Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios
This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is...
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| Veröffentlicht in: | Environmental modelling & software : with environment data news 2022-03, Vol.149, p.105307, Article 105307 |
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| creator | Zhao, Shangmin Cheng, Weiming Yuan, Yecheng Fan, Zemeng Zhang, Jin Zhou, Chenghu |
| description | This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is chosen and constructed to simulate and predict the global permafrost distributions. Thus, the global permafrost distributions at T1 (2010–2040), T2 (2040–2070) and T3 (2070–2100) are predicted under different climate scenarios (RCP26, RCP45 and RCP85). From T1 to T3, the area of global permafrost has the largest degradation under RCP85 scenarios. From RCP26 to RCP85 at T3, the area of the degraded permafrost reached 0.671 × 108 km2. The degraded permafrost mainly distributes in east Asia, west Asia, north Europe and north America. The west Asia has the highest degrading distance, about 600 km under the situations of both RCP85 from T1 to T3 and from RCP26 to RCP85 at T3.
•Global permafrost is simulated and predicted from 2010 to 2100.•Global permafrost degradation various largely under different climate scenarios.•West Asia has the highest degrading distance under RCP85 scenario. |
| doi_str_mv | 10.1016/j.envsoft.2022.105307 |
| format | Article |
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•Global permafrost is simulated and predicted from 2010 to 2100.•Global permafrost degradation various largely under different climate scenarios.•West Asia has the highest degrading distance under RCP85 scenario.</description><identifier>ISSN: 1364-8152</identifier><identifier>EISSN: 1873-6726</identifier><identifier>DOI: 10.1016/j.envsoft.2022.105307</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Climate prediction ; Climate scenarios ; Degradation ; Global permafrost distribution ; Land cover ; Land use ; Logistic regression model ; Permafrost ; Permafrost degradation ; Permafrost distribution simulation and prediction ; Regression models ; Simulation ; Topographic factors</subject><ispartof>Environmental modelling & software : with environment data news, 2022-03, Vol.149, p.105307, Article 105307</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Mar 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-f0cce19f0d340ed4539d8ed168becc54d68b5228d40d473211b192f3868aa1783</citedby><cites>FETCH-LOGICAL-c337t-f0cce19f0d340ed4539d8ed168becc54d68b5228d40d473211b192f3868aa1783</cites><orcidid>0000-0002-5197-0192</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.envsoft.2022.105307$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Zhao, Shangmin</creatorcontrib><creatorcontrib>Cheng, Weiming</creatorcontrib><creatorcontrib>Yuan, Yecheng</creatorcontrib><creatorcontrib>Fan, Zemeng</creatorcontrib><creatorcontrib>Zhang, Jin</creatorcontrib><creatorcontrib>Zhou, Chenghu</creatorcontrib><title>Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios</title><title>Environmental modelling & software : with environment data news</title><description>This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is chosen and constructed to simulate and predict the global permafrost distributions. Thus, the global permafrost distributions at T1 (2010–2040), T2 (2040–2070) and T3 (2070–2100) are predicted under different climate scenarios (RCP26, RCP45 and RCP85). From T1 to T3, the area of global permafrost has the largest degradation under RCP85 scenarios. From RCP26 to RCP85 at T3, the area of the degraded permafrost reached 0.671 × 108 km2. The degraded permafrost mainly distributes in east Asia, west Asia, north Europe and north America. The west Asia has the highest degrading distance, about 600 km under the situations of both RCP85 from T1 to T3 and from RCP26 to RCP85 at T3.
•Global permafrost is simulated and predicted from 2010 to 2100.•Global permafrost degradation various largely under different climate scenarios.•West Asia has the highest degrading distance under RCP85 scenario.</description><subject>Climate prediction</subject><subject>Climate scenarios</subject><subject>Degradation</subject><subject>Global permafrost distribution</subject><subject>Land cover</subject><subject>Land use</subject><subject>Logistic regression model</subject><subject>Permafrost</subject><subject>Permafrost degradation</subject><subject>Permafrost distribution simulation and prediction</subject><subject>Regression models</subject><subject>Simulation</subject><subject>Topographic factors</subject><issn>1364-8152</issn><issn>1873-6726</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LxDAQhosouK7-BCHguesk6edJZNFVWPCi55BNJpDSNjVJF_z3Zu3ePc073zNPlt1T2FCg1WO3wfEYnIkbBoylWMmhvshWtKl5XtWsukyaV0Xe0JJdZzchdACQdLHKxK53B9mTCf0gjXchkmCHuZfRupHIUZPJo7bqz035gTCgQKIjjAKQedToibbGoMcxEtXbQUYkQeEovXXhNrsysg94d7br7Ov15XP7lu8_du_b532uOK9jbkAppK0BzQtAXZS81Q1qWjUHVKosdBIlY40uQBc1Z5QeaMsMb6pGSlo3fJ09LHMn775nDFF0bvZjWilYxVvglFOWqsqlSqVPg0cjJp8O9j-CgjixFJ04sxQnlmJhmfqelj5MLxwtehGUxVElMh5VFNrZfyb8AgmFfxM</recordid><startdate>202203</startdate><enddate>202203</enddate><creator>Zhao, Shangmin</creator><creator>Cheng, Weiming</creator><creator>Yuan, Yecheng</creator><creator>Fan, Zemeng</creator><creator>Zhang, Jin</creator><creator>Zhou, Chenghu</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7SC</scope><scope>7ST</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-5197-0192</orcidid></search><sort><creationdate>202203</creationdate><title>Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios</title><author>Zhao, Shangmin ; Cheng, Weiming ; Yuan, Yecheng ; Fan, Zemeng ; Zhang, Jin ; Zhou, Chenghu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-f0cce19f0d340ed4539d8ed168becc54d68b5228d40d473211b192f3868aa1783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Climate prediction</topic><topic>Climate scenarios</topic><topic>Degradation</topic><topic>Global permafrost distribution</topic><topic>Land cover</topic><topic>Land use</topic><topic>Logistic regression model</topic><topic>Permafrost</topic><topic>Permafrost degradation</topic><topic>Permafrost distribution simulation and prediction</topic><topic>Regression models</topic><topic>Simulation</topic><topic>Topographic factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Shangmin</creatorcontrib><creatorcontrib>Cheng, Weiming</creatorcontrib><creatorcontrib>Yuan, Yecheng</creatorcontrib><creatorcontrib>Fan, Zemeng</creatorcontrib><creatorcontrib>Zhang, Jin</creatorcontrib><creatorcontrib>Zhou, Chenghu</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Computer and Information Systems Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Environment Abstracts</collection><jtitle>Environmental modelling & software : with environment data news</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Shangmin</au><au>Cheng, Weiming</au><au>Yuan, Yecheng</au><au>Fan, Zemeng</au><au>Zhang, Jin</au><au>Zhou, Chenghu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios</atitle><jtitle>Environmental modelling & software : with environment data news</jtitle><date>2022-03</date><risdate>2022</risdate><volume>149</volume><spage>105307</spage><pages>105307-</pages><artnum>105307</artnum><issn>1364-8152</issn><eissn>1873-6726</eissn><abstract>This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is chosen and constructed to simulate and predict the global permafrost distributions. Thus, the global permafrost distributions at T1 (2010–2040), T2 (2040–2070) and T3 (2070–2100) are predicted under different climate scenarios (RCP26, RCP45 and RCP85). From T1 to T3, the area of global permafrost has the largest degradation under RCP85 scenarios. From RCP26 to RCP85 at T3, the area of the degraded permafrost reached 0.671 × 108 km2. The degraded permafrost mainly distributes in east Asia, west Asia, north Europe and north America. The west Asia has the highest degrading distance, about 600 km under the situations of both RCP85 from T1 to T3 and from RCP26 to RCP85 at T3.
•Global permafrost is simulated and predicted from 2010 to 2100.•Global permafrost degradation various largely under different climate scenarios.•West Asia has the highest degrading distance under RCP85 scenario.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.envsoft.2022.105307</doi><orcidid>https://orcid.org/0000-0002-5197-0192</orcidid></addata></record> |
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| subjects | Climate prediction Climate scenarios Degradation Global permafrost distribution Land cover Land use Logistic regression model Permafrost Permafrost degradation Permafrost distribution simulation and prediction Regression models Simulation Topographic factors |
| title | Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios |
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