Loading…
The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018
Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the...
Saved in:
Published in: | Forests 2023-09, Vol.14 (9), p.1898 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3 |
---|---|
cites | cdi_FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3 |
container_end_page | |
container_issue | 9 |
container_start_page | 1898 |
container_title | Forests |
container_volume | 14 |
creator | Nie, Chong Chen, Xingan Xu, Rui Zhu, Yanzhong Deng, Chenning Yang, Queping |
description | Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000–2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 ± 472.4 g C m−2 yr−1 between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m−2 yr−1 per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 ± 13.86%, 58.87 ± 9.79%, and 27.07 ± 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB. |
doi_str_mv | 10.3390/f14091898 |
format | article |
fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_5d3de9cae0544061a908b67ad6807daa</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A771812642</galeid><doaj_id>oai_doaj_org_article_5d3de9cae0544061a908b67ad6807daa</doaj_id><sourcerecordid>A771812642</sourcerecordid><originalsourceid>FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3</originalsourceid><addsrcrecordid>eNpNkc1uEzEUhUcIJKrSBW9giRWLKf6ZsX2XJdASqRIVBCRW1h3_BEfJONhOJVjxDrwhT4JDUIW9sHXuuZ-PdbvuOaOXQgB9FdhAgWnQj7ozBgD9AFQ9_u_-tLsoZUPbGpUGPpx1dfXVk497rDH1K7_bp4xb8hlzPCpzISmQm7s7grMjy1rIYht3rWLJmxzv47wm12hryoXEmdRG-oLzuv7w5EO895m8xtJ0d8hHJ2-v_v75i1Omn3VPAm6Lv_h3nnefrt-uFu_62_c3y8XVbW8FqNpLpb0Xo3aDEkG4EKhtBcFFmCD40Q58tHLSdOLBOQmTBsknAD2iZMAnFOfd8sR1CTdmn1v2_N0kjOavkPLaYG6_2XozOuE8WPR0HAYqGQLVk1TopKbK4ZH14sTa5_Tt4Es1m3TIc4tvuJYtlVSCNdflybXGBo1zSDWjbdv5XbRp9iE2_UopphmXA28NL08NNqdSsg8PMRk1x6Gah6GKPz9Pkgc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2869326731</pqid></control><display><type>article</type><title>The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018</title><source>Publicly Available Content Database</source><creator>Nie, Chong ; Chen, Xingan ; Xu, Rui ; Zhu, Yanzhong ; Deng, Chenning ; Yang, Queping</creator><creatorcontrib>Nie, Chong ; Chen, Xingan ; Xu, Rui ; Zhu, Yanzhong ; Deng, Chenning ; Yang, Queping</creatorcontrib><description>Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000–2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 ± 472.4 g C m−2 yr−1 between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m−2 yr−1 per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 ± 13.86%, 58.87 ± 9.79%, and 27.07 ± 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB.</description><identifier>ISSN: 1999-4907</identifier><identifier>EISSN: 1999-4907</identifier><identifier>DOI: 10.3390/f14091898</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural production ; Aquatic ecosystems ; Carbon ; Climate ; climate change ; Datasets ; Drought ; Environmental aspects ; Environmental protection ; Forecasts and trends ; Forests ; Lake basins ; Lakes ; Least squares method ; Photosynthesis ; Precipitation ; Primary productivity (Biology) ; Productivity ; Radiation ; Remote sensing ; River basins ; River ecology ; River networks ; Rivers ; Short wave radiation ; Temporal variations ; Terrestrial ecosystems ; terrestrial gross primary productivity (GPP) ; Vegetation ; Water resources ; Water shortages ; Watersheds ; Yangtze River Basin</subject><ispartof>Forests, 2023-09, Vol.14 (9), p.1898</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3</citedby><cites>FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2869326731/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2869326731?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Nie, Chong</creatorcontrib><creatorcontrib>Chen, Xingan</creatorcontrib><creatorcontrib>Xu, Rui</creatorcontrib><creatorcontrib>Zhu, Yanzhong</creatorcontrib><creatorcontrib>Deng, Chenning</creatorcontrib><creatorcontrib>Yang, Queping</creatorcontrib><title>The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018</title><title>Forests</title><description>Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000–2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 ± 472.4 g C m−2 yr−1 between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m−2 yr−1 per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 ± 13.86%, 58.87 ± 9.79%, and 27.07 ± 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB.</description><subject>Agricultural production</subject><subject>Aquatic ecosystems</subject><subject>Carbon</subject><subject>Climate</subject><subject>climate change</subject><subject>Datasets</subject><subject>Drought</subject><subject>Environmental aspects</subject><subject>Environmental protection</subject><subject>Forecasts and trends</subject><subject>Forests</subject><subject>Lake basins</subject><subject>Lakes</subject><subject>Least squares method</subject><subject>Photosynthesis</subject><subject>Precipitation</subject><subject>Primary productivity (Biology)</subject><subject>Productivity</subject><subject>Radiation</subject><subject>Remote sensing</subject><subject>River basins</subject><subject>River ecology</subject><subject>River networks</subject><subject>Rivers</subject><subject>Short wave radiation</subject><subject>Temporal variations</subject><subject>Terrestrial ecosystems</subject><subject>terrestrial gross primary productivity (GPP)</subject><subject>Vegetation</subject><subject>Water resources</subject><subject>Water shortages</subject><subject>Watersheds</subject><subject>Yangtze River Basin</subject><issn>1999-4907</issn><issn>1999-4907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkc1uEzEUhUcIJKrSBW9giRWLKf6ZsX2XJdASqRIVBCRW1h3_BEfJONhOJVjxDrwhT4JDUIW9sHXuuZ-PdbvuOaOXQgB9FdhAgWnQj7ozBgD9AFQ9_u_-tLsoZUPbGpUGPpx1dfXVk497rDH1K7_bp4xb8hlzPCpzISmQm7s7grMjy1rIYht3rWLJmxzv47wm12hryoXEmdRG-oLzuv7w5EO895m8xtJ0d8hHJ2-v_v75i1Omn3VPAm6Lv_h3nnefrt-uFu_62_c3y8XVbW8FqNpLpb0Xo3aDEkG4EKhtBcFFmCD40Q58tHLSdOLBOQmTBsknAD2iZMAnFOfd8sR1CTdmn1v2_N0kjOavkPLaYG6_2XozOuE8WPR0HAYqGQLVk1TopKbK4ZH14sTa5_Tt4Es1m3TIc4tvuJYtlVSCNdflybXGBo1zSDWjbdv5XbRp9iE2_UopphmXA28NL08NNqdSsg8PMRk1x6Gah6GKPz9Pkgc</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Nie, Chong</creator><creator>Chen, Xingan</creator><creator>Xu, Rui</creator><creator>Zhu, Yanzhong</creator><creator>Deng, Chenning</creator><creator>Yang, Queping</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SN</scope><scope>7SS</scope><scope>7X2</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>M0K</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>DOA</scope></search><sort><creationdate>20230901</creationdate><title>The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018</title><author>Nie, Chong ; Chen, Xingan ; Xu, Rui ; Zhu, Yanzhong ; Deng, Chenning ; Yang, Queping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agricultural production</topic><topic>Aquatic ecosystems</topic><topic>Carbon</topic><topic>Climate</topic><topic>climate change</topic><topic>Datasets</topic><topic>Drought</topic><topic>Environmental aspects</topic><topic>Environmental protection</topic><topic>Forecasts and trends</topic><topic>Forests</topic><topic>Lake basins</topic><topic>Lakes</topic><topic>Least squares method</topic><topic>Photosynthesis</topic><topic>Precipitation</topic><topic>Primary productivity (Biology)</topic><topic>Productivity</topic><topic>Radiation</topic><topic>Remote sensing</topic><topic>River basins</topic><topic>River ecology</topic><topic>River networks</topic><topic>Rivers</topic><topic>Short wave radiation</topic><topic>Temporal variations</topic><topic>Terrestrial ecosystems</topic><topic>terrestrial gross primary productivity (GPP)</topic><topic>Vegetation</topic><topic>Water resources</topic><topic>Water shortages</topic><topic>Watersheds</topic><topic>Yangtze River Basin</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nie, Chong</creatorcontrib><creatorcontrib>Chen, Xingan</creatorcontrib><creatorcontrib>Xu, Rui</creatorcontrib><creatorcontrib>Zhu, Yanzhong</creatorcontrib><creatorcontrib>Deng, Chenning</creatorcontrib><creatorcontrib>Yang, Queping</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Agriculture Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content Database</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>Environmental Science Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Forests</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nie, Chong</au><au>Chen, Xingan</au><au>Xu, Rui</au><au>Zhu, Yanzhong</au><au>Deng, Chenning</au><au>Yang, Queping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018</atitle><jtitle>Forests</jtitle><date>2023-09-01</date><risdate>2023</risdate><volume>14</volume><issue>9</issue><spage>1898</spage><pages>1898-</pages><issn>1999-4907</issn><eissn>1999-4907</eissn><abstract>Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000–2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 ± 472.4 g C m−2 yr−1 between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m−2 yr−1 per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 ± 13.86%, 58.87 ± 9.79%, and 27.07 ± 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/f14091898</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1999-4907 |
ispartof | Forests, 2023-09, Vol.14 (9), p.1898 |
issn | 1999-4907 1999-4907 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_5d3de9cae0544061a908b67ad6807daa |
source | Publicly Available Content Database |
subjects | Agricultural production Aquatic ecosystems Carbon Climate climate change Datasets Drought Environmental aspects Environmental protection Forecasts and trends Forests Lake basins Lakes Least squares method Photosynthesis Precipitation Primary productivity (Biology) Productivity Radiation Remote sensing River basins River ecology River networks Rivers Short wave radiation Temporal variations Terrestrial ecosystems terrestrial gross primary productivity (GPP) Vegetation Water resources Water shortages Watersheds Yangtze River Basin |
title | The Spatio-Temporal Variations of GPP and Its Climatic Driving Factors in the Yangtze River Basin during 2000–2018 |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T22%3A04%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Spatio-Temporal%20Variations%20of%20GPP%20and%20Its%20Climatic%20Driving%20Factors%20in%20the%20Yangtze%20River%20Basin%20during%202000%E2%80%932018&rft.jtitle=Forests&rft.au=Nie,%20Chong&rft.date=2023-09-01&rft.volume=14&rft.issue=9&rft.spage=1898&rft.pages=1898-&rft.issn=1999-4907&rft.eissn=1999-4907&rft_id=info:doi/10.3390/f14091898&rft_dat=%3Cgale_doaj_%3EA771812642%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c397t-678ee358d473f3dff0cc39323fb9fe5c425c6b80b2fdd69b8962b9985a6192ba3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2869326731&rft_id=info:pmid/&rft_galeid=A771812642&rfr_iscdi=true |