Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Khaniya, Bhabishya; Karunanayake, Chamaka; Gunathilake, Miyuru B.; Rathnayake, Upaka

doi:10.1155/2020/8862067

Cited by 23 publications

(8 citation statements)

References 60 publications

(42 reference statements)

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“…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][29][30].…”

Section: Introductionmentioning

confidence: 99%

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

Mirani

Ayele

Lohani

et al. 2022

Advances in Meteorology

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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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Section: Introductionmentioning

confidence: 99%

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

Mirani

Ayele

Lohani

et al. 2022

Advances in Meteorology

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“…Ratnayake et al (2010), De Silva et al (2012 and De Silva et al (2016) have projected future rainfall in Sri Lanka by WRF and SDSM models and drove in hydrologic and hydraulic models to forecast flood inundation. In addition to these studies, RCM projections in conjunction with Artificial Neural Networks (ANNs) have been used for rainfall forecasting (Nagahamulla et al 2011, Nagahamulla et al 2014, future hydropower projection (Khaniya et al 2020), future reservoir inflow prediction (Karunanayake et al 2020), etc. in Sri Lanka.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Statistical Downscaling Model (SDSM) projected future rainfall in Northwestern, Western and Southern provinces of Sri Lanka

et al. 2022

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Even though an extensive amount of climate change studies have been carried out in different parts of the world, Sri Lanka is one of the least focused countries in this regard. Climate projections are important and encouraged to manage the futuristic adverse impacts. Identifying this research gap, future rainfall projections were carried out in three provinces in Sri Lanka, i.e. Northwestern (Puttalam and Kurunegala), Western (Katunayake and Colombo), and Southern province (Galle and Hambantota). The Canadian Earth System Model (CanESM2) under the Representation Concentration Pathways (RCP8.5) was downscaled using the Statistical DownScaling Model (SDSM). Non-parametric tests, including Mann-Kendall (MK) test and the Sen's Slope estimator, were used to determine the significance of trends and magnitude of the slope of the historical trends (1990-2019) and future projected trends (2020-2100). The trends were analyzed for four major seasons in Sri Lanka, including First Inter-monsoon (FIM), Southwest monsoon (SWM), Second Intermonsoon (SIM), and the Northeast monsoon (NEM). The standard error and model bias at rainfall stations were 0.014-0.034 mm and 1-1.1 respectively, which are acceptable when compared to previous studies. Several significant rainfall trends were identified, including positive trends in the mid-future (2041-2070), and negative trends in the far-future (2071-2100). In addition, rainfall indices, including Rx5day, R20mm, Consecutive dry days (CDD), and Consecutive wet days (CWD) were tested in future projected and historical rainfalls. The results of the present study will be useful for policymakers for decision-making processes in water resources management and agriculture.

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“…At the same time, advancements of science and computer technology have aided the development of soft computing tools, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system, and support vector system [15] to simulate the streamflows using the available data. Among these soft computing tools, it is witnessed that there is a growing demand and use in ANNs for multiple applications, including streamflow estimations [3] and future hydropower generation estimates [16], in understanding relationships between future climate and crop yield [17,18].…”

Section: Introductionmentioning

confidence: 99%

Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka

Gunathilake

Karunanayake

Gunathilake³

et al. 2021

Applied Computational Intelligence and Soft Computing

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Accurate streamflow estimations are essential for planning and decision-making of many development activities related to water resources. Hydrological modelling is a frequently adopted and a matured technique to simulate streamflow compared to the data driven models such as artificial neural networks (ANNs). In addition, usage of ANNs is minimum to simulate streamflow in the context of Sri Lanka. Therefore, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis for Seethawaka River Basin located in the upstream part of the Kelani River Basin, Sri Lanka. The hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS), while the data-driven ANN model was developed in MATLAB. The rainfall and streamflows’ data for 2003–2010 period have been used. The simulations by HEC-HMS were performed by four types of input rainfall data configurations, including observed rainfall data sets and three satellite-based precipitation products (SbPPs), namely, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The ANN model was trained using three well-known training algorithms, namely, Levenberg–Marquadt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software.

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Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Cited by 23 publications

References 60 publications

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

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

Analysis of Statistical Downscaling Model (SDSM) projected future rainfall in Northwestern, Western and Southern provinces of Sri Lanka

Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka

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