Impact of climate change on hydropower generation in Rio Jubones Basin,Ecuador

Mohammad Mehedi Hasan*,Guido Wyseure

aHydraulic Research Directorate,River Research Institute,Faridpur 7800,Bangladesh

bDepartment of Earth and Environmental Sciences,KU Leuven,Leuven 3001,Belgium

Abstract This study attempted to use the soil and water assessment tool(SWAT),integrated with geographic information systems(GIS),for assessment of climate change impacts on hydropower generation.This methodology of climate change impact modeling was developed and demonstrated through application to a hydropower plant in the Rio Jubones Basin in Ecuador.ArcSWAT 2012 was used to develop a model for simulating the river flow.The model parameters were calibrated and validated on a monthly scale with respect to the hydro-meteorological inputs observed from 1985 to 1991 and from 1992 to 1998,respectively.Statistical analyses produced Nash-Sutcliffe efficiencies(NSEs)of 0.66 and 0.61 for model calibration and validation,respectively,which were considered acceptable.Numerical simulation with the model indicated that climate change could alter the seasonal flow regime of the basin,and the hydropower potential could change due to the changing climate in the future.Scenario analysis indicates that,though the hydropower generation will increase in the wet season,the plant will face a significant power shortage during the dry season,up to 13.14%from the reference scenario,as a consequence of a 17%reduction of stream flow under an assumption of a 2.9°C increase in temperature and a 15%decrease in rainfall.Overall,this study showed that hydrological processes are realistically modeled with SWAT and the model can be a useful tool for predicting the impact of climate change.

Keywords:SWAT;Hydropower generation;Climate change;Sensitivity analysis;Nash-Sutcliffe efficiency(NSE)

1.Introduction

The United Nations Framework Convention on Climate Change(UNFCCC)defines climate change as an alteration of climate,accountable definitely or indefinitely to anthropogenic activities,that changes the structure of the global atmosphere and is discerned over a time period,distinguishing it from the natural climate instability(Houghton et al.,2001).To mitigate climate change impacts,several solutions have been proposed to reduce greenhouse gas emissions,including modern efficient energy alternatives and enhancing the use of sustainable energy sources.Among alternative options for power generation,hydropower is the most lucrative due to renewability,lower emissions,and longevity of infrastructure.Though the hydropower sector makes a significant effort to cope with today's rising world energy demands,this is difficult due to climate change effects.Madani(2011)stated that climate change will have a variety of effects on stream flow,involving quantity and timing,temperature,sediment load,and ecosystem changes.Temperature fluctuations,rainfall patterns, floods,and droughts are all major signs of climate change that have strong effects on river systems,which will consequently affect hydropower generation.The possible recession of river flow may lead to decreased hydropower generation,which in turn will have certain in fluences on the economic viability of the hydropower plant schemes.Hydropower plants with low storage capacities are more susceptible to climate change,as a high storage capacity requires more workability to operate.

Watershed models and general circulation models(GCMs)are often used to simulate the impact of climate change on watershed hydrology(Park et al.,2011).They are usually used to obtain the climatic data with the help of some downscaling techniques in a particular study area.Because of the quite coarse resolution,they are incapable of predicting regional climate scenarios.Furthermore,they are not designed to assess climate change impacts and are not used to provide a direct assessment of hydrological response to climate change(Dibike and Coulibaly,2005),because of a high amount of uncertainty.As for a regional climate model(RCM),the potential of simulating wide-scale data with significant,physically reliable climate variations and its advanced representation of extreme climate events have been the major advantages(Huntingford et al.,2003).However,computations of RCMs are detailed,and a small number of scenario ensembles are available,which limits the model quantification to 30 years under the existing climatic conditions from 1961 to 1990 as well as from 2071 to 2100(Fowleretal.,2007).Thismakesitdifficulttoevaluatethe climate change impacts for other phases.Moreover,Buytaert et al.(2010)have shown that,for some tropical regions,RCMsmightyieldunreliableorpoorresultsincomparisonwith GCMs on certain scales,particularly for precipitation.

Andean landscapes provide a good opportunity for hydropower generation(Buytaert et al.,2006).However,energy generation is vulnerable to climate change effects(Koch et al.,2011;Tamm et al.,2016).The Andes region,including the South Ecuadorian Andes,shows a high degree of weather and climate variability(Espinoza Villar et al.,2009).Considering the consequences of climate anomalies,a number of studies have been conducted to investigate various aspects of climate change impacts on water resources of the Andes region.Urrutia and Vuille(2009)quantified future climate change in the tropical Andes of Ecuador by means of an RCM.The uncertainties in projections of climate change impacts on regional water resources in the tropical Andes were evaluated by Buytaert et al.(2010).Vuille et al.(2003)conducted another study to compare observed and modeled results associated with the climate change effects in the 20th century in the tropical Andes.More recently,Mora et al.(2014)studied the climate change trends for hydro-meteorological extremes across the Paute River Basin,in the Ecuadorian Andes.However,much less attention has been paid to climate change impacts with regard to water availability or hydropower potential,which is important because hydropower has become the principal source of energy in most of the Andean countries(Bradley et al.,2006).

In Ecuador,greater hydropower generation is needed to compensate for ongoing population growth.To meet the country’s rising demand for electric energy,a new hydropower plant is being constructed in the Rio Jubones Basin,in Ecuador.This study aimed to investigate the climate change impact on hydropower generation in the Rio Jubones Basin.To this end,the Minas-San Francisco Hydroelectric Project,being constructed on the right bank of the Jubones River between the provinces of Azuay and El Oro,with an installed capacity of 275 MW,was studied.To assess the possible impact of climate change on hydropower generation,analysis of the river flow related to climate variability is required,as it will help planners and managers design and operate the hydropower plant efficiently.

In this study,the soil and water assessment tool(SWAT)model,which is basically a river basin model,was used to simulateriver flow.SWATisaprocess-basedandspatiallysemidistributed hydrological model developed by the United States Department of Agriculture(USDA)(Arnold et al.,1998).The tool anticipates the effects of management practices on water,sediment,and agrochemicals in large,complex basins with changeable soils,land use types,and management conditions,over extended periods of time(Ficklin et al.,2009;Githui et al.,2009;Fontaine et al.,2001;Li et al.,2011;Neitsch et al.,2013).Tosimulatethephysicalprocess,ariverbasinmaybesplitintoa number of sub-basins.For each sub-basin,input data are organized into the following categories:climate,hydrological response units(HRUs),ponds/wetlands,groundwater,and the main channel or reach discharging to the sub-basin.

The water balance is the key element in SWAT no matter what case is studied in a catchment.Hydrological simulation for a catchment may be divided into two main phases:a land phase and a water or routing phase of the hydrologic cycle.In the land phase,the amounts of water,sediment,nutrients,and pesticides discharging into the main channel in each sub-basin are simulated.The routing phase can be outlined as the movement of water,sediment,nutrients,etc.to the outlet of the catchment through the channel network.

The hydrologic cycle in SWAT is based on the water balance equation:

where SWtis the final soil water content on day t(mm),SW0is the initial soil water content(mm),Riis the amount of precipitation on day i(mm),Qsiis the amount of surface runoff on day i(mm),Eiis the amount of evapotranspiration on day i(mm),Wiis the amount of water entering the vadose zone from the soil pro file on day i(mm),and Qgiis the amount of return flow on day i(mm).

The sub-division of the catchment allows the model to re flect the disparity of evapotranspiration(ET)from different crops and soils.Runoff of each HRU is anticipated exclusively and routed to obtain the total runoff for the catchment.This enhances the precision and provides a better physical description of the water balance.The model takes the values of daily atmospheric conditions as inputs from observation and simulation.From average monthly weather data,daily values are generated.The model produces a number of weather data for each sub-basin.

SWAT also runs with minimal data inputs,and is computationally pro ficient and hence capable of running simulations of large basins or management practices without wasting computational resources.The SWAT model has been linked with many systems,one of the flexible examples being its linkage with ArcView GIS software(ArcView GIS 10.1 with the ArcSWAT,2012 extension),which was used in this study.An inclusive interpretation of SWAT can be obtained from Neitsch et al.(2013).

In this study,possible effects of climate change on hydropower generation in the Rio Jubones Basin,in Ecuador,were investigated by analyzing outputs of the SWAT model.Information regarding climate change at different hydrometeorological stations was analyzed.The SWAT database was configured with a customized local dataset as it was initially designed for regions of the U.S.Afterwards,a SWAT model was developed and numerical simulation was performed based on the available hydro-meteorological inputs.The SWAT model was calibrated and validated using the sequential uncertainty fitting(SUFI-2)algorithm,with the uncertainty and sensitivity analysis conducted.Finally,a summary was made of the weather conditions and hydropower potentials of the study area.

2.Study area

The Rio Jubones Basin,in Ecuador,ranges from the latitudes of 3°4′S to 3°44′S and the longitudes of 78°57′W to 80°1′W,with an area of 4362 km2(Fig.1).It lies on the western slope in the Andes region,with an approximate channel length of 180 km.Rivers in the basin discharge into the Pacific Ocean.Most of the sub-basins in this drainage basin ultimately stream towards the Jubones River,which has a mean annual discharge of 48.3 m3/s at the planned location of the dam(Enerjubones,2014).Due to the presence of the Andean mountain range,trade winds and ocean currents move inward from the Pacific Ocean,and the climatic conditions are extremely variable.Weather situations also change rapidly.They are generally rough with chilly wind,snow,hail,rain,and fog.The climate of the Rio Jubones Basin ranges from humid to arid.From January to May,rain falls more often,and intensely,throughout the region.However,there is a marked decrease in rainfall during the period from June to December,resulting in a desert-type climate.Annual rainfall varies from about 925 mm to about 290 mm.Dense cloud banks are almost a daily occurrence at different elevations across the catchment.Depending on the locality and elevation,the mean annual temperature varies slightly from around 15°C-28°C.Annual average relative humidity fluctuates with elevation,from 70%to 88%,depending on the temperature.Monthly average potential evapotranspiration(PET)was found to be roughly 95 mm,and the annual PET fluctuates from about 900 mm to less than 500 mm.In this study,the upper part of the Rio Jubones Basin,shown in Fig.1,was taken as the study area.It should be mentioned that an outlet of the basin is in a close proximity to the dam under construction,through which stream flow from the entire basin passes.As the outlet was selected as the point of interest for numerical simulation during watershed delineation,the lower part of the basin would have no meaning in modeling,and hence,it was excluded in this study.

Agriculture is the prevailing activity in the Rio Jubones Basin.The diversified patterns of rainfall and temperature bring about a variety of tropical crops and fruits,such as corn,barley,beans,sugarcane,wheat,potato,banana,and cacao.Discrete land use patterns(Fig.2)exist in this basin,covered with shrubs,meadow,pasture,crops,grass,paramo vegetation,forests and woodlands,barren land,and built-up areas.Paramo,an alpine ecosystem found in Ecuador and other places,is the major water source in this region and covers around 24%of the total area.Heterogeneous soil classes are observed here;among them,inceptisol and entisol are the most dominant soil types(Fig.3).

3.Materials and methods

3.1.Materials and modeling software

Fig.2.Land use classes of study area.

Fig.3.Major soil classes of study area.

In this study,spatial,meteorological,and daily river flow data were prerequisites for developing a SWAT model for the Rio Jubones Basin.The daily time series of hydrometeorological data for the period of 17 years from 1982 to 1998 were taken into consideration for numerical simulation based on their availability.River flow data at station H529 were used for model calibration and validation.SWAT necessitates meteorological data,consisting of daily rainfall,air temperature(minimum and maximum),relative humidity,and wind speed,as weather inputs.In this study,wind speed,relative humidity,and solar radiation data were derived from the monthly statistics of weather information,since these variables were sporadic or not available.Seven rain gauge stations and three temperature stations(Table 1)were utilized to obtain the weather information.Spatial data involved a digital elevation model(DEM),soil map,and land cover map.Both meteorological and stream flow data were taken from the National Institute for Meteorology and Hydrology,in Ecuador,while spatial data came from Promas at the University of Cuenca, in Ecuador. For the spatial data, the WGS_1984_UTM_Zone_17S coordinate projection was used.Information regarding the hydropower plant was obtained from the Enerjubones,the state owned power generation holding company of Ecuador.The ArcView GIS 10.1 interface for ArcSWAT 2012 was chosen as a tool in this study.

Fig.4 presents the rainfall pattern of the selected stations in the study area for the observed baseline period(1982-1998),showing the same trend throughout the period.The rainfall distributions at stations M419,M142,and M032 show higher values of rainfall than other stations.These stations are positioned at higher elevations,and high mountains radically change the rainfall patterns naturally,due to the forcing of upright atmospheric motions.

It is observed that the maximum and minimum daily temperatures at the selected stations in the study area are higher at M032 and M196,as compared to M142(Table 2),since the two stations are located at relatively lower elevations and fall in the dry climatic zone.As a rule of thumb,temperature drops about 1°C for every 180 m of ascent.

Fig.5 presents the mean annual stream flow for the period from 1982 to 1998 at station H529.Stream flow shows an upward trend from 1982 to 1983,followed by a downward trend,and then remains almost constant during the rest of the measured period.The observed annual stream flow atthis station showed an average of 32.36 m3/s,with the minimum and maximum stream flow being 19 m3/s and 74 m3/s,respectively.

Table 1 Observed hydro-meteorological stations in study area.

Fig.4.Mean monthly distribution of measured rainfall at different rain gauge stations for period from 1982 to 1998.

3.2.Design specifications of hydropower plant

The hydropower generation is a function of the flow discharge,head,and density of water.The gross hydropower generation in watts can be calculated as follows:

where ρ is the density of water,with ρ =103kg/m3;g is the gravitational acceleration,with g=9.81 m/s2;Q is the flow discharge(m3/s);Δh is the difference in elevations between the water level at the intake of the dam and the outlet of the turbine(m);and η is the efficiency of turbines.The design specifications for the hydropower plant are listed in Table 3(Enerjubones,2014).

3.3.Generation of climate change scenarios

According to Houghton et al.(2001),it is recommended to use the GCM output data for at least a 30-year averaging period.However,this study had a record of only 17 years of climatic data,which limited the use of GCMs in the study area.Therefore,to determine the actual impact of climate change on the water resources of the study area,it is crucial to use a downscaling technique.To study the climate change impact on the hydropower generation in the Rio Jubones Basin,three climate change scenarios were determined basedon temperature and rainfallfrom the investigation by Mora et al.(2014)on the impact of climate change on the Paute River Basin in the southern Ecuadorian Andes,since the study area is close to the Paute River Basin.The temperature change was predicted to vary between 1.1°C and 2.9°C for the future period from 2045 to 2065,and the rainfall was predicted to vary from 5%to 15%for the same period as well.The assumed climate change scenarios are listed in Table 4.The basic assumption applied to rainfall was that the wet periods become wetter and the dry periods become drier.

Table 2 Statistics of daily temperature at selected stations in study area for period from 1982 to 1998.

Fig.5.Mean annual stream flow trend at station H529 for period from 1982 to 1998.

The wet season of this study area ranges from January to May,and the dry season ranges from June to December.The three scenarios were assumed for a future period without specifying an exact period.Subsequently,the scenarios were used as SWAT inputs for assessing the climate change impact on the hydrological extremes of the catchment.

3.4.Model configuration

SWAT database files were adjusted for the case of the Rio Jubones Basin,as application of SWAT to other areas demands customization with regard to the local conditions.To this end,a new database of soil type,crop,land use,and weather was created.The development of the model initially required automatic watershed delineation,HRUs,and weather inputs.

The study area was divided into sub-basins linked with the stream network,and smaller units were named as HRUs,signifying an arrangement of land use,soil,and slope.This enabled the SWAT model to re flect differences in hydrologicalconditions along with land use and soil type,and in turn,enhanced the precision of load predictions and gave a better physical description of the water balance(Neitsch et al.,2014).Considering the watershed area and the number of sub-basins generated in this study,a threshold value of 20%was determined for land use,which means that a certain land use occupying less than 20%of a particular sub-basin was excluded from modeling,and the areas of other land uses were proportionately increased to make up 100%of the sub-basin.Meanwhile,a threshold value of 10%was determined for both soil and slope,which means that soil types or slope categories occupying less than 10%of the area of a particular sub-basin were excluded from modeling,and the areas associated with the other soil types or other slope categories were proportionately increased so that they made up 100%of the land uses or soil types within the sub-basin area.For the given threshold values,a total of 132 HRUs were generated in 19 sub-basins.It is essential to keep in mind that the HRUs were not spatially adjacent and they had clustered response units.The pixels generating an HRU may be stretched throughout the sub-basin.Basic prerequisites for building the model were achieved by defining weather input data.Files including daily weather information at three stations(M196,M142,and M032)were specified in this model study.

Table 3 Basic design specifications of hydropower plant.

Table 4 Assumed climate change scenarios for future period(2045-2065).

The numerical simulation was performed for a period of 17 years from 1982 to 1998 with the SWAT model.The calibration and validation periods were determined to be 1985 to 1991 and 1992 to 1998,respectively,considering the availability of river flow data at the control point of the study area.The first three years(1982-1984)were considered a warm-up period,allowing the model to make the hydrologic cycle fully functional and stabilize some initial model parameters.Since the model did not perform well in daily simulations,monthly data were used in this study.Model performance was assessed with statistical indices and hydrograph shape during the calibration and validation periods.

3.5.Sensitivity analysis,calibration,and validation of SWAT model using SUFI-2 algorithm

Prior to the calibration,sensitivity analysis was executed for the control point to identify the most in fluential parameters.This also assists in saving time throughout calibration since it minimizes the number of parameters to be optimized in an over-parameterized SWAT model.It helps in recognizing the relative significance of different parameters.The SUFI-2 algorithm was used for parameter optimization,which was linked to the SWAT model by means of SWAT-CUP software(Abbaspour et al.,2007;Abbaspour,2007).

The efficacy of a hydrological model mainly depends on how well the model is calibrated(Gupta et al.,1999).The SWAT model can be calibrated manually and automatically or through a combination of the two methods(Boyle et al.,2000;B´ardossy,2007).In this study,automatic calibration was performed with SWAT-CUP software for the period from 1985 to 1991 at the outlet of the study area to avoid more time consumption in manual calibration(Eckhardt et al.,2005).

Determination of the most sensitive parameters for a watershed or sub-basin is the primary step in the calibration and validation processes of the SWAT model(Arnold et al.,2012).Over-parameterization of a complex model often leads to complications of parametric nonuniqueness and equifinality in hydrological models,particularly for distributed models,which may negatively impact prediction uncertainties(Schoups et al.,2008).Therefore,sensitivity analysis was conducted to lessen the number of parameters for efficient use of the model(van Griensven et al.,2006).

Model validation was accomplished with SWAT-CUP software for the period from 1992 to 1998 at the outlet of the study area.Throughout the validation,the model was run with the same model parameters that were obtained from the calibration period,to observe how well the calibrated parameters functioned in the validation period(Shrestha et al.,2010).

4.Results and discussion

4.1.Model calibration and validation

Parameter sensitivity analysis indicated that a channel is highly responsive to groundwater movement as well as surface and subsurface releases.The base flow recession factor(ALPHA_BF)and the delay time for aquifer recharge(GW_DELAY),which control the groundwater behavior,were found to be the most in fluential parameters for the sub-basin.This can be attributed to the fact that the hydrological regime of this region is highly in fluenced by a slow flow response(Buytaert et al.,2007).Since the area is dominated by lowpermeability layers,the in fluence of ALPHA_BF was anticipated.The slope of the sub-basin(SLSUBBSN)was found to be one of the geomorphological factors relating to the basin response behavior.The initial soil conservation service(SCS)curve number for moisture condition II(CN2),the soil evaporation compensation factor(ESCO),and the effective hydraulic conductivity of the channel(CH_K2)were found to affect the surface runoff and other characteristics of the basin.

Not all of the parameters recognized by sensitivity analysis were customized during the calibration process.In this study,only 14 parameters from the sensitivity list were used during the calibration process to fit the model as closely as possible to natural processes(Table 5).The ranges of parameter values in the calibration process were physically realistic(Eckhardt etal.,2005).Therefore,the modelcan be appliedsubsequently for appraising the impacts of climate change scenarios and/or management options.

Table 5 Initial and calibrated parameter values of SWAT model for study area.

ALPHA_BF waschanged to fine-tune the base flow recession curve.A higher ALPHA_BF value corresponds to a less steep base flow recession curve,demonstrating quick drainage and low storage.GW_DELAY was modified to regulate the flow timing in the subsurface.SLSUBBSN was considered a parameter,representing the consequences of a parallel terrace in an HRU.Lessening CN2 values resulted in declining runoff and increasing in filtration,base flow,and recharge.The depth distribution of ESCO was adjusted to facilitate the evaporation from the soil pro file.An enhanced SOL_AWC value led to the reduction in stream flow,because the ability of soil to hold water was improved.The saturated hydraulic conductivity(SOL_K),a measure of ease of water movement through the vadose zone,was adjusted to improve the subsurface flow response and to facilitate the groundwater storage capacity(GWQMN),which was also tuned.CH_K2,showing the flow movement from groundwater to rivers,was found to be a quite in fluential parameter.The precipitation laps rate(PLAPS)was used,because the study area was assumed to be affected by the extreme orographic effect(Emck,2007;Mu~noz et al.,2016).The effects of the other parameters on model outputs can be found in Neitsch et al.(2014).Model performance was evaluated based on some statistical indices(Table 6).

NSE is a reliable statistical index used widely for appraising the goodness offit of hydrological models(McCuen et al.,2006).It signifies deviations between observed and simulated values.NSE varies between-∞and 1.0,and the value of 1.0 is considered the optimal value.The simulated results are considered to be satisfactory if NSE is greater than 0.5 and good if NSE ranges from 0.65 to 0.75(Moriasi et al.,2007).R2expresses the strength of the linear relationship between simulated and measured values,with the value ranging from 0 to 1.A greater R2value signifies a better agreement between simulated and measured values,and usually values greater than 0.5 are considered to be satisfactory(Santhi et al.,2001;van Liew et al.,2003).The optimal value of PBIAS is 0.0%.A positive or negative PBIAS value indicates a model bias of underestimation or overestimation,respectively(Gupta et al.,1999).An absolute value of PBIAS of lower than 20%is considered to be good(van Liew et al.,2005).RSR values range from 0 to+∞.The lower the RSR is,the better the simulation performance is.NSE and R2values in Table 6 indicatethattherewasstrong agreementbetween the measured and simulated results for the calibration period,whereas relatively smaller values were obtained throughout the validation period.RSR and PBIAS values also demonstrated the strong performance of the model.However,the model showed poor performance in daily simulation.

The time sequences of the measured and simulated stream flow for the calibration and validation periods(Fig.6)were compared graphically.It can be concluded from Fig.6 that,generally,there is rational agreement between the measured and simulated hydrographs.The probable reason of model overestimation and underestimation of the stream flow might be ascribed to the fact that there are considerable uncertainties regarding the estimation of some SWAT parameters that control the flow through the deep and shallow aquifers simulated by the model.Another reason for this model behavior might be an inexact estimation of hydrological conditions in the soil pro files.

The underestimation of the stream flow during the wet period could be ascribed to the fact that macropore flow(through worm holes,root holes,etc.)was not simulated,though it might be a major part of the measured peaks.The overestimation of flow peaks during the wet period might beattributed to the facts that the curve number(CN)technique was not sufficient to produce a precise runoff prediction for a day experiencing several rainstorms,and the level of soil moisture and the resultant runoff curve differed from storm to storm.Lastly,the availability of weather inputs played a major role in model performance and accuracy.

Table 6 Statistical indices for model performance evaluation of calibration and validation processes for monthly discharge simulation.

Fig.6.Measured and simulated monthly average stream flow of study area.

4.2.Analysis of climate change scenarios

Themodelwasrunwitheachclimatechangescenarioandthe outputs of these three scenarios were analyzed to quantify the possible climate change impact on the hydropower generation.

In the future period,runoff will increase with precipitation in the wet season and the warming temperature.The rate of evaporation will also increase,which will consequently affect river flow.The increased flow will contribute to hydropower generation.During the dry season,the hydropower plant will experience a power shortage due to lack of stream flow.It is observed from Fig.7 that there is an evident increase in hydropower generation in the wet season for different scenarios,as compared with the reference period,and the hydropower generation steadily decreases in the dry period,due to the scarcity of rainfall.

It is evident from Table 7 that the average annual hydropower generation will increase in the future.The plant will also face a significant shortage of power in the dry season due to lack of rainfall.In the case of scenario 3,the overall annual hydropower generation is likely to increase by 7.88%from the reference scenario due to a 13%projected augmented stream flow in the wet season,and an estimated drop of about 17%in the stream flow during the dry season will lessen the hydropower generation up to 13.14%from the reference scenario.More repeated droughts might make the hydropower schemes unpro fitable and more extreme rainfall events will cause siltation to develop,which will consequently increase the risk of dam failures and catastrophic flood releases.Plants can generally be operated below their design limit.It is observed that the projected average annual hydropower generation is lower than the value presented in Table 3.This is ascribed to the turbine efficiency,generator efficiency,and other physical factors,which have not been incorporated in this study.

Fig.7.Comparison of predicted average monthly hydropower generation in assumed climate change scenarios with that in reference scenario.

Table 7 Predicted average annual hydropower generation for different scenarios and their deviations from reference scenario.

5.Conclusions

Application of the SWAT model in exploring the impact of climate change on future hydropower generation has been addressed and analyzed in this study.This paper gives an overview of a plausible scenario of future development of the hydroelectric output in the Rio Jubones Basin with consideration of hydro-climatic variabilities and different parameters.The results show that climate change can significantly alter the flow regime of the Jubones River,which can consequently affect the hydropower potential in the long run.The scenario study shows that the annual hydropower generation will likely be improved by around 8%as a result of stream flow augmented by 13%in the wet season due to a 15%increased rainfall.However,the plant will experience a considerable power drop,by up to 13.14%from the reference scenario during the dry season,owing to a 17%reduced stream flow with an increased temperature of 2.9°C under the impact of climate change.A substantial reduction of rainfall and increased temperature in the dry season can be the largest contributor to the decrease in annual precipitation as well as reduced stream flow in this area,which can be attributable to power shortage.

In this study,the SWAT model provided satisfactory results in simulating the hydrological processes according to the reference scenario,signifying that it can be applied to forecasting discharge in climate change scenarios.While SWAT has been applied effectively with the intention of attaining the goals of this study,some boundary conditions and theories of the model should be addressed to enhance the assessed results.On the whole,the study reveals that SWAT is competent in analyzing hydrological processes reasonably,and that the model can be utilized as a useful tool for predicting the impact of climate change scenarios.

This study provides insight into the hydrological response of the Jubones River Basin to various climate change scenarios,which is crucial for effective and sustainable water resources management.The predicted increase in river flow will offer an opportunity to conserve water in the wet season,which will be one of the most essential adaptations to the climate change.Watershed decision makers must have other operational policies that are adapted to new climatic and hydrological realities.The projected water resources could be considered in planning local water management in different sectors in the study region,especially irrigation,agriculture,and hydropower generation.This study shows that there is a need to improve the collection of hydro-climatic data,and also that the use of other watershed hydrological models,together with dynamically downscaled climatic inputs for hydropower generation in this region,needs to be addressed in separate studies.

Acknowledgements

The first author is grateful to the authority of Vlaamse Interuniversitaire Raad,Belgium,for providing him with the Flemish Interuniversity Council Scholarship to carry out this research.The authors would like to acknowledge the National Institute for Meteorology and Hydrology and Promas at the University of Cuenca,in Ecuador,for providing necessary data and information.