Assessment of the impact of climate change on hydropower potential in the Nanliujiang River basin of China

Wang, Hejia; Xiao, Wang; Wang, Yicheng; Zhao, Yong; Lü, Fan; Yang, Mingzhi; Hou, Baodeng; Yang, Heng

doi:10.1016/j.energy.2018.10.159

Cited by 31 publications

(20 citation statements)

References 29 publications

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“…For example, the researcher Wang Hejia to accurately assess the impacts on potential hydroelectric, mentions that a quantitative approach that combines climatology, hydrology, econometrics, and operational research models should be implemented for the future [50].…”

Section: Byman Hamududumentioning

confidence: 99%

Hydropower: Projections in a changing climate and impacts by this "clean" source

Naranjo-Silva

Castillo

2021

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INTRODUCTION: Hydropower is an extensively used renewable source; in 2016, 159 countries reported benefiting; currently, there are around 9,000 projects in operation due to the competitive cost of generating a similar cost such as thermal energy such as coal, oil, or gas in the range of USD 4 - 5 cents US dollars per kilowatt-hour. OBJECTIVE: Investigate the results of hydroelectric development in the face of the changing climate and the generated impacts, making hydropower a subsector of special attention to discussing the global projection. METHOD: Bibliographic review to reflect on the global context of hydroelectricity based on scientific studies. RESULTS: Hydropower projects a 6% decrease for Europe by 2070, from 20% to 50% throughout the Mediterranean, and a reduction in usable capacity in most hydroelectric plants between 61% for the 2040 scenario– 2069 globally. DISCUSSION AND CONCLUSIONS: Globally, hydropower presents a broad vision of the advantages, and little said about the disadvantages and problems, and only there are specific studies that shown various project studied in a general way. It is shown that hydroelectric production has several implications in the face of the changing climate and impacts generated in ecosystems by the deployment of large infrastructures.

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Section: Byman Hamududumentioning

confidence: 99%

Hydropower: Projections in a changing climate and impacts by this "clean" source

Naranjo-Silva

Castillo

2021

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“…In another research, Naz et al (2018) applied the MEP for several models to implement 10 GCMs in the United States (15). Wang et al (2019) evaluated the effect of climate change on temperature and precipitation. The results indicated that the MEP of a number of models could reduce the uncertainty of GCMs.…”

Section: Figure 1 Location Of Hamedan Province In the Regionmentioning

confidence: 99%

Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran

Ahmadi

Ghasem

Bazgir

et al. 2021

jorar

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INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.

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“…Liu et al (2016) applied eight global hydrological models to project the impact of climate change on the hydropower potential in China [27]. Kim et al (2018) and Wang et al (2019) used a grid-based surface runoff model and variable infiltration capacity model to study the impact of climate change on the future runoff and SHP potential [6,28]. Van Vliet et al (2016) pointed out the limitations of previous studies that used only one hydrological Energies 2021, 14, 3643 2 of 10 model and then used an ensemble of results from three global hydrological models [8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

et al. 2021

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As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

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Assessment of the impact of climate change on hydropower potential in the Nanliujiang River basin of China

Cited by 31 publications

References 29 publications

Hydropower: Projections in a changing climate and impacts by this "clean" source

Hydropower: Projections in a changing climate and impacts by this "clean" source

Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

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