Estimating greenhouse gas emissions from future Amazonian hydroelectric reservoirs
Brazil plans to meet the majority of its growing electricity demand with new hydropower plants located in the Amazon basin. However, large hydropower plants located in tropical forested regions may lead to significant carbon dioxide and methane emission. Currently, no predictive models exist to esti...
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Veröffentlicht in: | Environmental research letters 2015-12, Vol.10 (12), p.124019 |
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description | Brazil plans to meet the majority of its growing electricity demand with new hydropower plants located in the Amazon basin. However, large hydropower plants located in tropical forested regions may lead to significant carbon dioxide and methane emission. Currently, no predictive models exist to estimate the greenhouse gas emissions before the reservoir is built. This paper presents two different approaches to investigate the future carbon balance of eighteen new reservoirs in the Amazon. The first approach is based on a degradation model of flooded carbon stock, while the second approach is based on flux data measured in Amazonian rivers and reservoirs. The models rely on a Monte Carlo simulation framework to represent the balance of the greenhouse gases into the atmosphere that results when land and river are converted into a reservoir. Further, we investigate the role of the residence time stratification in the carbon emissions estimate. Our results imply that two factors contribute to reducing overall emissions from these reservoirs: high energy densities reservoirs, i.e., the ratio between the installed capacity and flooded area, and vegetation clearing. While the models' uncertainties are high, we show that a robust treatment of uncertainty can effectively indicate whether a reservoir in the Amazon will result in larger greenhouse gas emissions when compared to other electricity sources. |
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However, large hydropower plants located in tropical forested regions may lead to significant carbon dioxide and methane emission. Currently, no predictive models exist to estimate the greenhouse gas emissions before the reservoir is built. This paper presents two different approaches to investigate the future carbon balance of eighteen new reservoirs in the Amazon. The first approach is based on a degradation model of flooded carbon stock, while the second approach is based on flux data measured in Amazonian rivers and reservoirs. The models rely on a Monte Carlo simulation framework to represent the balance of the greenhouse gases into the atmosphere that results when land and river are converted into a reservoir. Further, we investigate the role of the residence time stratification in the carbon emissions estimate. Our results imply that two factors contribute to reducing overall emissions from these reservoirs: high energy densities reservoirs, i.e., the ratio between the installed capacity and flooded area, and vegetation clearing. While the models' uncertainties are high, we show that a robust treatment of uncertainty can effectively indicate whether a reservoir in the Amazon will result in larger greenhouse gas emissions when compared to other electricity sources.</description><identifier>ISSN: 1748-9326</identifier><identifier>EISSN: 1748-9326</identifier><identifier>DOI: 10.1088/1748-9326/10/12/124019</identifier><identifier>CODEN: ERLNAL</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Amazon ; Atmospheric models ; Carbon ; Carbon dioxide ; Electric power demand ; Electricity ; emission factor ; Emissions ; Greenhouse effect ; greenhouse gas emissions ; Greenhouse gases ; Hydroelectric plants ; Hydroelectric power ; hydropower ; Monte Carlo simulation ; Prediction models ; Reservoirs ; River basins ; Rivers ; Tropical forests ; Uncertainty</subject><ispartof>Environmental research letters, 2015-12, Vol.10 (12), p.124019</ispartof><rights>2015 IOP Publishing Ltd</rights><rights>2015. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-bb1bca97431e8ae401f4a2961aa3fd4e0d1d132f889f5f585e46202738258f0b3</citedby><cites>FETCH-LOGICAL-c526t-bb1bca97431e8ae401f4a2961aa3fd4e0d1d132f889f5f585e46202738258f0b3</cites><orcidid>0000-0002-4214-1106</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1748-9326/10/12/124019/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,780,784,864,2102,27924,27925,38868,38890,53840,53867</link.rule.ids></links><search><creatorcontrib>de Faria, Felipe A M</creatorcontrib><creatorcontrib>Jaramillo, Paulina</creatorcontrib><creatorcontrib>Sawakuchi, Henrique O</creatorcontrib><creatorcontrib>Richey, Jeffrey E</creatorcontrib><creatorcontrib>Barros, Nathan</creatorcontrib><title>Estimating greenhouse gas emissions from future Amazonian hydroelectric reservoirs</title><title>Environmental research letters</title><addtitle>ERL</addtitle><addtitle>Environ. Res. Lett</addtitle><description>Brazil plans to meet the majority of its growing electricity demand with new hydropower plants located in the Amazon basin. However, large hydropower plants located in tropical forested regions may lead to significant carbon dioxide and methane emission. Currently, no predictive models exist to estimate the greenhouse gas emissions before the reservoir is built. This paper presents two different approaches to investigate the future carbon balance of eighteen new reservoirs in the Amazon. The first approach is based on a degradation model of flooded carbon stock, while the second approach is based on flux data measured in Amazonian rivers and reservoirs. The models rely on a Monte Carlo simulation framework to represent the balance of the greenhouse gases into the atmosphere that results when land and river are converted into a reservoir. Further, we investigate the role of the residence time stratification in the carbon emissions estimate. Our results imply that two factors contribute to reducing overall emissions from these reservoirs: high energy densities reservoirs, i.e., the ratio between the installed capacity and flooded area, and vegetation clearing. While the models' uncertainties are high, we show that a robust treatment of uncertainty can effectively indicate whether a reservoir in the Amazon will result in larger greenhouse gas emissions when compared to other electricity sources.</description><subject>Amazon</subject><subject>Atmospheric models</subject><subject>Carbon</subject><subject>Carbon dioxide</subject><subject>Electric power demand</subject><subject>Electricity</subject><subject>emission factor</subject><subject>Emissions</subject><subject>Greenhouse effect</subject><subject>greenhouse gas emissions</subject><subject>Greenhouse gases</subject><subject>Hydroelectric plants</subject><subject>Hydroelectric power</subject><subject>hydropower</subject><subject>Monte Carlo simulation</subject><subject>Prediction models</subject><subject>Reservoirs</subject><subject>River basins</subject><subject>Rivers</subject><subject>Tropical forests</subject><subject>Uncertainty</subject><issn>1748-9326</issn><issn>1748-9326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqFUV1r3DAQNCGFXNL8hWDoS1-up9WX5ccQ0jQQCJT2WezJqzsdPusq2YXk10cXlzT0pSBYsTsz7OxU1RWwL8CMWUEjzbIVXK-ArYCXJxm0J9XibXD67n9Wnee8Y0xJ1ZhF9f02j2GPYxg29SYRDds4Zao3mGvah5xDHHLtU9zXfhqnRPX1Hp_jEHCot09ditSTG1NwdaJM6XcMKX-sPnjsM13-qRfVz6-3P26-LR8e7-5vrh-WTnE9LtdrWDtsGymADFLZ2UvkrQZE4TtJrIMOBPfGtF55ZRRJzRlvhOHKeLYWF9X9rNtF3NlDKjbSk40Y7Gsjpo3FNAbXk9UtNi2XrgMEScBRMBJNkffOdIyrovV51jqk-GuiPNpi3lHf40DlIBaaRmsplNQF-ukf6C5OaShOLVeKMWmAyYLSM8qlmHMi_7YgMHuMzR4TscdEXjvczrEVIp-JIR7-Kv-H9ALFWpkj</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>de Faria, Felipe A M</creator><creator>Jaramillo, Paulina</creator><creator>Sawakuchi, Henrique O</creator><creator>Richey, Jeffrey E</creator><creator>Barros, Nathan</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4214-1106</orcidid></search><sort><creationdate>20151201</creationdate><title>Estimating greenhouse gas emissions from future Amazonian hydroelectric reservoirs</title><author>de Faria, Felipe A M ; Jaramillo, Paulina ; Sawakuchi, Henrique O ; Richey, Jeffrey E ; Barros, Nathan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-bb1bca97431e8ae401f4a2961aa3fd4e0d1d132f889f5f585e46202738258f0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Amazon</topic><topic>Atmospheric models</topic><topic>Carbon</topic><topic>Carbon dioxide</topic><topic>Electric power demand</topic><topic>Electricity</topic><topic>emission factor</topic><topic>Emissions</topic><topic>Greenhouse effect</topic><topic>greenhouse gas emissions</topic><topic>Greenhouse gases</topic><topic>Hydroelectric plants</topic><topic>Hydroelectric power</topic><topic>hydropower</topic><topic>Monte Carlo simulation</topic><topic>Prediction models</topic><topic>Reservoirs</topic><topic>River basins</topic><topic>Rivers</topic><topic>Tropical forests</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Faria, Felipe A M</creatorcontrib><creatorcontrib>Jaramillo, Paulina</creatorcontrib><creatorcontrib>Sawakuchi, Henrique O</creatorcontrib><creatorcontrib>Richey, Jeffrey E</creatorcontrib><creatorcontrib>Barros, Nathan</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Environmental research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Faria, Felipe A M</au><au>Jaramillo, Paulina</au><au>Sawakuchi, Henrique O</au><au>Richey, Jeffrey E</au><au>Barros, Nathan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating greenhouse gas emissions from future Amazonian hydroelectric reservoirs</atitle><jtitle>Environmental research letters</jtitle><stitle>ERL</stitle><addtitle>Environ. Res. Lett</addtitle><date>2015-12-01</date><risdate>2015</risdate><volume>10</volume><issue>12</issue><spage>124019</spage><pages>124019-</pages><issn>1748-9326</issn><eissn>1748-9326</eissn><coden>ERLNAL</coden><abstract>Brazil plans to meet the majority of its growing electricity demand with new hydropower plants located in the Amazon basin. However, large hydropower plants located in tropical forested regions may lead to significant carbon dioxide and methane emission. Currently, no predictive models exist to estimate the greenhouse gas emissions before the reservoir is built. This paper presents two different approaches to investigate the future carbon balance of eighteen new reservoirs in the Amazon. The first approach is based on a degradation model of flooded carbon stock, while the second approach is based on flux data measured in Amazonian rivers and reservoirs. The models rely on a Monte Carlo simulation framework to represent the balance of the greenhouse gases into the atmosphere that results when land and river are converted into a reservoir. Further, we investigate the role of the residence time stratification in the carbon emissions estimate. Our results imply that two factors contribute to reducing overall emissions from these reservoirs: high energy densities reservoirs, i.e., the ratio between the installed capacity and flooded area, and vegetation clearing. While the models' uncertainties are high, we show that a robust treatment of uncertainty can effectively indicate whether a reservoir in the Amazon will result in larger greenhouse gas emissions when compared to other electricity sources.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1748-9326/10/12/124019</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4214-1106</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Amazon Atmospheric models Carbon Carbon dioxide Electric power demand Electricity emission factor Emissions Greenhouse effect greenhouse gas emissions Greenhouse gases Hydroelectric plants Hydroelectric power hydropower Monte Carlo simulation Prediction models Reservoirs River basins Rivers Tropical forests Uncertainty |
title | Estimating greenhouse gas emissions from future Amazonian hydroelectric reservoirs |
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