Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios
Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-co...
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description | Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015–2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046–2075, but degenerates during 2015–2044 and 2076–2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change. |
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In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015–2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046–2075, but degenerates during 2015–2044 and 2076–2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.</description><identifier>ISSN: 1674-6767</identifier><identifier>EISSN: 2194-7783</identifier><identifier>DOI: 10.1007/s40333-022-0094-9</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Arid regions ; Arid zones ; Bias ; Climate change ; Disaggregation ; Earth and Environmental Science ; Geography ; Intercomparison ; Mountains ; Physical Geography ; Plant Ecology ; Research Article ; Semi arid areas ; Semiarid lands ; Sensitivity analysis ; Sustainable Development</subject><ispartof>Journal of arid land, 2022-05, Vol.14 (5), p.521-536</ispartof><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022</rights><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2574-bc5876cce44769f76fffa0c0b37dd9620a0f35864d33b6bb4ee6a3e3a79bdc743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/ghqkx/ghqkx.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40333-022-0094-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40333-022-0094-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Yao, Linlin</creatorcontrib><creatorcontrib>Zhou, Hongfei</creatorcontrib><creatorcontrib>Yan, Yingjie</creatorcontrib><creatorcontrib>Li, Lanhai</creatorcontrib><creatorcontrib>Su, Yuan</creatorcontrib><title>Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios</title><title>Journal of arid land</title><addtitle>J. Arid Land</addtitle><description>Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015–2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046–2075, but degenerates during 2015–2044 and 2076–2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.</description><subject>Arid regions</subject><subject>Arid zones</subject><subject>Bias</subject><subject>Climate change</subject><subject>Disaggregation</subject><subject>Earth and Environmental Science</subject><subject>Geography</subject><subject>Intercomparison</subject><subject>Mountains</subject><subject>Physical Geography</subject><subject>Plant Ecology</subject><subject>Research Article</subject><subject>Semi arid areas</subject><subject>Semiarid lands</subject><subject>Sensitivity analysis</subject><subject>Sustainable Development</subject><issn>1674-6767</issn><issn>2194-7783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLAzEUhYMoWGp_gLsBwd3ozWOSmWUZfEHBanUdMpmkndombdKi_femjuDKbBJuvnPu4SB0ieEGA4jbyIBSmgMhOUDF8uoEDQhODyFKeooGmAuWc8HFORrFuIR0eMkqhgfoZRr80uhd513mbbY4tMHvFias1SrT3rXdz0_nstq4XUjDcexUtnetCZn1-5DNZtP8tZ5mURunQufjBTqzahXN6Pceovf7u7f6MZ88PzzV40muSZHiNLooBdfaMCZ4ZQW31irQ0FDRthUnoMDSouSspbThTcOM4YoaqkTVtFowOkTXve-ncla5uVymOC5tlPPF9uOLpDagADiCVz24CX67N3H3RxIugBOOC5wo3FM6-BiDsXITurUKB4lBHluWfcsy-cpjy7JKGtJrYmLd3IQ_5_9F3-xafkc</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Yao, Linlin</creator><creator>Zhou, Hongfei</creator><creator>Yan, Yingjie</creator><creator>Li, Lanhai</creator><creator>Su, Yuan</creator><general>Science Press</general><general>Springer Nature B.V</general><general>Fukang National Field Scientific Observation and Research Station for Desert Ecosystems,Chinese Academy of Sciences,Fukang 831505,China</general><general>Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone,Urumqi 830011,China</general><general>University of Chinese Academy of Sciences,Beijing 100049,China</general><general>Fukang National Field Scientific Observation and Research Station for Desert Ecosystems,Chinese Academy of Sciences,Fukang 831505,China%State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China</general><general>State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China</general><general>University of Chinese Academy of Sciences,Beijing 100049,China%State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China</general><general>CAS Research Center for Ecology and Environment of Central Asia,Urumqi 830011,China</general><general>Ili Station for Watershed Ecosystem Research,Chinese Academy of Sciences,Xinyuan 835800,China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20220501</creationdate><title>Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios</title><author>Yao, Linlin ; Zhou, Hongfei ; Yan, Yingjie ; Li, Lanhai ; Su, Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2574-bc5876cce44769f76fffa0c0b37dd9620a0f35864d33b6bb4ee6a3e3a79bdc743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Arid regions</topic><topic>Arid zones</topic><topic>Bias</topic><topic>Climate change</topic><topic>Disaggregation</topic><topic>Earth and Environmental Science</topic><topic>Geography</topic><topic>Intercomparison</topic><topic>Mountains</topic><topic>Physical Geography</topic><topic>Plant Ecology</topic><topic>Research Article</topic><topic>Semi arid areas</topic><topic>Semiarid lands</topic><topic>Sensitivity analysis</topic><topic>Sustainable Development</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yao, Linlin</creatorcontrib><creatorcontrib>Zhou, Hongfei</creatorcontrib><creatorcontrib>Yan, Yingjie</creatorcontrib><creatorcontrib>Li, Lanhai</creatorcontrib><creatorcontrib>Su, Yuan</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Journal of arid land</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yao, Linlin</au><au>Zhou, Hongfei</au><au>Yan, Yingjie</au><au>Li, Lanhai</au><au>Su, Yuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios</atitle><jtitle>Journal of arid land</jtitle><stitle>J. Arid Land</stitle><date>2022-05-01</date><risdate>2022</risdate><volume>14</volume><issue>5</issue><spage>521</spage><epage>536</epage><pages>521-536</pages><issn>1674-6767</issn><eissn>2194-7783</eissn><abstract>Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015–2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046–2075, but degenerates during 2015–2044 and 2076–2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s40333-022-0094-9</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Arid regions Arid zones Bias Climate change Disaggregation Earth and Environmental Science Geography Intercomparison Mountains Physical Geography Plant Ecology Research Article Semi arid areas Semiarid lands Sensitivity analysis Sustainable Development |
title | Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios |
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