Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE
Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topog...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/158539 |
| _version_ | 1855531190471098368 |
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| author | De Sarkar, K. Ghosh, Surajit Bhattacharyya, S. Chowdhury, A. Holmatov, Bunyod |
| author_browse | Bhattacharyya, S. Chowdhury, A. De Sarkar, K. Ghosh, Surajit Holmatov, Bunyod |
| author_facet | De Sarkar, K. Ghosh, Surajit Bhattacharyya, S. Chowdhury, A. Holmatov, Bunyod |
| author_sort | De Sarkar, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topographic surveys, hydrological analyses, and environmental assessments with high-end instrument requirements. In a country like India, where the hydropower sector is mushrooming rapidly, implementing these techniques on such a large scale is challenging. Alternatively, cloud-based tools like Google Earth Engine (GEE), G-res, and Earth Observation (EO) data related to biophysical and climatic conditions with in-situ reservoir water levels provide an opportunity to quantify GHG emissions from LHRs efficiently. In the present study, Maithon, one of the oldest LHRs in India, situated in a tropical climatic zone, has been studied by integrating site-specific parameters to estimate GHG emissions. The results from this study, which show that at the mean operating level (146.31 m) of the reservoir, net GHG emission is 1,024 - 1,271 gCO2e/m2/yr (with a 95% confidence interval), are of significant importance. This study highlights the GHG emissions varying greatly between the full reservoir level (786 gCO2e/m2/yr) and near the dead storage level (3,855 gCO2e/m2/yr), indicating the role of reservoir operating level in mitigating GHG emissions to achieve global goals like net zero emissions. There has been limited work globally using the G-res tool, and this is the first comprehensive study of initial GHG emission estimation of a tropical reservoir using G-res and GEE incorporating updated high-resolution land use land cover and Sentinel-1 images. |
| format | Journal Article |
| id | CGSpace158539 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1585392025-12-08T09:54:28Z Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE De Sarkar, K. Ghosh, Surajit Bhattacharyya, S. Chowdhury, A. Holmatov, Bunyod greenhouse gas emissions estimation hydropower reservoirs water levels satellite imagery datasets land use land cover climate change rainfall Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topographic surveys, hydrological analyses, and environmental assessments with high-end instrument requirements. In a country like India, where the hydropower sector is mushrooming rapidly, implementing these techniques on such a large scale is challenging. Alternatively, cloud-based tools like Google Earth Engine (GEE), G-res, and Earth Observation (EO) data related to biophysical and climatic conditions with in-situ reservoir water levels provide an opportunity to quantify GHG emissions from LHRs efficiently. In the present study, Maithon, one of the oldest LHRs in India, situated in a tropical climatic zone, has been studied by integrating site-specific parameters to estimate GHG emissions. The results from this study, which show that at the mean operating level (146.31 m) of the reservoir, net GHG emission is 1,024 - 1,271 gCO2e/m2/yr (with a 95% confidence interval), are of significant importance. This study highlights the GHG emissions varying greatly between the full reservoir level (786 gCO2e/m2/yr) and near the dead storage level (3,855 gCO2e/m2/yr), indicating the role of reservoir operating level in mitigating GHG emissions to achieve global goals like net zero emissions. There has been limited work globally using the G-res tool, and this is the first comprehensive study of initial GHG emission estimation of a tropical reservoir using G-res and GEE incorporating updated high-resolution land use land cover and Sentinel-1 images. 2025-04 2024-11-05T11:58:27Z 2024-11-05T11:58:27Z Journal Article https://hdl.handle.net/10568/158539 en Limited Access Springer De Sarkar, K.; Ghosh, Surajit; Bhattacharyya, S.; Chowdhury, A.; Holmatov, Bunyod. 2025. Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE. Journal of the Indian Society of Remote Sensing, 53(4):1053-1064. [doi: https://doi.org/10.1007/s12524-024-02045-3] |
| spellingShingle | greenhouse gas emissions estimation hydropower reservoirs water levels satellite imagery datasets land use land cover climate change rainfall De Sarkar, K. Ghosh, Surajit Bhattacharyya, S. Chowdhury, A. Holmatov, Bunyod Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title | Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title_full | Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title_fullStr | Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title_full_unstemmed | Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title_short | Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE |
| title_sort | assessing ghg emissions of a tropical large hydropower reservoir using g res and gee |
| topic | greenhouse gas emissions estimation hydropower reservoirs water levels satellite imagery datasets land use land cover climate change rainfall |
| url | https://hdl.handle.net/10568/158539 |
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