# Evaluation of Hydropower Generation and Reservoir Operation under Climate Change from Kesem Reservoir, Ethiopia

Authors: Kinfe Bereda Mirani; Mesfin Amaru Ayele; Tarun Kumar Lohani; Tigistu Yisihak Ukumo
Journal: Advances in Meteorology
 (2022)

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## Abstract

Climate changes significantly cause the precipitation deficiency and in turn reduce the inflow amount in reservoir affecting hydroelectric power generation. The primary objective of this study was to evaluate hydropower generation and reservoir operation under climate change from Kesem reservoir. Recent Representative Pathway (RCP) scenarios were used to evaluate the impact of climate change on power generation. Power transformation equation and variance scaling approach were amalgamated to adjust the bias correction of precipitation and temperature, respectively. Bias, root mean square error, and coefficient of variation were used to check the accuracy of projected rainfall. The base and future precipitation, temperature, and evaporation trend was analysed using the Mann–Kendall test. The flow calibration and validation were carried out by the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and hydropower generation was evaluated with reservoir simulation model (MODSIM 8.1) under climate scenarios. The performance of the model was found good with Nash–Sutcliffe coefficient (NSE) of 0.72 and coefficient of determination (R2) of 0.73 for calibration and NSE of 0.74 and R2 of 0.75 for validation. Projected future climate scenarios predicted increasing and decreasing trend of temperature and precipitation, respectively. For RCP4.5 climate scenario, the average energy generation is likely to decrease by 0.64% and 0.82% in both short-term (2021–2050) and long-term (2051–2080), respectively. In case of RCP8.5 climate scenario, the average energy generation will be decreased by 1.06% and 1.35% for short-term and long-term, respectively. Remarkable reduction of energy generation was revealed in RCP8.5 with relation to RCP4.5 scenario. This indicates that there will be high energy fluctuation and decreasing trend in the future energy generation. The research finding is crucial for decision-makers, power authorities, governmental and nongovernmental organizations, and watershed management agencies to take care for sustainability in the future hydropower generation in the Kesem reservoir.


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## Body

## 1. Introduction

The well-known impacts of global warming on the water recourses system include changes in the hydrologic cycle and the water availability in the reservoir [1–3]. The variations in the hydrologic system are also articulated by increasing/decreasing frequencies of streamflow and reservoir storage capacity. Hydrologic risks and the unusual frequency of water catastrophe in turn affect the country’s economic activities [4].In electricity generation, hydropower contributes about 16% of energy which is more than other renewable electricity sources in the world [5]. About 200 Giga Watts (GW) of hydroelectricity can be explored from runoff river mini hydropower plants worldwide [6]. However, the world’s precipitation is affected by the variability of climate due to global warming that makes the hydropower plant vulnerable [7–9]. The impacts of climate change on water resources development are becoming a critical issue for many developing and developed countries in the world [10].The optimal development of hydroelectric power generation and water resources systems are the major factors for economic development of developing countries [11]. Total hydropower production capacity in Africa is around 70 Giga Watts (GW), while almost 25% of the hydroelectric power plants are presently not operating due to poor maintenance [12]. In sub-Saharan Africa (SSA) countries, the capacity of hydropower installed is 27 GW with the additional capacity of 15 GW hydropower plants under construction [13]. According to the International Energy Agency (IEA) [14], the forecasted hydropower capacity in SSA will reach 95 GW by 2040 and this shows the fast growing of hydropower industry. For hydropower-dependent countries, hydropower represents more than 50% of electricity production providing 45% to the SSA population through grids [15].Ethiopia is one of the developing countries, and it has rich water resources to generate hydropower with a capacity of 45,000 Mega Watt (MW) [16]. The country’s power producing capacity had expanded from 850 MW to 4300 MW in 2017 [17]. The Ethiopian government is seriously working on a number of hydropower projects to make the country become a power center in East Africa with the Gibe-III hydropower (1870 MW) constructed on Omo Gibe River and Grand Ethiopian Renaissance Dam with the installed capacity of 5000 MW which is constructing on Abay (Blue Nile) River [18]. Currently, the demand is increasing consistently resulting to frequent power cut [19]. In addition, the government is working hard to control the country’s 2030 GHG emissions to today’s 1500 Mt CO2 [20]. The emission is largely due to the traditional and unsustainable ways of using natural resources [21].In Ethiopia, exploiting hydropower potential is considered as a key issue to bring economic growth to the country [22]. The importance of the function of operating maximum efficiency of reservoir is one of the most important requirements [23]. Temperature, precipitation, and stream flow are altering due to climate change [24, 25], and its impact is significant on hydropower scheme, dam/reservoir design horizons, and life periods [26, 27]. Reservoirs are important in balancing water scarcity in the system and sustaining the hydropower generation [28–30]. Reservoir operating rules are widely used to give directions for basin releases in order to maintain the best benefits of a reservoir with specific inflow and storage levels [31]. The impacts of climate change variables such as precipitation and temperature have hydrological impacts on reservoir operation and hydropower production [32, 33]. Countries like Ethiopia which poured a substantial investment in hydroelectric power generation were concerned about the climate change in terms of variation in precipitation and temperature on hydropower production besides forecasting the future power generation [34]. Most of the developing countries are vulnerable to climate change impact due to lack of financial development and organized capacity [35]. In Ethiopia, the effects of climate change may seriously affect the reservoir operation and hydropower production [26, 36]. Currently, there is considerable uncertainty about the performance of reservoirs in the context of climate change in Ethiopia [37]. About 50% of capacity reduction is perceived in hydropower projects due to climate change in the Ethiopia [17] by which the total installed capacity has to increase by 5.9% compared with the New Policy Scenario (169 GW to 179 GW), though the energy generation from the hydroelectric power had decreased by 2.7% compared with the New Policy Scenario (517 TWh to 503 TWh).According to the Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the risks are projected for the near-term, mid-term, and long-term, at different global warming levels and for pathways that overshoot 1.5°C global warming level for multiple decades [38]. Increasing weather and climate extreme events have exposed millions of people to face severe food insecurity and reduced water security and energy, with the largest impacts cited in many locations and/or communities in Africa, Asia, Central and South America, Small Islands, and the Arctic [38].The impact of climate variation on hydropower production is studied by many researchers using General circulation models (GCMs)/Regional Climate Models (RCMs) [39–41]. Dynamical downscaling is one of the methods that transfer data from GCMs to smaller scales by using a higher resolution regional climate models (RCMs) [42, 43].Kesem watershed is one of the largest tributaries of Awash basin characterized by high climatic variation and water demands [44]. The climatic variation and water demands like irrigation, water supply for industries, and livestock put additional stress on Kesem reservoir and hydropower generation. There is no significant research on hydropower generation under climate change in the Kesem watershed for future periods. This study was initiated and focused on evaluating the future hydropower generation and reservoir operation under climate change.


## 2. Materials and Methods

### 2.1. Study Area

Kesem River basin is one of the sub-basins of Awash River basin located in the eastern part of Ethiopia. The basin is roughly found between 8°–10°N latitudes and 39°–40°E longitudes and covers a drainage area of 2885 km2 with the mean elevation of 1825 m above mean sea level (Figure 1) [45]. The land use types of the Kesem watershed are acacia, agricultural land, bare land, eucalyptus, forest, settlement, shrub land, water body, and grass land [46]. The dominant soil types are vertic cambisols, lithic leptosols, eutric vertisols eutric leptosols, eutric cambisol, and chromic luvisols [47]. The climate of the Kesem dam site is hot and semiarid to arid climatic zone (mean annual maximum and minimum temperature of 26.8°c and 10.56°c, respectively) with the mean annual rainfall of 882 mm [46]. In this watershed, rainfall is categorized by two different seasons like spring (February to May) and summer (July to September) [46].Figure 1
(a) Major river basins in Ethiopia, (b) Awash river basin, and (c) Kesem watershed.


### 2.2. Data Collection

Meteorological data (precipitation, temperature, relative humidity, wind speed, and sunshine) for five meteorological stations within and around the Kesem watershed were obtained from the Ethiopia National Meteorology Agency. And also, hydrological data (streamflow) were collected from Ethiopia ministry of Water, Irrigation, and Energy. Dynamically downscaled outputs of General Circulation Model climate data were obtained by using the CORDEX-Africa program (http://wcrp-cordex.ipsl.jussieufr) for Representative concentration pathway (RCP 4.5 and RCP 8.5) scenarios for the period (1951 to 2100). IPCC used the new RCP scenarios in the fifth assessment report to represent emissions trajectories of RCP 2.6 (low emissions), RCP 4.5 (intermediate emissions), RCP 6 (intermediate emissions), and RCP 8.5 (high emissions scenario) [48]. In this research, the precipitation, minimum and maximum temperature, evapotranspiration, and evaporation for short-term (2021–2050) and long-term (2051–2080) under the RCP 4.5 and RCP 8.5 scenarios with respect to base period (1971–2000) were projected. 30 m × 30 m digital elevation model (DEM) was downloaded from United States Geological Survey (USGS), SRTM (Shuttle Radar Topography Mission) website (http:/earthexplorer.usgs.gov/on 10 October 2021) and used to delineate watershed by ArcGIS 10.5.


### 2.3. Data Processing

#### 2.3.1. Bias Correction of RCP Data

The power transformation equation (Equation (1)) and the variance scaling (VARI) method were used for precipitation and temperature bias correction, respectively, since these methods found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature [49].(1)P∗=aPb,where P∗ is the corrected precipitation, a and b are the calibration parameters in the baseline period applied to the projected period, and P is the power transform constant for the reference point of the precipitation bias correction.The VARI method was used to adjust both the average and variance of normally distributed temperature data [49]:(2)T∗=Tob¯+δToδT¯RTRcp−T¯ob+T¯ob−T¯Rcp,where TRcp is the daily temperature, uncorrected (RCP data value), T¯ob is the observed daily average temperature, and T¯Rcp denotes the matching RCP basin mean temperature. In this equation, an over bar represents the average over the time period under consideration, as well as the standard deviation.


### 2.4. Accuracy of Rainfall Simulations from RCP Data

In this study, the accuracy of rainfall simulation of RCP data is checked by systematic error in rainfall amount (Bias), root mean square error (RMSE) and coefficient of variation. Each performance measures is expressed in equations (3)–(5) [50].(3)Bias=100∗Rrcp¯−Robs¯Robs¯,(4)RMSE=∑k=1NRrcp−Robs2N,(5)CV=100∗δRrcpδRobs,where N is the analysis period; Rrcp¯ and Robs¯ are the average rainfall amount obtained from RCP and observation data, respectively; and δ refers the standard deviation.


### 2.5. Trend Analysis

Trend analysis has been used to detect the impacts of climate change on precipitation, temperature, and evapotranspiration in the Kasem watershed. The Mann–Kendall test is a nonparametric test that does not require the data to follow normal distribution [51]:(6)S=∑i=1n−1∑j=i+1nsignYj−Yi,where Yj and Yi show the sequential precipitation or temperature values in average month/year where i = 1, 2, 3, …, n − 1 and j = i + 1, i + 2, i + 3… n. j and i (j > i).S is used to calculate the current and future annual precipitation and temperature trends.The variance ofS is calculated using the following equation:(7)VarS=nn−12n+518.If ties occur,n is the number of tied group and m is the number of tied values. For ties, the variance of S is calculated as(8)VarS=nn−102n+5−∑i=1ntiii−12i+518.For this study, an investigation was carried out to identify trends in climate data for 5 stations distributed over the whole of the Kesem watershed. Then, all the above equations are used to compute the Mann–Kendall Trend analysis [52].


### 2.6. Potential Evapotranspiration

The Penman and Hargreaves methods were used to compute evapotranspiration (ETo) on the Kesem watershed for the base and future periods [53]. The adjustment factor between the Penman and Hargreaves methods for future scenarios was used. The correction factor to be well-suited with the approach was adopted during model calibration, validation, and reservoir simulation [53]. In this research, evapotranspiration was calculated using both the Penman and Hargreaves methods for the base and future periods. A regression equation was developed to estimate the future potential evaporation:(9)RVE=∑i=1nQs−Qo∑i=1nQo∗100.


### 2.7. Reservoir Evaporation

Evaporation from reservoirs cannot be measured directly; it should be determined indirectly by number of methods [54]. In this study, the Penman Monteith method was used to estimate the monthly evapotranspiration rate at Kesem reservoir. The meteorological data (precipitation, temperature, relative humidity, wind speed, and sunshine) are collected at Metehara synoptic station, which is adjacent to the basin and is used to estimate monthly evapotranspiration (estimated evapotranspiration) from Kesem Reservoir.


### 2.8. HEC-HMS 4.2.1 Model Development

Input data used in the HEC-HMS model were stream flow, precipitation, evapotranspiration, and different watershed characteristics (digital elevation model, soil, and slope) obtained from Arc Hydro tools and the Geospatial Hydrologic Modelling Extension (HEC-GeoHMS). HEC-GeoHMS can be used under the GIS 10.5 environment, which is a geospatial hydrology tool allowing users to determine sub-basin streams as an input for the HEC-HMS hydrological model. Arc Hydro tools, an extension in ArcGIS, are used to process terrain data, define streams, and delineate the watershed of interest. The Kesem watershed is classified into three sub-basins such as sub-basin 1, sub-basin 2, and sub-basin 3 (Figure2).Figure 2
HEC-HMS model development.In addition, an outlet point (dam site) was used to estimate the total simulated flow. The Kesem watershed sub-basins, area, and the rainfall contribution of each sub-basin are illustrated in Table1.Table 1
Kesem watershed sub-basins, area, and rainfall contribution.
Sub-basinArea (km2)Contributing rainfall stationSub-basin 1488Debre berhanAleletuSub-basin 21880Awash7MetehraraChefedonsaSub-basin 3606ChefedonsaAleletu


### 2.9. HEC-HMS Model Performance

The Nash–Sutcliffe coefficient (NSE) was used to evaluate model performance and values varying from zero to one. NSE of one and zero indicates the excellent and poor model performance, respectively [55]. The values between 0.6 and 1.0 are considered to be very good performance:(10)NSE=1−∑i=1nQO−Qs2∑i=1nQo−Q¯2.The coefficient of determination (R2) was used to determine the strength of observed and simulated flow [56]:(11)R2=∑i=1nQs−Qs¯Qo–Qo¯2∑i=1nQs−Qs¯Qo–Qo¯2.Relative volume error (RVE) was used for quantifying the volume errors between observed and simulated value. When the RVE value is zero, its performance is the best which means there is no difference between simulated and observed runoff [57]:(12)RVE=∑i=1nQs−Qo∑i=1nQs−Qo∗100,where QO is the observed flow, QS is the simulated flow, Qo¯ is the average of observed flow, Qs¯ is the average of simulated flow, and n is the number of data points. RVE ranges between −∞ and +∞. The model performance is very good for RVE between −5% and 5%, while RVE between −10% and −5% and 5% and 10% suggests satisfactory performance and indicates a model with reasonable performance [57].


### 2.10. MODSIM 8.1 Model

MODSIM model is river basin management decision support system and used for river basin management and reservoir operation and to determine hydroelectric power generation [45, 58, 59]. The MODSIM model includes a powerful, interactive graphical user interface for creating, locating, and connecting eiver basin network components [60]. In this study, the input data used in the MODSIM model were stream flow, water demand, environmental release, net evaporation, maximum reservoir capacity, minimum and initial reservoir capacity, reservoir area and elevation, power plant capacity, load factor, tail water discharge, efficiency, and reservoir node properties.


## 2.1. Study Area

Kesem River basin is one of the sub-basins of Awash River basin located in the eastern part of Ethiopia. The basin is roughly found between 8°–10°N latitudes and 39°–40°E longitudes and covers a drainage area of 2885 km2 with the mean elevation of 1825 m above mean sea level (Figure 1) [45]. The land use types of the Kesem watershed are acacia, agricultural land, bare land, eucalyptus, forest, settlement, shrub land, water body, and grass land [46]. The dominant soil types are vertic cambisols, lithic leptosols, eutric vertisols eutric leptosols, eutric cambisol, and chromic luvisols [47]. The climate of the Kesem dam site is hot and semiarid to arid climatic zone (mean annual maximum and minimum temperature of 26.8°c and 10.56°c, respectively) with the mean annual rainfall of 882 mm [46]. In this watershed, rainfall is categorized by two different seasons like spring (February to May) and summer (July to September) [46].Figure 1
(a) Major river basins in Ethiopia, (b) Awash river basin, and (c) Kesem watershed.


## 2.2. Data Collection

Meteorological data (precipitation, temperature, relative humidity, wind speed, and sunshine) for five meteorological stations within and around the Kesem watershed were obtained from the Ethiopia National Meteorology Agency. And also, hydrological data (streamflow) were collected from Ethiopia ministry of Water, Irrigation, and Energy. Dynamically downscaled outputs of General Circulation Model climate data were obtained by using the CORDEX-Africa program (http://wcrp-cordex.ipsl.jussieufr) for Representative concentration pathway (RCP 4.5 and RCP 8.5) scenarios for the period (1951 to 2100). IPCC used the new RCP scenarios in the fifth assessment report to represent emissions trajectories of RCP 2.6 (low emissions), RCP 4.5 (intermediate emissions), RCP 6 (intermediate emissions), and RCP 8.5 (high emissions scenario) [48]. In this research, the precipitation, minimum and maximum temperature, evapotranspiration, and evaporation for short-term (2021–2050) and long-term (2051–2080) under the RCP 4.5 and RCP 8.5 scenarios with respect to base period (1971–2000) were projected. 30 m × 30 m digital elevation model (DEM) was downloaded from United States Geological Survey (USGS), SRTM (Shuttle Radar Topography Mission) website (http:/earthexplorer.usgs.gov/on 10 October 2021) and used to delineate watershed by ArcGIS 10.5.


## 2.3. Data Processing

### 2.3.1. Bias Correction of RCP Data

The power transformation equation (Equation (1)) and the variance scaling (VARI) method were used for precipitation and temperature bias correction, respectively, since these methods found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature [49].(1)P∗=aPb,where P∗ is the corrected precipitation, a and b are the calibration parameters in the baseline period applied to the projected period, and P is the power transform constant for the reference point of the precipitation bias correction.The VARI method was used to adjust both the average and variance of normally distributed temperature data [49]:(2)T∗=Tob¯+δToδT¯RTRcp−T¯ob+T¯ob−T¯Rcp,where TRcp is the daily temperature, uncorrected (RCP data value), T¯ob is the observed daily average temperature, and T¯Rcp denotes the matching RCP basin mean temperature. In this equation, an over bar represents the average over the time period under consideration, as well as the standard deviation.


## 2.3.1. Bias Correction of RCP Data

The power transformation equation (Equation (1)) and the variance scaling (VARI) method were used for precipitation and temperature bias correction, respectively, since these methods found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature [49].(1)P∗=aPb,where P∗ is the corrected precipitation, a and b are the calibration parameters in the baseline period applied to the projected period, and P is the power transform constant for the reference point of the precipitation bias correction.The VARI method was used to adjust both the average and variance of normally distributed temperature data [49]:(2)T∗=Tob¯+δToδT¯RTRcp−T¯ob+T¯ob−T¯Rcp,where TRcp is the daily temperature, uncorrected (RCP data value), T¯ob is the observed daily average temperature, and T¯Rcp denotes the matching RCP basin mean temperature. In this equation, an over bar represents the average over the time period under consideration, as well as the standard deviation.


## 2.4. Accuracy of Rainfall Simulations from RCP Data

In this study, the accuracy of rainfall simulation of RCP data is checked by systematic error in rainfall amount (Bias), root mean square error (RMSE) and coefficient of variation. Each performance measures is expressed in equations (3)–(5) [50].(3)Bias=100∗Rrcp¯−Robs¯Robs¯,(4)RMSE=∑k=1NRrcp−Robs2N,(5)CV=100∗δRrcpδRobs,where N is the analysis period; Rrcp¯ and Robs¯ are the average rainfall amount obtained from RCP and observation data, respectively; and δ refers the standard deviation.


## 2.5. Trend Analysis

Trend analysis has been used to detect the impacts of climate change on precipitation, temperature, and evapotranspiration in the Kasem watershed. The Mann–Kendall test is a nonparametric test that does not require the data to follow normal distribution [51]:(6)S=∑i=1n−1∑j=i+1nsignYj−Yi,where Yj and Yi show the sequential precipitation or temperature values in average month/year where i = 1, 2, 3, …, n − 1 and j = i + 1, i + 2, i + 3… n. j and i (j > i).S is used to calculate the current and future annual precipitation and temperature trends.The variance ofS is calculated using the following equation:(7)VarS=nn−12n+518.If ties occur,n is the number of tied group and m is the number of tied values. For ties, the variance of S is calculated as(8)VarS=nn−102n+5−∑i=1ntiii−12i+518.For this study, an investigation was carried out to identify trends in climate data for 5 stations distributed over the whole of the Kesem watershed. Then, all the above equations are used to compute the Mann–Kendall Trend analysis [52].


## 2.6. Potential Evapotranspiration

The Penman and Hargreaves methods were used to compute evapotranspiration (ETo) on the Kesem watershed for the base and future periods [53]. The adjustment factor between the Penman and Hargreaves methods for future scenarios was used. The correction factor to be well-suited with the approach was adopted during model calibration, validation, and reservoir simulation [53]. In this research, evapotranspiration was calculated using both the Penman and Hargreaves methods for the base and future periods. A regression equation was developed to estimate the future potential evaporation:(9)RVE=∑i=1nQs−Qo∑i=1nQo∗100.


## 2.7. Reservoir Evaporation

Evaporation from reservoirs cannot be measured directly; it should be determined indirectly by number of methods [54]. In this study, the Penman Monteith method was used to estimate the monthly evapotranspiration rate at Kesem reservoir. The meteorological data (precipitation, temperature, relative humidity, wind speed, and sunshine) are collected at Metehara synoptic station, which is adjacent to the basin and is used to estimate monthly evapotranspiration (estimated evapotranspiration) from Kesem Reservoir.


## 2.8. HEC-HMS 4.2.1 Model Development

Input data used in the HEC-HMS model were stream flow, precipitation, evapotranspiration, and different watershed characteristics (digital elevation model, soil, and slope) obtained from Arc Hydro tools and the Geospatial Hydrologic Modelling Extension (HEC-GeoHMS). HEC-GeoHMS can be used under the GIS 10.5 environment, which is a geospatial hydrology tool allowing users to determine sub-basin streams as an input for the HEC-HMS hydrological model. Arc Hydro tools, an extension in ArcGIS, are used to process terrain data, define streams, and delineate the watershed of interest. The Kesem watershed is classified into three sub-basins such as sub-basin 1, sub-basin 2, and sub-basin 3 (Figure2).Figure 2
HEC-HMS model development.In addition, an outlet point (dam site) was used to estimate the total simulated flow. The Kesem watershed sub-basins, area, and the rainfall contribution of each sub-basin are illustrated in Table1.Table 1
Kesem watershed sub-basins, area, and rainfall contribution.
Sub-basinArea (km2)Contributing rainfall stationSub-basin 1488Debre berhanAleletuSub-basin 21880Awash7MetehraraChefedonsaSub-basin 3606ChefedonsaAleletu


## 2.9. HEC-HMS Model Performance

The Nash–Sutcliffe coefficient (NSE) was used to evaluate model performance and values varying from zero to one. NSE of one and zero indicates the excellent and poor model performance, respectively [55]. The values between 0.6 and 1.0 are considered to be very good performance:(10)NSE=1−∑i=1nQO−Qs2∑i=1nQo−Q¯2.The coefficient of determination (R2) was used to determine the strength of observed and simulated flow [56]:(11)R2=∑i=1nQs−Qs¯Qo–Qo¯2∑i=1nQs−Qs¯Qo–Qo¯2.Relative volume error (RVE) was used for quantifying the volume errors between observed and simulated value. When the RVE value is zero, its performance is the best which means there is no difference between simulated and observed runoff [57]:(12)RVE=∑i=1nQs−Qo∑i=1nQs−Qo∗100,where QO is the observed flow, QS is the simulated flow, Qo¯ is the average of observed flow, Qs¯ is the average of simulated flow, and n is the number of data points. RVE ranges between −∞ and +∞. The model performance is very good for RVE between −5% and 5%, while RVE between −10% and −5% and 5% and 10% suggests satisfactory performance and indicates a model with reasonable performance [57].


## 2.10. MODSIM 8.1 Model

MODSIM model is river basin management decision support system and used for river basin management and reservoir operation and to determine hydroelectric power generation [45, 58, 59]. The MODSIM model includes a powerful, interactive graphical user interface for creating, locating, and connecting eiver basin network components [60]. In this study, the input data used in the MODSIM model were stream flow, water demand, environmental release, net evaporation, maximum reservoir capacity, minimum and initial reservoir capacity, reservoir area and elevation, power plant capacity, load factor, tail water discharge, efficiency, and reservoir node properties.


## 3. Results and Discussion

### 3.1. RCP Rainfall Data Performance Evaluation

The mean annual observed rainfall of the basin is 882 mm/year, and the RCP rainfall value is 1115 mm (Table2). The accuracy of RCP rainfall data performance showed that the rainfall of RCP data overestimated by 26% and root mean square (RMSE) performed 31 mm/year compared with the observed value.Table 2
Accuracy of rainfall from RCP data.
Mean annual rainfall (mm)Bias (%)CV (%)RMSE (mm year−1)Observed882—9.6—RCP1115267.831


### 3.2. Trends in Historical Climate

Figure3 shows that the rainfall has decreased during the period from 1989 to 2003 except 1993 and 1998 (Figure 3(a)). The rainfall value was remarkably decreased by 6.706 mm/year. The Kesem watershed received its lowest rainfall amount in the periods of 2002. In the case of temperature, there was a considerable increasing trend (Figures 3(c) and 3(d)). The mean annual maximum and minimum temperature in the watershed has increased at rate of 0.42°C and 0.38°C per decade, respectively. The potential evapotranspiration (PET) shows an increasing trend between 1999 and 2005 (Figure 3(b)).Figure 3
Trends in historical climatic: (a) rainfall, (b) evapotranspiration, (c) maximum temperature, and (d) minimum temperature data. Bias correction of precipitation and temperature data.
(a)(b)(c)(d)The bias correction result showed that there was a significant systematic error between the observed and climatic data in the months of April, May, June, July, August, and September (Figure4). However, there was a small error revealed in RCP rainfall data in the months of January, February, March, October, November, and December.Figure 4
Bias correction of average monthly precipitations.The RCP average maximum temperature and observed temperature data show small underestimates for all months (Figure5).Figure 5
Bias correction of average monthly maximum temperature.Figure6 shows the RCP average minimum temperature and the observed minimum temperature.Figure 6
Bias correction of average monthly minimum temperature.The RCP average minimum temperature was slightly underestimated in most of the months except January, February, March, April, and May (Figure6).


### 3.3. Future Scenarios of Precipitation and Temperature

Figure7 shows the average monthly precipitation in the Kesem watershed under RCP4.5 and RCP8.5 scenarios with the baseline period (1971–2000) and future periods (2021–2050 and 2051–2080). Average monthly precipitation for RCP4.5 scenario was changed by −9.7% and 16% in (2021–2050) and (2051–2080), respectively, and also for RCP8.5 average monthly precipitation was decreased by 16.4% and 17.6% in (2021–2050) and (2051–2080), respectively.Figure 7
Average monthly precipitation of future scenarios for Kesem watershed.The maximum temperatures in the climate scenario RCP4.5 and RCP8.5 for the baseline period (1971–2000) and future periods (2021–2050 and 20051–2080) are illustrated in Figure8. The monthly average maximum temperature rises by 0.2°C and 0.5°C for RCP4.5 (2021–2050) and (2051–2080), respectively, and by 0.7°C and 1°C for RCP8.5 (2021–2050) and (2051–2080), respectively.Figure 8
Average monthly maximum temperatures for future scenarios.Figure9 shows the minimum temperature in the climate scenarios of RCP4.5 and RCP8.5 for the baseline period (1971–2000) and future periods (2021–2050 and 20051–2080). Monthly average minimum temperature was increased by 0.4°C and 0.7°C for RCP4.5 scenario for (2021–2050) and (2051–2080), respectively. Moreover, RCP8.5 monthly average minimum temperature was increased by 0.5°C and 0.6°C in future periods (2021–2050) and (2051–2080), respectively.Figure 9
Average monthly minimum temperatures for future scenarios.


### 3.4. Climate Change Impact in Kesem Watershed

Precipitation was changed by −9.7% (2021–2050) and 16% (2051–2080) in the RCP4.5 scenario. In the RCP8.5 scenario, precipitation indicates a decreasing trend and the result shows 16.4% (2021–2050) and 17.5% (2051–2080). Climate change impacts on the Kesem watershed were compared with the observed period (1971–2000) and future periods (2021–2050 and 20051–2080) in the RCP4.5 and RCP8.5 scenarios (Figure10).Figure 10
Change in average monthly rainfall over the period (2021–2050 and 2051–2080).Maximum temperature was increased by 0.2% (2021–2050) and 0.5% (2051–2080) for RCP4.5 scenarios, respectively (Figure11). The average monthly temperature for the RCP8.5 scenario shows rising temperatures and fluctuated by 0.7% (2021–2050) and 1% (2051–2080).Figure 11
Change in average monthly maximum temperature.Figure12 demonstrates that during the future time period, the mean monthly minimum temperature over the Kesem watershed increased by 0.2% (2021–2050) and 0.3% (2051–2080) under RCP4.5 scenario. The average monthly minimum temperature change for the RCP8.5 scenario shows an increase in temperature.Figure 12
Change in average monthly minimum temperature.


### 3.5. Evapotranspiration

Figure13 shows RCP4.5 and RCP8.5 scenarios of average monthly evapotranspiration increasing trend in the future scenarios. From Figure 13, it is observed that the average monthly evapotranspiration was increased by 2.5% (2021–2050) and 4.4% (2051–2080) for RCP4.5 and also for RCP8.5 increased by 3.4% (2021–2050) and 6.8% (2051–2080).Figure 13
Changes in average monthly evapotranspiration.


### 3.6. Evaporation

In the RCP4.5 and RCP8.5 scenarios, the rate of monthly evaporation in the Kesem watershed was significantly increasing for both short-term (2021–2050) and long-term (2051–2080) (Figure14).Figure 14
Changes in average monthly evaporation from Kesem reservoir.


### 3.7. HEC-HMS Model Sensitivity Analysis, Calibration, and Validation

Sensitivity analysis can be used to identify more sensitive model parameters for further model calibration. The effects of each ±30% modification in the model parameter values were investigated [61]. To test the sensitive parameters, each model parameters can have its value increased or decreased by up to ±30%. After the model parameters were adjusted by ±30% of peak volume, the sensitive parameters were identified. The constant rate (CR), storage coefficient (SC), and dimensionless weight (X) were more sensitive parameters (Figure 15). The recession constant (RC), the initial loss (IL), time of concentration (TC), Initial discharge (ID), recession constant (RC), Ratio-to-peak (RP), and Travel time (K) all were less sensitive parameters.Figure 15
Percentage change in volume verses percentage of parameter variation.Streamflow data were used to calibrate (1992 to 2003) and validate (2004 to 2009) the HEC-HMS with warm up period (1990 to 1991) (Figure16).Figure 16
Observed and simulated stream flow at Kesem dam.Stream flow calibration process is used to establish the most suitable parameter in modelling studies and an iterative process that compares simulated and observed data through parameter evaluation. The observed areal precipitation, areal evapotranspiration, and observed stream flow are calibrated using automatic and manual calibration process for Kesem reservoir. The calibration result (Figure16) showed that the model has good agreement between the observed and simulated stream flow, and the peak is well captured most of year. However, there is some underestimation in the years of 1993, 1997, and 2000 for simulated flow.After calibration of the model, Nash–Sutcliffe coefficient (NSE) value of 0.72, coefficient of determination (R2) of 0.73, and relative volume error (RVE) of 0.063% were observed. Moreover, for validation, the model performed with NSE of 0.74, R2 of 0.75, and RVE of −4.7%. Based on Moriasi et al. [55] model evaluation criteria, the aforementioned model performance result was viewed as acceptable level and good correlation of data. Figure 16 shows the peak stream flow well captured in the years of 2004 and 2006. However, there is underestimation in 2005, 2003, and 2008 and overestimation in 2009.


### 3.8. Reservoir Inflow

Reservoir inflow (stream flow data) was used to forecast the future scenarios in different time horizons including the baseline, short-term, and long-term. Average stream flow will change by −12.93% (2021–2050) and −17.01% (2051–2080) RCP4.5 as compared with the observed flow. Average monthly stream flow will likely decrease by −21.83% (2021–2050) and −24.87% (2051–2080) for RCP8.5 scenario, and this shows a downward trend (Figure17).Figure 17
Kesem reservoir inflow discharge at different time horizons.


### 3.9. Hydropower Generation with Observed Data

The hydropower production anticipated from the observed flow data results depicts that there is a high fluctuation of average power generation and energy. In the observed period (1987–2005), the reservoir simulation showed that the maximum energy generated was 378.613 MWH with the average energy of 300.093 MWH (Figure18).Figure 18
Average hydropower verses energy for base period scenario.


### 3.10. Hydropower Generation with RCPs Scenarios

The reservoir simulation focused on energy variation between observed period (1987–2005) and future (2021–2050) and (2051–2080) periods for RCP4.5 and RCP8.5 scenarios. In RCP4.5 scenario, energy generated for short-term period (2021–2050) will be 376.213 MWH with the average energy of 321.392 MWH. In the case of long-term (2051–2080), energy generated in the long-term scenarios (2051–2080) will be 370.513 MWH with the average energy of 307.682 MWH. This indicates that the average energy generation will be decreased by 0.64% and 0.82% in both short-term and long-term, respectively.The RCP8.5 scenario result showed that the maximum energy generated in short-term scenarios will be 368.605 MWH, with an average energy of 320.411 MWH. In the case of long-term, for RCP8.5, the maximum energy generated will be 363.492 MWH and average energy of 306.584 MWH. In both short-term and long-term, the average energy generated will likely decrease by 1.06% and 1.35%, respectively. Figure 19 shows the sample of the MODSIM output of daily hydropower generation production and energy.Figure 19
Daily hydropower generation and energy.


### 3.11. Average Hydropower Generation

The result of this study reveals that the Kesem reservoirs daily hydropower generation and water volume reservoir had a good hydropower generation potential for baseline condition. However, as a result of the influence of climate change on the Kesem watershed, the inflow, storage volume, and hydropower output have all declined. Figure20 shows that on average the hydropower generated will likely change or slightly decrease between the observed period −0.64% (2021–2050) and −0.82% (2051–2080) RCP4.5 scenario, respectively. The RCP8.5 scenario of average monthly hydropower change shows decreased power on average; the power will likely change by −1.06% (2021–2050) and −1.35% (2051–2080), respectively.Figure 20
Average monthly power generation.


### 3.12. Reservoir Operation Guide Curve

Depending on Chaleeraktrakoon and Chinsomboon [62] reservoir guide curve, the reservoir rule curves were developed for Kesem reservoir and classified into three periods. These were the observed period (1987–2005), short-term (2021–2050), and long-term (2051–2080) for RCP4.5 and RCP8.5 scenarios to achieve the target demand of the projects. Figure 21 shows the reservoir rule guide curves for Kesem reservoir, and the result shows the decreasing trend for future scenarios compared with the observed period. The findings of the research work indicate that in Kesem, watershed climate change affects the reservoir inflow and reservoir operation and in turn reduces the future hydropower and energy production. This research finding is important for undertaking climate change mitigation strategies, watershed management practices, and to control the future change of hydropower production.Figure 21
Reservoir operation guide curve.


## 3.1. RCP Rainfall Data Performance Evaluation

The mean annual observed rainfall of the basin is 882 mm/year, and the RCP rainfall value is 1115 mm (Table2). The accuracy of RCP rainfall data performance showed that the rainfall of RCP data overestimated by 26% and root mean square (RMSE) performed 31 mm/year compared with the observed value.Table 2
Accuracy of rainfall from RCP data.
Mean annual rainfall (mm)Bias (%)CV (%)RMSE (mm year−1)Observed882—9.6—RCP1115267.831


## 3.2. Trends in Historical Climate

Figure3 shows that the rainfall has decreased during the period from 1989 to 2003 except 1993 and 1998 (Figure 3(a)). The rainfall value was remarkably decreased by 6.706 mm/year. The Kesem watershed received its lowest rainfall amount in the periods of 2002. In the case of temperature, there was a considerable increasing trend (Figures 3(c) and 3(d)). The mean annual maximum and minimum temperature in the watershed has increased at rate of 0.42°C and 0.38°C per decade, respectively. The potential evapotranspiration (PET) shows an increasing trend between 1999 and 2005 (Figure 3(b)).Figure 3
Trends in historical climatic: (a) rainfall, (b) evapotranspiration, (c) maximum temperature, and (d) minimum temperature data. Bias correction of precipitation and temperature data.
(a)(b)(c)(d)The bias correction result showed that there was a significant systematic error between the observed and climatic data in the months of April, May, June, July, August, and September (Figure4). However, there was a small error revealed in RCP rainfall data in the months of January, February, March, October, November, and December.Figure 4
Bias correction of average monthly precipitations.The RCP average maximum temperature and observed temperature data show small underestimates for all months (Figure5).Figure 5
Bias correction of average monthly maximum temperature.Figure6 shows the RCP average minimum temperature and the observed minimum temperature.Figure 6
Bias correction of average monthly minimum temperature.The RCP average minimum temperature was slightly underestimated in most of the months except January, February, March, April, and May (Figure6).


## 3.3. Future Scenarios of Precipitation and Temperature

Figure7 shows the average monthly precipitation in the Kesem watershed under RCP4.5 and RCP8.5 scenarios with the baseline period (1971–2000) and future periods (2021–2050 and 2051–2080). Average monthly precipitation for RCP4.5 scenario was changed by −9.7% and 16% in (2021–2050) and (2051–2080), respectively, and also for RCP8.5 average monthly precipitation was decreased by 16.4% and 17.6% in (2021–2050) and (2051–2080), respectively.Figure 7
Average monthly precipitation of future scenarios for Kesem watershed.The maximum temperatures in the climate scenario RCP4.5 and RCP8.5 for the baseline period (1971–2000) and future periods (2021–2050 and 20051–2080) are illustrated in Figure8. The monthly average maximum temperature rises by 0.2°C and 0.5°C for RCP4.5 (2021–2050) and (2051–2080), respectively, and by 0.7°C and 1°C for RCP8.5 (2021–2050) and (2051–2080), respectively.Figure 8
Average monthly maximum temperatures for future scenarios.Figure9 shows the minimum temperature in the climate scenarios of RCP4.5 and RCP8.5 for the baseline period (1971–2000) and future periods (2021–2050 and 20051–2080). Monthly average minimum temperature was increased by 0.4°C and 0.7°C for RCP4.5 scenario for (2021–2050) and (2051–2080), respectively. Moreover, RCP8.5 monthly average minimum temperature was increased by 0.5°C and 0.6°C in future periods (2021–2050) and (2051–2080), respectively.Figure 9
Average monthly minimum temperatures for future scenarios.


## 3.4. Climate Change Impact in Kesem Watershed

Precipitation was changed by −9.7% (2021–2050) and 16% (2051–2080) in the RCP4.5 scenario. In the RCP8.5 scenario, precipitation indicates a decreasing trend and the result shows 16.4% (2021–2050) and 17.5% (2051–2080). Climate change impacts on the Kesem watershed were compared with the observed period (1971–2000) and future periods (2021–2050 and 20051–2080) in the RCP4.5 and RCP8.5 scenarios (Figure10).Figure 10
Change in average monthly rainfall over the period (2021–2050 and 2051–2080).Maximum temperature was increased by 0.2% (2021–2050) and 0.5% (2051–2080) for RCP4.5 scenarios, respectively (Figure11). The average monthly temperature for the RCP8.5 scenario shows rising temperatures and fluctuated by 0.7% (2021–2050) and 1% (2051–2080).Figure 11
Change in average monthly maximum temperature.Figure12 demonstrates that during the future time period, the mean monthly minimum temperature over the Kesem watershed increased by 0.2% (2021–2050) and 0.3% (2051–2080) under RCP4.5 scenario. The average monthly minimum temperature change for the RCP8.5 scenario shows an increase in temperature.Figure 12
Change in average monthly minimum temperature.


## 3.5. Evapotranspiration

Figure13 shows RCP4.5 and RCP8.5 scenarios of average monthly evapotranspiration increasing trend in the future scenarios. From Figure 13, it is observed that the average monthly evapotranspiration was increased by 2.5% (2021–2050) and 4.4% (2051–2080) for RCP4.5 and also for RCP8.5 increased by 3.4% (2021–2050) and 6.8% (2051–2080).Figure 13
Changes in average monthly evapotranspiration.


## 3.6. Evaporation

In the RCP4.5 and RCP8.5 scenarios, the rate of monthly evaporation in the Kesem watershed was significantly increasing for both short-term (2021–2050) and long-term (2051–2080) (Figure14).Figure 14
Changes in average monthly evaporation from Kesem reservoir.


## 3.7. HEC-HMS Model Sensitivity Analysis, Calibration, and Validation

Sensitivity analysis can be used to identify more sensitive model parameters for further model calibration. The effects of each ±30% modification in the model parameter values were investigated [61]. To test the sensitive parameters, each model parameters can have its value increased or decreased by up to ±30%. After the model parameters were adjusted by ±30% of peak volume, the sensitive parameters were identified. The constant rate (CR), storage coefficient (SC), and dimensionless weight (X) were more sensitive parameters (Figure 15). The recession constant (RC), the initial loss (IL), time of concentration (TC), Initial discharge (ID), recession constant (RC), Ratio-to-peak (RP), and Travel time (K) all were less sensitive parameters.Figure 15
Percentage change in volume verses percentage of parameter variation.Streamflow data were used to calibrate (1992 to 2003) and validate (2004 to 2009) the HEC-HMS with warm up period (1990 to 1991) (Figure16).Figure 16
Observed and simulated stream flow at Kesem dam.Stream flow calibration process is used to establish the most suitable parameter in modelling studies and an iterative process that compares simulated and observed data through parameter evaluation. The observed areal precipitation, areal evapotranspiration, and observed stream flow are calibrated using automatic and manual calibration process for Kesem reservoir. The calibration result (Figure16) showed that the model has good agreement between the observed and simulated stream flow, and the peak is well captured most of year. However, there is some underestimation in the years of 1993, 1997, and 2000 for simulated flow.After calibration of the model, Nash–Sutcliffe coefficient (NSE) value of 0.72, coefficient of determination (R2) of 0.73, and relative volume error (RVE) of 0.063% were observed. Moreover, for validation, the model performed with NSE of 0.74, R2 of 0.75, and RVE of −4.7%. Based on Moriasi et al. [55] model evaluation criteria, the aforementioned model performance result was viewed as acceptable level and good correlation of data. Figure 16 shows the peak stream flow well captured in the years of 2004 and 2006. However, there is underestimation in 2005, 2003, and 2008 and overestimation in 2009.


## 3.8. Reservoir Inflow

Reservoir inflow (stream flow data) was used to forecast the future scenarios in different time horizons including the baseline, short-term, and long-term. Average stream flow will change by −12.93% (2021–2050) and −17.01% (2051–2080) RCP4.5 as compared with the observed flow. Average monthly stream flow will likely decrease by −21.83% (2021–2050) and −24.87% (2051–2080) for RCP8.5 scenario, and this shows a downward trend (Figure17).Figure 17
Kesem reservoir inflow discharge at different time horizons.


## 3.9. Hydropower Generation with Observed Data

The hydropower production anticipated from the observed flow data results depicts that there is a high fluctuation of average power generation and energy. In the observed period (1987–2005), the reservoir simulation showed that the maximum energy generated was 378.613 MWH with the average energy of 300.093 MWH (Figure18).Figure 18
Average hydropower verses energy for base period scenario.


## 3.10. Hydropower Generation with RCPs Scenarios

The reservoir simulation focused on energy variation between observed period (1987–2005) and future (2021–2050) and (2051–2080) periods for RCP4.5 and RCP8.5 scenarios. In RCP4.5 scenario, energy generated for short-term period (2021–2050) will be 376.213 MWH with the average energy of 321.392 MWH. In the case of long-term (2051–2080), energy generated in the long-term scenarios (2051–2080) will be 370.513 MWH with the average energy of 307.682 MWH. This indicates that the average energy generation will be decreased by 0.64% and 0.82% in both short-term and long-term, respectively.The RCP8.5 scenario result showed that the maximum energy generated in short-term scenarios will be 368.605 MWH, with an average energy of 320.411 MWH. In the case of long-term, for RCP8.5, the maximum energy generated will be 363.492 MWH and average energy of 306.584 MWH. In both short-term and long-term, the average energy generated will likely decrease by 1.06% and 1.35%, respectively. Figure 19 shows the sample of the MODSIM output of daily hydropower generation production and energy.Figure 19
Daily hydropower generation and energy.


## 3.11. Average Hydropower Generation

The result of this study reveals that the Kesem reservoirs daily hydropower generation and water volume reservoir had a good hydropower generation potential for baseline condition. However, as a result of the influence of climate change on the Kesem watershed, the inflow, storage volume, and hydropower output have all declined. Figure20 shows that on average the hydropower generated will likely change or slightly decrease between the observed period −0.64% (2021–2050) and −0.82% (2051–2080) RCP4.5 scenario, respectively. The RCP8.5 scenario of average monthly hydropower change shows decreased power on average; the power will likely change by −1.06% (2021–2050) and −1.35% (2051–2080), respectively.Figure 20
Average monthly power generation.


## 3.12. Reservoir Operation Guide Curve

Depending on Chaleeraktrakoon and Chinsomboon [62] reservoir guide curve, the reservoir rule curves were developed for Kesem reservoir and classified into three periods. These were the observed period (1987–2005), short-term (2021–2050), and long-term (2051–2080) for RCP4.5 and RCP8.5 scenarios to achieve the target demand of the projects. Figure 21 shows the reservoir rule guide curves for Kesem reservoir, and the result shows the decreasing trend for future scenarios compared with the observed period. The findings of the research work indicate that in Kesem, watershed climate change affects the reservoir inflow and reservoir operation and in turn reduces the future hydropower and energy production. This research finding is important for undertaking climate change mitigation strategies, watershed management practices, and to control the future change of hydropower production.Figure 21
Reservoir operation guide curve.


## 4. Conclusion

The evaluation of hydropower generation and reservoir operation under climate change can play a significant role by providing the baseline and future hydropower production evidences in the particular hydropower plant. Climate change is likely to have severe effects on water availability of Kesem reservoir. The result of climate projection reveals that the RCP data can be seen considering climate scenarios of RCP4.5 and RCP8.5 for short-term and long-term.The HEC-HMS model performance during calibration period for stream flow was good with the NSE of 0.72,R2 of 0.73, and RVE of −0.063%. During validation period, model performance was also good result with NSE of 0.74, R2 of 0.75, and RVE of −4.7%. Reservoir operation for base and future period was analysed using the reservoir simulation model (MODSIM). The model had simulated the amount of release and hydropower generation to determine reservoir operation rule curve for both RCP4.5 and RCP8.5 scenarios.Accordingly, the output generated will be used for the hydropower production and reservoir operation rule curve for different climate periods that includes baseline (1987–2005), short-term (2021–2050), and long-term (2051–2080). The quantified maximum energy for RCP4.5 will be 376.213 MWH and 370.513 MWH in short and long period, respectively. Moreover, in case of RCP8.5, the maximum energy of 368.605 MWH and 363.492 MWH will likely generate for the short and long period, respectively. This indicates that there is fluctuation in energy generation and decreasing trend in the future energy production from the Kesem reservoir. The outcome of this study is important to hydroelectric power authorities and watershed management agencies to ensure sustainability in the future hydroelectric power production in the Kesem reservoir and it is highly suggested that other scientific researchers can apply the same approach for similar type of reservoirs.



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*Source: 3336257-2022-06-22.xml*