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

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bhabishya Khaniya ◽  
Chamaka Karunanayake ◽  
Miyuru B. Gunathilake ◽  
Upaka Rathnayake
Keyword(s):  
Power Generation ◽  
Sri Lanka ◽  
Climate Models ◽  
Regional Climate ◽  
Local Area ◽  
Hydropower Plant ◽  
Ann Model ◽  
Hydropower Generation ◽  
The Future ◽  
The Impact

The projection of future hydropower generation is extremely important for the sustainable development of any country, which utilizes hydropower as one of the major sources of energy to plan the country’s power management system. Hydropower generation, on the other hand, is mostly dependent on the weather and climate dynamics of the local area. In this paper, we aim to study the impact of climate change on the future performance of the Samanalawewa hydropower plant located in Sri Lanka using artificial neural networks (ANNs). ANNs are one of the most effective machine learning tools for examining nonlinear relationships between the variables to understand complex hydrological processes. Validated ANN model is used to project the future power generation from 2020 to 2050 using future projected rainfall data extracted from regional climate models. Results showcased that the forecasted hydropower would increase in significant percentages (7.29% and 10.22%) for the two tested climatic scenarios (RCP4.5 and RCP8.5). Therefore, this analysis showcases the capability of ANN in projecting nonstationary patterns of power generation from hydropower plants. The projected results are of utmost importance to stakeholders to manage reservoir operations while maximizing the productivity of the impounded water and thus, maximizing economic growth as well as social benefits.

Changes in extreme surface solar radiation in EURO-CORDEX Regional Climate Models

2021 ◽  
Author(s):  
Blanka Bartok
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
Solar Radiation ◽  
Solar Energy ◽  
Electricity Generation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Surface Solar Radiation ◽  
The Impact

<p>As solar energy share is showing a significant growth in the European electricity generation system, assessments regarding long-term variation of this variable related to climate change are becoming more and more relevant for this sector. Several studies analysed the impact of climate change on the solar energy sector in Europe (Jerez et al, 2015) finding light impact (-14%; +2%) in terms of mean surface solar radiation. The present study focuses on extreme values, namely on the distribution of low surface solar radiation (overcast situation) and high surface solar radiation (clear sky situation), since the frequencies of these situations have high impact on electricity generation.</p><p>The study considers 11 high-resolution (0.11 deg) bias-corrected climate projections from the EURO-CORDEX ensemble with 5 Global Climate Models (GCMs) downscaled by 6 Regional Climate Models (RCMs).</p><p>Changes in extreme surface solar radiation frequencies show different regional patterns over Europe.</p><p>The study also includes a case study determining the changes in solar power generation induced by the extreme situations.</p><p> </p><p> </p><p>Jerez et al (2015): The impact of climate change on photovoltaic power generation in Europe, Nature Communications 6(1):10014, 10.1038/ncomms10014</p><p> </p>

scholarly journals Climate change and hydrological analysis of Tekeze river basin Ethiopia: implication for potential hydropower production

2019 ◽  
Vol 11 (3) ◽  
pp. 744-759
Author(s):  
Abebe G. Adera ◽  
Knut T. Alfredsen
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Energy Production ◽  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Hydropower Plant ◽  
Operational Strategy ◽  
The Future ◽  
Hydropower Production

Abstract Climate change is expected to intensify the hydropower production in East Africa. This research investigates the runoff and energy production in the current and future climate for the Tekeze hydropower plant located in the Tekeze river basin in the northern part of Ethiopia. The rainfall-runoff model HBV and the hydropower simulator nMAG were used to generate runoff and energy production in the current and future climate. A combination of five regional climate models and seven global climate models from the Coordinated Regional Climate Downscaling Experiment were used to generate bias-corrected scenarios for the future climate. The result shows an increase in future runoff which was shown to be due to an increase in precipitation. However, the current operational strategy of the power plant did not utilize the future runoff in an optimal way. Therefore, based on the projected future inflow, we have developed a new reservoir operational strategy to preserve water for power production. As a result, the energy production was increased, and the flood spill from the reservoir reduced. This shows the need to adapt the hydropower production system to the future flow regimes to get the most out of the available water.

Statistical Emulators for Regional Climate Models: Preliminary results

2020 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Statistical Model ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Preliminary Results ◽  
Skill Scores ◽  
The Future ◽  
The Impact

<p><strong>Statistical Emulators for Regional Climate Models: Preliminary results</strong></p><p>Predicting some robust information on climate at some local geographical scale is of primary importance to assess the impact of the future climate change. But even more important is to quantify the whole range of uncertainties around the evolution of the climate that translates (i) the imperfections of the climate models, (ii) the natural variability variability and (iii) the uncertainties about the future human emissions of greenhouse gases. One of the nowadays tools used to produce future simulations at the local scale is the Regional Climate Models (RCM): they correspond to high resolution climate models used to downscale over a specific region the information simulated by a Global Climate Model (GCM) scenario simulation.</p><p>To cover the full range of uncertainties one should ideally force each RCM with every GCM under different emission scenarios and make several members. It comes down to filling up a huge 4D-matrix [Scenario, GCM, RCM, members]. However regarding the increasing number of climate models (regional and global) and the increasing cost of the RCMs due to their increased complexity and resolution, filling up such matrix becomes unrealistic.</p><p>To address this issue we propose a novel approach to merge statistical and dynamical downscaling techniques. The principle relies on three phases. Firstly, some RCM simulations are performed using the classical dynamical downscaling approach. Then, following the statistical downscaling principle, a statistical model is trained to learn the relationship between the large scale information given by the GCM and the local one produced by the RCM, using the runs previously performed. We call this statistical model an emulator. Finally this emulator allows to downscale more GCMs simulation, at a very reasonable cost in order to get a robust ensemble.</p><p>In this preliminary work we focus on emulating the surface temperature at the daily scale by testing different machine learning methods (RandomForest, Boosting, Neural Network) sometimes coupled with an <em>a-priori</em> signal decomposition. We train and test the emulator with simulations from the ALADIN RCM forced by the CNRM-CM5 GCM over the period 1950-2100. The different methods are discriminated over hidden simulations using skill scores measuring the match between the emulated series and the pseudo-reality RCM series. Day-to-day scores such as correlation or RMSE are used as well as statistical scores to control on the distribution of the predicted series.</p>

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals Impact of climate change on hydropower production in the Upper Danube

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Transfer Functions ◽  
Danube River ◽  
Emission Scenarios ◽  
Hydropower Generation ◽  
Climate Change Projections ◽  
Impact Of Climate Change ◽  
The Future ◽  
The Impact

<p>In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.</p><p>A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810<sup></sup>km<sup>2</sup> with a mean annual discharge of 1923 m<sup>3</sup>/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the “Green Deal” initiative.</p><p>Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.</p><p>The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.</p><p> </p><p> </p><p>References:</p><p>Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987–999. https://doi.org/10.1016/j.jhydrol.2018.06.057</p><p> </p>

scholarly journals Analysis of Small Hydropower Generation Potential : (2) Future Prospect of the Potential Under Climate Change

2021 ◽  
Author(s):  
Jaewon Jung ◽  
Sungeun Jung ◽  
Junhyeong Lee ◽  
Myungjin Lee ◽  
Hung Soo Kim
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
Renewable Energy ◽  
Future Prospect ◽  
High Energy ◽  
Future Climate Change ◽  
Small Rivers ◽  
Hydropower Generation ◽  
Small Hydropower ◽  
The Future

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change become more severe. Small hydropower (SHP) is evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. As the results, SHP potential for the near future (2021 to 2040) shows a tendency to be increased and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP in order to expand the use of renewable energy. Also, a methodology presented in this study could be used for the future prospect of the small hydropower potential.

Evaluating the effect of climate change on snow load on structures

2019 ◽  
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi ◽  
Francesca Marsili
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Extremes ◽  
Regional Climate Models ◽  
Extreme Weather Events ◽  
Uncertainty Range ◽  
Greenhouse Gasses ◽  
Weather Events ◽  
The Impact

<p>As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue for the resilience of infrastructures and buildings and is a key challenge for adaptation planning. In this paper, a suitable procedure for the estimation of future trends of climatic actions is presented starting from the output of regional climate models and taking into account the uncertainty in the model itself. In particular, the influence of climate change on ground snow loads is discussed in detail and the typical uncertainty range is determined applying an innovative algorithm for weather generation. Considering different greenhouse gasses emission scenarios, some results are presented for the Italian Mediterranean region proving the ability of the method to define factors of change for climate extremes also allowing a sound estimate of the uncertainty range associated with different models.</p>

Impact of aerosols on the future Euro-Mediterranean climate: results from the CORDEX FPS-Aerosol

2021 ◽  
Author(s):  
Pierre Nabat ◽  
Samuel Somot ◽  
Lola Corre ◽  
Eleni Katragkou ◽  
Shuping Li ◽  
...  
Keyword(s):  
Mediterranean Climate ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Surface Radiation ◽  
Climate Change Signal ◽  
Historical Period ◽  
Future Evolution ◽  
Aerosol Forcing ◽  
The Future

&lt;p&gt;The Euro-Mediterranean region is subject to numerous and various aerosol loads, which interact with radiation, clouds and atmospheric dynamics, with ensuing impact on regional climate. However up to now, aerosol variations are hardly taken into account in most regional climate simulations, although anthropogenic emissions have been dramatically reduced in Europe since the 1980s. Moreover, inconsistencies between regional climate models (RCMs) and their driving global model (GCM) have recently been identified in terms of future radiation and temperature evolution, which could be related to the differences in aerosol forcing.&amp;#160;&lt;br&gt;The present study aims at assessing the role of aerosols in the future evolution of the Euro-Mediterranean climate, using a specific multi-model protocol carried out in the Flagship Pilot Study &quot;Aerosol&quot; of the CORDEX program. This protocol relies on three simulations for each RCM: a historical run (1971-2000) and two future RCP8.5 simulations (2021-2050), a first one with evolving aerosols, and a second one with the same aerosols as in the historical period. Six modeling groups have taken part in this protocol, providing nine triplets of simulations. The analysis of these simulations will be presented here. First results show that the future evolution of aerosols has a significant impact on the evolution of surface radiation and surface temperature. In addition RCM runs taking into account the evolution of aerosols are simulating climate change signal closer to the one of their driving GCM than those with constant aerosols.&lt;/p&gt;

ANN-SCS-based hybrid model in conjunction with GCM to evaluate the impact of climate change on the flow scenario of the River Subansiri

2019 ◽  
Vol 11 (4) ◽  
pp. 1150-1164
Author(s):  
Swapnali Barman ◽  
Rajib Kumar Bhattacharjya
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Hybrid Model ◽  
Curve Number ◽  
Future Trend ◽  
Land Use Land Cover ◽  
Ann Model ◽  
Non Linear ◽  
The Future ◽  
The Impact

Abstract The River Subansiri, one of the largest tributaries of the Brahmaputra, makes a significant contribution towards the discharge at its confluence with the Brahmaputra. This study aims to investigate an appropriate model to predict the future flow scenario of the river Subansiri. Two models have been developed. The first model is an artificial neural network (ANN)-based rainfall-runoff model where rainfall has been considered as the input. The future rainfall of the basin is calculated using a multiple non-linear regression-based statistical downscaling technique. The proposed second model is a hybrid model developed using ANN and the Soil Conservation Service (SCS) curve number (CN) method. In this model, both rainfall and land use/land cover have been incorporated as the inputs. The ANN models were run using time series analysis and the method selected is the non-linear autoregressive model with exogenous inputs. Using Sen's slope values, the future trend of rainfall and runoff over the basin have been analyzed. The results showed that the hybrid model outperformed the simple ANN model. The ANN-SCS-based hybrid model has been run for different land use/land cover scenarios to study the future flow scenario of the River Subansiri.

scholarly journals The impact of RCM formulation and resolution on simulated precipitation in Africa

2019 ◽  
Author(s):  
Minchao Wu ◽  
Grigory Nikulin ◽  
Erik Kjellström ◽  
Danijel Belušić ◽  
Colin Jones ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Added Value ◽  
Primary Control ◽  
Model Formulation ◽  
Simulated Precipitation ◽  
Mean Climate ◽  
The Impact

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs – SMHI-RCA4 and HCLIM38-ALADIN are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100 and 200 km. Additionally to the two RCMs, two different configurations of the same RCA4 are used. Contrasting different RCMs, configurations and resolutions it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle is completely controlled by model formulation (convection scheme) while its amplitude is a function of resolution. Although higher resolution in many cases leads to smaller biases in the time mean climate, the impact of higher resolution is mixed. An improvement in one region/season (e.g. reduction of dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). The experiments confirm a pronounced and well known impact of higher resolution – a more realistic distribution of daily precipitation. Even if the time-mean climate is not always greatly sensitive to resolution, what the time-mean climate is made up of, higher order statistics, is sensitive. Therefore, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and in general cannot be considered as an added value of downscaling.

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