Estimation of Phytoplankton Primary Productivity in Qinghai Lake Using Ocean Color Satellite Data: Seasonal and Interannual Variations
Estimation of primary production in Qinghai Lake is crucial for the aquatic ecosystem management in the northeastern Qinghai–Tibet Plateau. This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qingha...
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Veröffentlicht in: | Water (Basel) 2024-05, Vol.16 (10), p.1433 |
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description | Estimation of primary production in Qinghai Lake is crucial for the aquatic ecosystem management in the northeastern Qinghai–Tibet Plateau. This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m2/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes. |
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This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m2/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w16101433</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aquatic ecosystems ; Biomass ; Chlorophyll ; Ecosystems ; Fisheries management ; Fishing ; Lakes ; Plankton ; Precipitation ; Productivity ; Remote sensing ; Rivers ; Time series ; Water quality ; Water temperature</subject><ispartof>Water (Basel), 2024-05, Vol.16 (10), p.1433</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 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/). 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This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m2/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes.</description><subject>Aquatic ecosystems</subject><subject>Biomass</subject><subject>Chlorophyll</subject><subject>Ecosystems</subject><subject>Fisheries management</subject><subject>Fishing</subject><subject>Lakes</subject><subject>Plankton</subject><subject>Precipitation</subject><subject>Productivity</subject><subject>Remote sensing</subject><subject>Rivers</subject><subject>Time series</subject><subject>Water quality</subject><subject>Water temperature</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUc1OwzAMrhBIIODAG0TixGGQ1GmyckPjb9IkQDCulZe6I1CSkWSgvQDPTWAIYR_89322ZRfFgeDHADU_-RBKcCEBNoqdkmsYSCnF5j9_u9iP8ZlnkfVwWPGd4vMiJvuKyXrHfMdun1bJL3p0LyknbkMuhVW2vl2aZN9tWjHr2J118ye0bIIvxKYxR-zGEDo28r0P7B4T9b1NxM4x4Sm7J4zeYc_QtWzsEgV0bpnjRwz2Z3TcK7Y67CPt_9rdYnp58TC6Hkxursajs8nAlLVIAyhBa1XNALREDZUkQ51WZYVDPdNiCIZrZYRqhWl5JbuSylmHJBVvFRDMYLc4XPddBP-2pJiaZ78MebfYAK9qXVdciow6XqPm2FNjXedTQJO1pVdrvKPO5vxZRisBSlSZcLQmmOBjDNQ1i_XpGsGb79c0f6-BL4ZdgQE</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Ban, Xuan</creator><creator>Dang, Yingchao</creator><creator>Shu, Peng</creator><creator>Qi, Hongfang</creator><creator>Luo, Ying</creator><creator>Xiao, Fei</creator><creator>Feng, Qi</creator><creator>Zhou, Yadong</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-8873-2585</orcidid></search><sort><creationdate>20240501</creationdate><title>Estimation of Phytoplankton Primary Productivity in Qinghai Lake Using Ocean Color Satellite Data: Seasonal and Interannual Variations</title><author>Ban, Xuan ; Dang, Yingchao ; Shu, Peng ; Qi, Hongfang ; Luo, Ying ; Xiao, Fei ; Feng, Qi ; Zhou, Yadong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-3237765b3374a7354ecef7625a87b7183c076c16d1cd054f2e2bfae460d63e3b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aquatic ecosystems</topic><topic>Biomass</topic><topic>Chlorophyll</topic><topic>Ecosystems</topic><topic>Fisheries management</topic><topic>Fishing</topic><topic>Lakes</topic><topic>Plankton</topic><topic>Precipitation</topic><topic>Productivity</topic><topic>Remote sensing</topic><topic>Rivers</topic><topic>Time series</topic><topic>Water quality</topic><topic>Water temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ban, Xuan</creatorcontrib><creatorcontrib>Dang, Yingchao</creatorcontrib><creatorcontrib>Shu, Peng</creatorcontrib><creatorcontrib>Qi, Hongfang</creatorcontrib><creatorcontrib>Luo, Ying</creatorcontrib><creatorcontrib>Xiao, Fei</creatorcontrib><creatorcontrib>Feng, Qi</creatorcontrib><creatorcontrib>Zhou, Yadong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</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><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ban, Xuan</au><au>Dang, Yingchao</au><au>Shu, Peng</au><au>Qi, Hongfang</au><au>Luo, Ying</au><au>Xiao, Fei</au><au>Feng, Qi</au><au>Zhou, Yadong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Phytoplankton Primary Productivity in Qinghai Lake Using Ocean Color Satellite Data: Seasonal and Interannual Variations</atitle><jtitle>Water (Basel)</jtitle><date>2024-05-01</date><risdate>2024</risdate><volume>16</volume><issue>10</issue><spage>1433</spage><pages>1433-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>Estimation of primary production in Qinghai Lake is crucial for the aquatic ecosystem management in the northeastern Qinghai–Tibet Plateau. This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m2/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w16101433</doi><orcidid>https://orcid.org/0000-0002-8873-2585</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aquatic ecosystems Biomass Chlorophyll Ecosystems Fisheries management Fishing Lakes Plankton Precipitation Productivity Remote sensing Rivers Time series Water quality Water temperature |
title | Estimation of Phytoplankton Primary Productivity in Qinghai Lake Using Ocean Color Satellite Data: Seasonal and Interannual Variations |
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