temperature or precipitation is matched with the consequent quantile value of the cumulative distribution function of GCM simulated variable (Hamlet et al., 2010). Qmap-package is a statistical transformations package for post-processing of climate model output. The Qmap package can be used for quantile mapping method. The statistical transformation is used to match the cumulative distribution of the modeled variable with the observed ones (Gudmundsson et al., 2012). The statistical transformation in Quantile mapping are represented by equation 3.1 and 3.2 (Gudmundsson et al., 2012). The statistical transformation can be represented as: PO = h (Pm)
(3.1)
The transformation is defined as: PO = FO-1(Fm (Pm))
(3.2)
Where, Fm is the CDF of Pm and FO-1 is the inverse CDF corresponding to PO.
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3.6
Hydrological Modeling
To obtain the daily discharge at the outlet and to predict the future runoff of the basin, SWAT was used based on observed discharge data. The application of the model involves sensitivity analysis, calibration and validation. The GIS input needed for the Arc-SWAT includes DEM, soil data, land use data and stream network layers. There are two methods to estimate the surface runoff: SCS curve number procedure i) USDA Soil conservation Service and ii) Green and Ampt Infiltration method. In this study SCS curve number method has been used to estimate the surface runoff. The surface runoff from SCS curve number is represented by following equation (USDA, 1969). Qsurf =
(𝑅𝑑𝑎𝑦 −0.2𝑆)2
(3.3)
(𝑅𝑑𝑎𝑦 +0.8𝑆)
Where, Qsurf is the accumulated runoff (mm) Rday is the rainfall depth for the day (mm) S is the retention parameter (mm) USDA (1969) gave the retention parameter: 100
S = 25.4 (
𝐶𝑁
) – 10
(3.4)
Where, CN is the SCS curve number, which is a function of the soil’s permeability, land use and antecedent moisture conditions. Snowmelt was included with rainfall in the calculations of runoff and percolation. The snowmelt in SWAT is a linear function of the difference between the average snow packmaximum air temperature and the base of threshold temperature for snow melt (Neitsch, 2009). It can be represented by following equation (Neitsch, 2009): 𝑇𝑠𝑛𝑜𝑤+𝑇𝑚𝑙𝑡
SNOmlt = bmlt – snocov. (
2
)- Tmlt
Where, SNOmlt is the amount of snow melt on a given day (mm) Bmlt is the melt factor for the day (mm/day ̊C) Tmlt is the base temperature above which snow melt is allowed (̊C)
31
(3.5)
SWAT 2012
DEM Setup Stream Definition Outlet selection
Watershed Delineation
Hydrological Unit (HRU)
Input Data DEM (ASTER GDEM) Stream Network
Land Use Map Soil Map Slope Map
Response
Write Input Table
Weather Generator Rainfall Temperature Relative Humidity Wind speed Solar radiation
Run SWAT
Calibration and Validation
Validated SWAT
Hydrological Data Busti (1998-2008) Lamabagar(20012006)
Future Climate Data Precipitation Temperature
Future Discharge
Figure 3.8 Methodological framework for hydrological modeling in SWAT
32
3.7
Evaluation of Performance of SWAT Model
Moriasi et al. (2007) recommended three quantitative statistics: Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR), for model evauluation. The performance analysis of the model in this study was hence based on Percent bias, Nash-Sutcliffe Efficiency (NSE), ratio of the root mean square error to the standard deviation of measured data (RSR) with addition to correlation coefficient (R2). Coefficient of Determination (R2) It represents the strength of relationship between observed and simulated values. R2 =
𝑛 ∑ 𝑥𝑦−∑ 𝑥 ∑ 𝑦 (√𝑛(∑ 𝑥 2 )−(∑ 𝑥)2 )×(√𝑛(∑ 𝑦 2 )−(∑ 𝑦)2 )
(3.6)
Where, R2 = Correlation coefficient x = Observed value y = Simulated value R2 ranges from 0 to 1 with higher value representing better compliance between observed and simulated values. Percent Bias (PBIAS) Percent bias measures whether the simulated data is larger or smaller than that of observed data (Gupta et al., 1999). Percent bias can be positive or negative depending on the underestimation or overestimation respectively. PBIAS =
∑𝑛 𝑖 (𝑌𝑖𝑜𝑏𝑠−𝑌𝑖𝑠𝑖𝑚)∗100
(3.7)
∑𝑛 𝑖 𝑌𝑖𝑜𝑏𝑠
Where, Yiobs = Observed data Yisim = Model simulated data Nash-Sutcliffe Simulation Efficiency (NSE) It is widely used and highly reliable method to evaluate the performance analysis of hydrological model. ′ 2 ∑𝑛 𝑖=1(𝑄𝑖 −𝑄𝑖 ) NSE = 1 - 𝑛 ∑ (𝑄𝑖 −𝑄̅𝑖 )2
(3.8)
𝑖=1
Where, 𝑄𝑖 = measured daily discharge 𝑄𝑖′ = simulated daily discharge 𝑄̅𝑖 = average daily discharge for simulation period 𝑛 = number of daily discharge value NS value of 1 indicates the perfect match of simulated discharge with the observed data. It can range from -∞ to 1. 33
RMSE-observations standard deviation ratio (RSR) RSR is calculated as the ratio of the RMSE and standard deviation of the measured data (Moriasi et al., 2007).
RSR =
𝑅𝑀𝑆𝐸 𝑆𝑇𝐷𝐸𝑉 𝑜𝑏𝑠
=
2 √∑𝑛 𝑖=1(𝑌𝑖𝑜𝑏𝑠−𝑌𝑖𝑠𝑖𝑚) 2 √∑𝑛 𝑖=1(𝑌𝑖𝑜𝑏𝑠−𝑌𝑚𝑒𝑎𝑛)
(3.9)
The value of RSR ranges from 0 to large positive values. Lower RSR values indicates better performance of the model whereas large value indicates poor performance. 3.8
Assessment of Climate Change Impact on Upper Tamakoshi Hydro Electricity Production
Upper Tamakoshi hydroelectric project is run of the river type of hydropower plant. The energy production during the dry and wet period depends upon the availability of water in the river. To analyze the impact of climate change in the energy production from the hydropower plant, SWAT model was used to generate the discharge for the historical period from 1975 to 2004. The discharge obtained from the SWAT model was used as a baseline to analyze the impact of future change in precipitation and temperature obtained after bias correction of GCM data. The impact of climate change in the power generation was analyzed in three time windows: 2030s (2015 – 2039), 2060s (2040 – 2069) and 2090s (2070 – 2099). Hydropower can be calculated using following formula: Electrical Power (W) = ɳ×ɤ×Q×H
(3.10)
Where, ɳ is the dimensionless efficiency of turbine ɤ is the specific weight of water Q is the flow in cubic meters per second Net Head (H) = Hg – lm (
𝑄 2 ) 𝑄𝑚
Where, Hg= Gross head (m) lm = length of the waterways Q and Qm are actual water flow and design water flow respectively Energy production can be calculated using following formula: Annual Energy (KWh) = Power (KW) × hours in a day ×days in a year
34
(3.11)
3.9
Flood Frequency Analysis
Flood frequency analysis at the intake dam site was carried out using Gumbel’s extremevalue distribution. The impact of climate change in the magnitude of the flood was studied for the future period from the discharge obtained from the SWAT model for the future climatic condition. Gumbel’s method is one of the most widely used probabilistic distribution method to predict the peak or maximum flood in hydrological and meteorological studies. According to Gumbel’s theory of extreme events, the probability of occurrence of an event equal to or larger than a value x0 is given by: P(X ≥ x0) = 1- e-e-y
(3.12)
Where, X is an event variable y is a dimensionless variable given by y = α (x-a) a = x – 0.45005σx α = 1.2825/ σx Thus, y=
1.2825 (𝑥−𝜇) σx
+ 0.577
(3.13)
Where, μ = mean and σx = Standard deviation of the variate X From equation (3.12) yP = -ln ( -ln (1-P)) or, yT = - [ln.ln
𝑇 𝑇−1
]
yT = - [0.834 + 2.303 log log
𝑇 𝑇−1
]
(3.14)
Where, T = 1/P = return period yT = the value of y, commonly called reduced variate for a given T Rearranging equation (3.13), the value of the variate X with a return period T is given by: xT = μ + K σx
(3.15)
Where, K=
(𝑦𝑇−0.577) 1.2825
35
3.10
Risk Analysis
The sensitivity of the operation of hydropower reservoir under due to changing climate condition is based on a risk analysis of the annual energy production (Mimikou and Baltas, 1997). The risk analysis of the run of the river hydropower plant for this study was carried out similar with the process used for reservoir type of hydropower plant. The risk analysis was carried out by varying the annual energy production (E) within a specified range. The annual energy was chosen to vary between the minimum guaranteed energy production of 824.9 GWh obtained from the minimum discharge of 13.6 m3/s to 1961 GWh which is obtained at the mean average annual discharge 60 m3/s. The risk analysis was carried out for the future climatic projections of three GCMs described earlier: MIROC-ESM, MRICGCM3 and MPI-ESM-M with RCP 4.5 and RPC 8.5 scenarios. The risk analysis was carried out for three timeline period of 2030s (2015-2039), 2060s (2040-2069) and 2090s (2070-2099). A failure or risk value were calculated for each scenarios, equal to the relative percentage of frequency of monthly failure within the series. A risk was considered to occur when the monthly energy E does not the follow the constraints in the equation 3.16. The risk associated with the given E was calculated for each GCMs and different timeline period for RCP 4.5 and RCP 8.5 scenarios. Risk is assumed to occur when the energy Et produced during the month t is greater than the guaranteed or designed value (Mimikou and Baltas, 1997). btE ≤ Et
(3.16)
Where, E = annual energy production (GWh) Et = energy produced during month t (GWh) bt = monthly distribution coefficient of observed/designed annual energy production = ∑12 𝑖=1 𝑏𝑡 = 1 The monthly distribution coefficient were calculated for each month based on the mean value of energy production during the design period. Table 3.5 shows the mean values of the monthly distribution coefficient (bt) based on the designed period of energy production. The value of at is higher for the month of June, July, August and September as maximum amount of energy is produced during the rainy season. The coefficient can be found low for dry season during the month of November up to the month of May, when the production of energy is low.
36
Table 3.5 The mean values of the monthly distribution coefficients (bt) based on historical simulated energy production Month January February March April May June July August September October November December
Monthly Distribution coefficients (bt) 0.037 0.027 0.024 0.019 0.063 0.14 0.148 0.148 0.14 0.131 0.064 0.049
37
CHAPTER 4 RESULTS AND DISCUSSION 4.1
Trend Analysis and Extreme Climate Indices
The temperature data and precipitation data of the study area from 1975 to 2013 was used to calculate the extreme climate indices of the historical period. RClimDex (1.0) was used to compute the temperature indices of Jiri station and precipitation indices of Charikot and Jiri station. RClimDex provides user to compute all 27 core indices recommended by the CLIVAR (Climate Variability and Predictability) Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI). Table 4.1 Temperature indices for Jiri station based on historical data from 1975 to 2013. Indices
ID Name
Units Slope
Standard Error P-Value
SU25
Summer days
Days
1.093
0.187
0.000
FD0
Frost days
Days
0.723
0.199
0.001
TXx
Hottest day
ºC
0.052
0.013
0.000
TXn
Coldest day
ºC
0.054
0.037
0.160
TNx
Hottest night
ºC
-0.006
0.008
0.443
TNn
Coldest night
ºC
-0.076
0.021
0.002
TX10p
Cool days
Days
-0.598
0.089
0.000
TX90p
Warm nights
Days
0.493
0.087
0.000
TN10p
Cool nights
Days
0.143
0.105
0.184
TN90p
Warm nights
Days
-0.103
0.053
0.059
From the 27 core indices, 10 temperature indices and 10 precipitation indices were used to study the extreme climate condition of the historic climate variables as shown in Table 5.1 and Table 5.2. The extreme climate indices calculation shows different trend of temperature indices and precipitation indices of Charikot and Jiri station. The maximum 1-day precipitation (Rx1day), maximum 5-day precipitation (Rx5day) and number of heavy precipitation days (R10) are in decreasing trend for Charikot station whereas the same indices are in increasing trend for Jiri station. R20 and R25 were found to be increasing for both the stations. Consecutive dry days are in increasing trend with the value of 0.722 days/year and 0.55 days/year for Charikot and Jiri station respectively. However, consecutive wet days is in decreasing trend for Charikot station with value of 0.311 days/year. Very wet days (R95) are in increasing trend. Annual total wet day precipitation (PRCPTOT) is decreasing slightly for Charikot station whereas it was found to be increasing significantly by 14.56 mm per year for Jiri station. The standard error was calculated as the ratio of standard deviation to the square root value of sample size.
38
Table 4.2 Precipitation indices for Charikot and Jiri station based on historical data from 1975 to 2013. Indices
ID Name
Rx1day
Max 1-day precipitation Max 5-day precipitation No. of heavy precipitation days
Rx5day R10
Charikot Station Slope Standard PError Value
Jiri Station Slope Standard PError Value
mm
-0.91
0.792
0.256
0.37
0.195
0.065
mm
-0.34
0.912
0.71
0.93
0.613
0.138
Days
-0.24
0.178
0.187
0.32
0.148
0.039
Units
R20
No. of days for very heavy precipitation
Days
0.01
0.121
0.998
0.34
0.096
0.001
R25
No of days above 25mm Consecutive dry days Consecutive wet days Very wet days
Days
0.05
0.098
0.55
0.36
0.078
0.000
Days
0.72
0.386
0.07
0.55
0.341
0.113
Days
-0.31
0.168
0.073
0.39
0.252
0.126
mm
3.94
3.232
0.231
4.93
2.328
0.042
Extremely wet days Annual total wet day precipitation
mm
-1.12
2.242
0.619
2.69
1.795
0.143
mm
-0.01
5.27
0.998
14.56
4.034
0.001
CDD CWD R95p R99p PRCPT OT
Temperature indices were calculated for Jiri station only as it the only temperature station of the study area. The temperature indices such as SU25 (summer days), FD0 (frost days), TXx (hottest day) and TXn (coldest day) were found to be increasing. There is a slight decrease in the hottest night (TNx) and coldest night (TNn) temperature indices. Cool days (TX10p) and Warm nights (TN90p) are in decreasing trend with the value of 0.143 days/year and 0.103 days/year respectively. Warm nights (TX90p) and cool nights (TN10p) are in increasing trend with the value of 0.493 days/year and 0.143 days/year respectively.
39
4.2
Bias Correction
Bias Correction of precipitation and temperature of the historical period was done before predicting the future rainfall and temperature estimates from the selected GCMs. The historical rainfall and temperature data from MRI-CGCM3, MIROC-ESM and MPI-ESMM were bias corrected for the observed stations at Charikot and Jiri. The rainfall and temperature data for the period of 30 years from 1975 to 2004 were used for bias correction using the method of empirical quantile mapping method which is also known as empirical distribution bias correction method (Hamlet et al., 2010 ; Wood et al., 2004). 4.3
Bias Correction of Precipitation and Temperature
Bias correction of temperature and precipitation is necessary before it can be used to analyze the future climatic conditions. The performance of bias correction method was evaluated using statistical parameters like correlation coefficient (R2), Root mean square error (RMSE) and Standard deviation (SD). Bias correction of GCM simulated rainfall data shows significant improvement in correlation and root mean square error (RMSE). The correlation coefficient of precipitation varies from 0.63 to 0.72 after bias correction. MRICGCM3 shows least improvement after bias correction with correlation coefficient of 0.63 for Jiri station and high RMSE value whereas MIROC-ESM performed better with least RMSE and high correlation coefficient of 0.72 after bias correction. According to the evaluation statistics, MIROC-ESM showed better results than the other GCMs for the study area. Bias correction of GCM simulated rainfall data shows correction in the mean as well as improvement in the standard deviation. The mean precipitation before and after correction for all three GCMs are nearly equal for each month which shows the validation of this method. Table 4.3 Evaluation of bias correction for precipitaiton of MIROC-ESM, MRI-CGCM3 and MPI-ESM-M GCMs for Charikot Station. Charikot Station Precipitation SD R2 RMSE
MIROC-ESM MRI-CGCM3 MPI-ESM-M Raw Corrected Raw Corrected Raw Corrected 206.78 111.57 217.4 119.69 226.67 201.9 219.5 0.43 0.70 0.43 0.66 0.58 0.67 Obs
170.09
122.12
186.83
135.04
147.3
135.13
The simulated temperature from the GCM has a much better resemblence to the observed temperature than in the case of precipitation. However, the correlation of maximum temperature of MPI-ESM-M was found to be only 0.55 before bias correction and 0.69 after bias correction. Minimum temperature for all stations were found to have correlation coefficient greater than 0.9 for all GCMs. The correlation coefficient of maximum temperature after bias correction varies from 0.69 to 0.85. All the three GCMs show significant biases with respect to the observed precipitation and temperature before bias correction. Wet seasons are under projected by the GCMs whereas some dry seasons are overprojected. Both the wet season biases and dry season biases of precipitation are corrected after bias correction of the GCM data which can be analyzed from Figure 4.1
40
Table 4.4 Evaluation of bias correction for precipitation, minimum temperature and maximum temperature of MIROC-ESM, MRI-CGCM3 and MPI-ESM-M GCMs for Jiri Station. Obs
Jiri Station Precipitation SD R2 RMSE
227.73 -
MIROC-ESM MRI-CGCM3 MPI-ESM-M Raw Corrected Raw Corrected Raw Corrected 111.57 235.63 119.69 248.89 201.9 240.5 0.44 0.73 0.38 0.63 0.62 0.7 194.54 125.41 220.76 154.58 159.5 136.15
Min Temp.
SD R2 RMSE
6.76 -
6.44 0.87 3.72
7.37 0.90 2.01
6.18 0.88 2.80
6.84 0.92 1.89
7.19 0.86 9.02
6.81 0.93 2.89
Max Temp.
SD R2
4.17 -
5.09 0.69 3.81
3.92 0.85 1.87
7.44 0.69 4.64
3.78 0.72 2.32
7.59 0.55 10.44
3.97 0.69 2.42
RMSE
Mean Daily Prcp.(mm/day)
25
25
20
20
MIROC-ESM
MRI-CGCM3
15
15
10
10
5
5
0
0 J
F M A M J
J A S O N D
J
F M A M J
J
A S O N D
Mean Daily Prcp.(mm/day)
25 20
MPI-ESM-M 15 10 5 0 J F M A M J J A S O N D Observed GCM Raw Corrected
41
Figure 4.1 Comparison of monthly mean precipitation of Jiri Station before and after bias correction of GCMs.
In case of temperature, MIROC-ESM and MPI-ESM-M overpredicts both the maximum and minimum temperature throughout the year. Maximum temperature during the spring and summer seasons are overprojected by MRI-CGCM3 whereas minimum temperature are higher compared to observed minimum temperature for all seasons. After bias correction, the simulated temperature and observed temperature has a close resemblance. Figure 4.2 shows the correction of the mean temperature after bias correcton of the GCM data. The bias corrected minimum temperature and maximum temperature shows a very good correlation with R2 varying from 0.71 to 0.94. The mean minimum temperature and the mean maximum temperature for each month seems to be corrected and same applies with the standard deviation and RMSE error as well. The RMSE error varies from 1.89 to 2.89 for all the GCMs after bias correction of minimum and maximum raw temperature data which has the RMSE error ranging from 2.01 to 10.44. The raw temperature data of MPI-ESM-M has maximum RMSE error of 9.02 and 10.44 for minimum and maximum temperature respectively which is improved to 2.42 and 2.89 after bias correction. The performance of MIROC-ESM is relatively better compared to other two GCMs with RMSE value of 2.01 and 1.87 for minimum and maximum temperature respectively after bias correction.
Temperautre ( ͦC)
35
35
MIROC-ESM
25
25
15
15
5
5
-5
-5 J
F M A M J
J A S O N D
J
F M A M J
J A S O N D
MPI-ESM-M
35
Temperature ( ͦC)
MRI-CGCM3
25 15 5 -5 J
F
M A M
Obs Min Corrected Min GCM Raw Max
J
J
A
S
O
N
D
GCM Raw Min Obs Max Corrected Max
42
Figure 4.2 Comparison of monthly mean maximum Temperature and monthly mean minimum Temperautre of Jiri Station before and after bias correction of GCMs.
4.4
Future Projection of Precipitation and Temperature
For the future projection of precipitation and temperature, the GCM data from the period of 2010 to 2099 were used after bias correction. The future time period was divided into 3 timelines of 30 years interval: 2030s (2015-2039) , 2060s (2040-2069) and 2090s (20702099). MIROC-ESM and MRI-CGCM3 shows increase in the annual precipitation for both RCP 4.5 and RCP 8.5 scenario except for RCP 4.5 scenario of MIROC-ESM GCM which predict decrease in precipitation by 0.7% and 2.1% during 2030s for Charikot station and Jiri station respectively. However, the pattern of precipitation changes seasonally and are different for different GCMs. The annual average rainfall of charikot station is around 2100 mm while Jiri station receives more precipitation with annual rainfall more than 2300 mm annually. The projections shows increase in precipitation by MIROC-ESM and MRI-CGCM3 whereas MPI-ESM-M shows decrease in future precipitation. The range of change in annual precipitation for all the three GCMs and two RCP scenarios varies from -9.9% to +24.8%.
Prcp. Anomaly Jiri (mm)
2500
1500
2500
MRI-CGCM3
MIROC-ESM
1500
500
500
-500
-500
-1500 2015
2035
2055
2075
2095
-1500 2015
2035
2055
2075
2095
Prcp. Anomaly Jiri (mm)
2500 1500 MPI-ESM-M 500 -500
-1500 2015
Figure 4.3 Future projected annual precipitation anomaly of Jiri station with respect to baseline period (19752004) for different GCMs.
2065
RCP 4.5
RCP 8.5
For Charikot station, MRI-CGCM3 and MIROC-ESM expect increase in annual average precipitation except for RCP 4.5 scenario of MIROC-ESM GCM, which expect decrease in precipitation by 0.7% during 2030s. MPI-ESM-M projects increase in average annual precipitation only during 2030s and 2060s under RCP 4.5 scenario. RCP 8.5 scenario shows decrease in precipitation in all three future period.
43
2500
Prcp Anomaly Charikot (mm)
2500 1500
1500
500
500
-500
-500
-1500 2015 2500 Prcp. Anomaly Charikot (mm)
MRI-CGCM3
MIROC-ESM
2035
2055
2075
-1500 2015
2095
2035
2055
2075
2095
MPI-ESM-M
1500 500
Figure 4.4 Future projected annual precipitation anomaly of Charikot station with respect to baseline period (1975-2004) for different GCMs.
-500
-1500 2015
2035 2055 RCP 4.5
2075 2095 RCP 8.5
60
% Change in Prcp.
60
MIROC-ESM
40
40
20
20
0
0
-20
-20
-40
-40
-60
MRI-CGCM3
-60 2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
% Change in Prcp.
60 40
MPI-ESM-M
20 0 -20
-40 -60 2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
D/J/F
M/A/M
J/J/A
S/O/N
44
Figure 4.5 Percentage change in future projected precipitation at Charikot station with respect to baseline period (1975-2004) for different GCMs.
There is no definite trend in the change in precipitation seasonally and it is very difficult to interpret the changing pattern. The monsoon precipitation (months of June, July and August) are expected to be more intense with projection ranging from increase in precipitation by 3.4% (MIROC-ESM RCP 4.5 scenario) up to 20.7% (MRI-CGCM3 RCP 8.5 scenario). Monsoon rainfall are projected to decrease in case of MPI-ESM-M. The bias corrected future MIROC-ESM and MPI-ESM-M precipitation shows decreasing projection during dry seasons as well. MRI-CGCM3 projection shows increase in precipitation for all seasons with the average annual increase of precipitation by 10.1% and 14.5% for RCP 4.5 and RCP 8.5 scenarios respectively. 60
% Change in Prcp.
60
40
MIROC-ESM
40
20
20
0
0
-20
-20
-40
-40
-60 2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
MRI-CGCM3
-60 2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
% Change in Prcp.
60 40
MPI-ESM-M
20 0 -20 -40 -60 2030s 2060s 2090s 2030s 2060s 2090s RCP4.5 RCP4.5 RCP4.5 RCP8.5 RCP8.5 RCP8.5
D/J/F
M/A/M
J/J/A
Figure 4.6 Percentage change in future projected precipitation at Jiri station with respect to baseline period (19752004) for different GCMs.
S/O/N
For Jiri station, the projection shows increase in annual precipitation by MIROC-ESM and MRI-CGCM3, whereas MPI-ESM-M shows decrease in annual average precipitation except for slight increase of 0.9% during 2090s for RCP 4.5 scenario. The projection of precipitation from MRI-CGCM3 shows increase in annual average rainfall for all seasons except for spring season with the decrease of 12.9% and 11.8% for RCP 4.5 and RCP 8.5 scenarios respectively. Similarly, we can also observe increase in wet season precipitation and decrease in some dry season precipitation from MIROC-ESM and MRI-CGCM3. Both wet seasons and dry seasons are expected to decrease under MPI-ESM-M projection. Maximum decrease in precipitation is expected to occur during the spring of 2030s by 19.7% under RCP 4.5 scenario whereas maximum increase is expected to occur during the autumn of 2090s by 41.7% under RCP 8.5 scenario.
45
1200
Prcp. Change (mm)
1200
800
2030s RCP 4.5
800 400
400
0
0
-400
-400
-800
-800
-1200
-1200 0
2
4
6
8
10
12
1200
1200
Prcp. Change (mm)
2030s RCP 8.5
800
800
2060s RCP 4.5
400
400
0
0
-400
-400
-800
-800
-1200
2060s RCP 8.5
-1200 0
2
4
6
8
10
12
1200 1200
Prcp. Change (mm)
800
2090s RCP 4.5
800
400
2090s RCP 8.5
400
0
0
-400
-400
-800
-800 -1200
-1200 0 D/J/F
MPI-ESM-M
M/A/M 5
J/J/A
MRI-CGCM3
D/J/F
S/O/N 10
MPI-ESM-M
MIROC-ESM
M/A/M
J/J/A
MRI-CGCM3
S/O/N
MIROC-ESM
Figure 4.7 Range of change in seasonal mean precipitation for different scenarios and GCMs for Charikot station. The lower end of the bar represents the 5th percentile, the upper end represents the 95th percentile, and the marker represents the 50th percentile intervals of the uncertainty range.
46
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Figure 4.8 Range of change in seasonal mean precipitation for different scenarios and GCMs for Jiri station. The lower end of the bar represents the 5th percentile, the upper end represents the 95th percentile, and the marker represents the 50th percentile intervals of the uncertainty range.
47
It can be observed from Figure 4.3 and Figure 4.4 that the changes in the precipitation for the future period are not uniform and varies without any pattern and consistency for different seasons. The precipitation projections are very complex to interpret than temperature projections because the GCMs often do not agree on whether precipitation will increase or decrease at specific locations, and agree much less on the magnitude of that change (Girvetz et al., 2009). The range of uncertainty for the future projected precipitation can be analyzed from Figure 4.7 and Figure 4.8. The lower and upper ends of the bar show the 5th percentile and the 95th percentile intervals of the uncertainty range respectively while the dot represents the 50th percentile value or the median value of change in precipitation. December, January and February (winter season) are the driest period receiving least amount of rainfall and hence the uncertainty range is expected to be less during this period. June, July and August (Monsoon season) are the wettest period of the year receiving maximum amount of rainfall. The uncertainty range is projected to be higher during this period for both Jiri station and Charikot station. Higher range of uncertainty can be judged by the large difference in the 5th percentile value and 95th percentile value of the precipitation change. The variability in the uncertainty range during the other seasons are different for different GCMs and different period. The median value of change in precipitation is projected to be greater than zero or close to zero in most of the cases for MIROC-ESM and MRI-CGCM3. However, the median values for MPI-ESM-M for most of the seasons are negative due to decrease in annual average precipitation. Winter season shows least uncertainty in the change in precipitation as it receives only around 3% to 5% of the total rainfall. The future projected maximum temperature also shows uncertainty for different seasons, which can be analyzed from Figure 4.9. The future projected temperature mostly shows rise in temperature throughout all seasons. The range of uncertainty of the change in maximum temperature are different for different seasons with winter and spring season showing greater range of uncertainty. The positive median value for all period and scenarios indicate rise in maximum temperature as projected by all three GCMs. MIROC-ESM shows highest median value greater than 5º C during winter and spring season. The 95th percentile values are also found to be highest with 7.4 ºC and 7.23 ºC compared to other GCMs during these seasons. The median value for other GCMs varies from 0.5 ºC to 4.5 ºC for different seasons of the year.
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Figure 4.9 Range of change in seasonal mean maximum temperature for different scenarios and GCMs for Jiri station. The lower end of the bar represents the 5th percentile, the upper end represents the 95th percentile, and the marker represents the 50th percentile intervals of the uncertainty range.
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Figure 4.10 Maximum average annual Temperature Anomaly of Jiri station with respect to baseline period (1975-2004) for different GCMs.
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Minimum Temperature as well as maximum temperature are subjected to increase in the future as projected by all the three GCMs. Temperature at Jiri station is expected to rise by 0.22 ºC to 1.58 ºC during 2030s. 2060s is expected to see the increase in temperature by 0.83 ºC to 3.6 ºC whereas maximum rise in temperature is projected to increase during 2090s by 1.39 ºC to 6.33 ºC. Increase in minimum temperature is expected much higher than the increase in maximum temperature with maximum temperature expected to increase at most by 3.48 ºC during 2090s whereas minimum temperature is projected to increase by 6.33 ºC during 2090s.The maximum rise in both minimum and maximum temperature are projected by MIROC-ESM.
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Figure 4.11 Future projected change in maximum temperature and minimum temperature with respect to baseline period (1975-2004) of Jiri station for different GCMs.
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4.5
Hydrological Modeling
4.5.1 SWAT Model Setup ArcSWAT 2012 was used to simulate the hydrological process under present and future climatic condition. ASTER DEM of 30m×30m resolution was used for the delineation of the watershed in the model. The DEM was used after the projection of coordinate to UTM zone 45N. A threshold area of 2700 sq. km was defined to create the river networks. Defining of large threshold area leads to delineation of larger sub-watersheds whereas smaller threshold area leads to creation of too many sub-watersheds and finer streams. Manual outlets were generated automatically at the streams intersection by SWAT model based on the threshold area defined. To delineate the watershed, outlet was defined at Busti station of Tamakoshi river which resulted in the creation of 66 number of sub basins. HRU (Hydrological Response unit) is the smallest unit of the basin. Each HRU unit is the combination of unique land feature, soil type and slope classification. For creation of the HRU (Hydrological response unit), land use map, soil map and slope classes were used as input in SWAT. Lookup tables were used to reclassify the land use map and soil map based on the SWAT database. Slope was classified into four classes (0%-25%, 25%-50%, 50%70% and >70%). However, five classes of slope can be defined in the SWAT model. Classification of slopes into more classes is viable for the mountainous watershed. In order to create less number of HRU units, a threshold of 10% for land use, soil type and slope was used. The input resulted in the creation of 828 numbers of HRU units. Each HRU unit is based on unique combination of land use, soil type and slope class. To model the process of snowmelt and orographic distribution of temperature and precipitation in SWAT, temperature lapse rate and precipitation lapse rate were introduced. Snowmelt model like SWAT handles the spatial and temporal variations due to elevation by incorporating elevation bands or zones allowing the model to discretize the snowmelt process based on basin topographic controls (Hartman et al., 1999). Within the sub basin input files, the average elevation of each elevation band was entered, followed by the percentage of the sub basin area within that band. Each sub-basin were divided into five elevation bands. The sub-basin with less orographic difference were assigned only one elevation band. However, up to 10 elevation band can be assigned to each sub-basin in SWAT model. 4.5.2 Calibration and Validation Calibration and Validation was attained at the two outlets of the basin in SWAT model. Initially, the calibration and validation was done at the intake of the dam site. Six years of discharge data from 2001 to 2006 at the inlet of the dam is available which is under the NEA network. 4 years of discharge data from 2001 to 2004 was used for calibration and two years from 2005 to 2006 was used for validation. In the next step, discharge data of the outlet of the basin from 2000 to 2008 was used for calibration and validation. The discharge data from 2004 to 2008 was used for calibration of the model, whereas the discharge data from 2000 to 2001 was used for validation. The discharge data of 2002 and 2003 were not used due to incomplete data. The warm up period of 3 years from 2001 to 2003 was used of calibration whereas warm up period of 2 years from 1998 to 1999 was used for validation of the model. Sufficient warm up period is necessary to establish appropriate initial conditions for groundwater and soil water storage (Fontaine et al., 2002). 51
Table 4.5 Calibrated parameters, sensitivity analysis and their default values for SWAT model simulations. Input Parameters
Description
TLAPS CH_K1
Temperature Lapse Rate (ºC/km) Effective hydraulic conductivity in tributary channel (mm/hr) SFTMP Snowfall temperature (ºC) CN2 Initial runoff SCS curve number PLAPS Precipitation Lapse Rate (mm/Km) SOL_K Saturated hydraulic conductivity (mm/hr) SMFMX Maximum melt rate for snow during the year (mm/ºC-day) CH_K2 Effective hydraulic conductivity in main channel (mm/hr) Alpha_BF Baseflow alpha factor (days) SMTMP Snowmelt temperature (ºC) SOL_AWC Available water capacity of the soil (mm/mm) GW_Delay Groundwater Delay (days) CH_N2 Manning n value for main channel SMFMN Minimum melt rate for snow during the year (mm/ºC-day)
Rank
Best Parameter
SWAT Default
1 2
-5.5 79
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0.2 60-85 -150,+150 190 9
1 38-85 0 43.14 4.5
8
267
0
9 10 11
0.36 2 0.68
0.048 0 0.08
13 14 15
337 0.288 7
31 0.07 4.5
Fourteen model parameters were modified for the calibration of SWAT model. Curve number parameter is most sensitive and its value depends upon the type of land use, which affects the runoff directly. Routing parameters such as manning’s n value (CH_N2) and hydraulic conductivity (CH_K2) were also found sensitive and were modified to match the observed data. Temperature laps rate and precipitation lapse rate plays a significant role in adjusting the orographic distribution of temperature and precipitation of the basin. Temperature lapse rate was adjusted to the value of -5.5 ºC/km from its default value of zero (Khadka et al., 2014). The default setting of SWAT does not take account of the lapse rate of precipitation and temperature. The calibrated model seems to under predict some peak values during the monsoon period. The base flow is very well projected for both calibration and validation period. Baseflow alpha factor (Alpha_BF) is an important parameter to adjust the base flow. SOL_AWC (available water capacity of the soil) and SOL_K (saturated hydraulic conductivity) are the two sensitive soil parameters, which were calibrated for the model. Auto calibration along with manual adjustments was preferred for calibration in SWAT because of the involvement of large set of parameters including the snowmelt parameters.
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Figure 4.12 Daily observed and simulated streamflow at the outlet of the watershed after calibration for 2004-2008.
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Figure 4.13 Daily observed and simulated streamflow at the outlet of the watershed after validation for 2000-2001.
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Figure 4.15 Daily observed and simulated streamflow at the intake dam site after validation for 2005-2006.
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Figure 4.14 Daily observed and simulated streamflow at the intake dam site after calibration for 2001-2004.
Table 4.6 Model performance of daily streamflow during calibration period (2004-2008) and validation period (2000-2001) at the outlet of Busti station. Period Daily Streamflow
Statistics NSE R2 PBIAS RSR
Calibration Validation 0.76 0.84 0.76 0.85 -1.69% 5.24% 0.53 0.28
Table 4.7 Model performance of daily streamflow during calibration period (2004-2008) and validation period (2000-2001) at the outlet of intake dam site at Lamabagar station. Period Daily Streamflow
Statistics NSE R2 PBIAS RSR
Calibration Validation 0.71 0.78 0.8 0.82 -7.73% 14.25% 0.54 0.33
The Simulated discharge from the model shows a good result with the observed data and the performance of the model was evaluated using four statistical parameters: Nash Sutcliffe Coefficient, correlation coefficient (R2), RMSE observations standard deviation ratio (RSR) and percent Bias. Nash Sutcliffe coefficient is one of the statistical parameters, which is frequently used to evaluate the performance of hydrological model. NSE indicates the variance between the observe data and simulated data from the hydrological model (Nash and Sutcliffe, 1970). NSE shows the fitting of the observed and simulated data in 1:1 line (Moriasi et al., 2007). NSE value can vary from negative value to the model perfection value of 1.0. Coefficient of determination (R2) determines the correlation between the simulated data and measured data (Moriasi et al., 2007). The correlation coefficient ranges from the value of -1.0 to positive value of 1.0, and generally values greater than 0.5 are considered acceptable for hydrological modeling (Santhi e al., 2001). Percent bias (PBIAS) indicates underestimation or overestimation of the simulate data with respect to the observed data (Gupta et al., 1999). The model performs best when PBIAS is zero, with positive values indicating the underestimation of the model and negative value indicate overestimation of the model simulation (Gupta et al., 1999). RSR value can vary from model perfection value of zero to large positive values. Table 4.8 General performance rating for recommended parameters for streamflow (Moriasi et al., 2007). Performance Rating Very Good Good Satisfactory Unsatisfactory
RSR 0.00 ≤ RSR ≤ 0.50 0.50 < RSR ≤ 0.60 0.60 < RSR ≤ 0.70 RSR > 0.70
NSE 0.75 < NSE ≤ 1.00 0.65 < NSE ≤ 0.75 0.50 < NSE ≤ 0.65 NSE ≤ 0.50
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PBIAS PBIAS < ±10 ±10 ≤ PBIAS < ±15 ±15 ≤ PBIAS < ±25 PBIAS ≥ ±25
For the daily data, the model performance shows a good performance during calibration and validation period at the two outlets of the basin. At the Busti station, which is the outlet of the watershed, the model shows very good performance with NSE value of 0.76 during calibration period of 5 years and 0.84 during validation period of 2 years. Coefficient of determination (R2) is 0.76 for calibration and 0.85 for validation period. Percent bias is negative during calibration, which indicates model overestimation whereas the percent bias is positive during validation, which indicates model underestimation bias (Gupta et al., 1999). The acceptable range of PBIAS is
