模拟印度沿海海洋生态系统动态的海洋生态系统模式评估

IF 1.7 3区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES
K. Chakraborty, A. Lotliker, G. Gupta, V. Narayanan Nampoothiri S., A. Paul, Jayashree Ghosh, Trishneeta Bhattacharya, S. K. Baliarsingh, A. Samanta
{"title":"模拟印度沿海海洋生态系统动态的海洋生态系统模式评估","authors":"K. Chakraborty, A. Lotliker, G. Gupta, V. Narayanan Nampoothiri S., A. Paul, Jayashree Ghosh, Trishneeta Bhattacharya, S. K. Baliarsingh, A. Samanta","doi":"10.1080/1755876X.2020.1843298","DOIUrl":null,"url":null,"abstract":"ABSTRACT This study describes an assessment of an ocean-ecosystem model in simulating marine ecosystem dynamics in the Indian coastal waters. Long-term sustained in-situ observations of temperature, salinity, chlorophyll-a and dissolved oxygen (DO) collected in the coastal waters of India, and ship-based observations are used for this assessment. The model captures observed trend of temperature, salinity and chlorophyll-a with high correlation in both eastern and western shelf waters except for salinity and DO along west and DO along east coast. The model performs very well in simulating the Indian coastal shelf ecosystem dynamics. The seasonal occurrence of prominent phytoplankton bloom in the central-east coast during pre-monsoon, south-west coast during monsoon and north-west coast during post-monsoon is realistically reproduced by the model. The model also reproduces the seasonal presence of significantly low DO content in the upwelled water at a shallow depth leading to coastal hypoxia in the south-west and central-east coast of India during summer monsoon. A fine-tuned model is useful in understanding better Indian coastal shelf ecosystem and predicting future changes in time and space, like the occurrence of coastal hypoxia or anticipating phytoplankton blooms. Such information is useful for predicting potential fishing grounds in coastal waters and fisheries management.","PeriodicalId":50105,"journal":{"name":"Journal of Operational Oceanography","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Assessment of an ocean-ecosystem model in simulating the Indian coastal marine ecosystem dynamics\",\"authors\":\"K. Chakraborty, A. Lotliker, G. Gupta, V. Narayanan Nampoothiri S., A. Paul, Jayashree Ghosh, Trishneeta Bhattacharya, S. K. Baliarsingh, A. Samanta\",\"doi\":\"10.1080/1755876X.2020.1843298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This study describes an assessment of an ocean-ecosystem model in simulating marine ecosystem dynamics in the Indian coastal waters. Long-term sustained in-situ observations of temperature, salinity, chlorophyll-a and dissolved oxygen (DO) collected in the coastal waters of India, and ship-based observations are used for this assessment. The model captures observed trend of temperature, salinity and chlorophyll-a with high correlation in both eastern and western shelf waters except for salinity and DO along west and DO along east coast. The model performs very well in simulating the Indian coastal shelf ecosystem dynamics. The seasonal occurrence of prominent phytoplankton bloom in the central-east coast during pre-monsoon, south-west coast during monsoon and north-west coast during post-monsoon is realistically reproduced by the model. The model also reproduces the seasonal presence of significantly low DO content in the upwelled water at a shallow depth leading to coastal hypoxia in the south-west and central-east coast of India during summer monsoon. A fine-tuned model is useful in understanding better Indian coastal shelf ecosystem and predicting future changes in time and space, like the occurrence of coastal hypoxia or anticipating phytoplankton blooms. Such information is useful for predicting potential fishing grounds in coastal waters and fisheries management.\",\"PeriodicalId\":50105,\"journal\":{\"name\":\"Journal of Operational Oceanography\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2020-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Operational Oceanography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/1755876X.2020.1843298\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Oceanography","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/1755876X.2020.1843298","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 1

摘要

摘要:本研究描述了一个海洋生态系统模型在模拟印度沿海水域海洋生态系统动态方面的评估。对印度沿海水域收集的温度、盐度、叶绿素-a和溶解氧(DO)进行了长期持续的原位观测,并利用船舶观测资料进行了评估。该模式捕获了东西部陆架水域除西海岸和东海岸盐度和DO外的温度、盐度和叶绿素-a的高相关性趋势。该模型能很好地模拟印度海岸陆架生态系统动态。在季风前的中东部海岸、季风期间的西南海岸和季风后的西北海岸,浮游植物大量繁殖的季节分布都得到了真实的再现。该模型还再现了夏季季风期间,浅层上升水中显著低DO含量的季节性存在,导致印度西南和中东部海岸的沿海缺氧。一个微调模型有助于更好地理解印度沿海大陆架生态系统,并预测未来的时间和空间变化,比如沿海缺氧的发生或预测浮游植物的大量繁殖。这些资料对于预测沿海水域的潜在渔场和渔业管理是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of an ocean-ecosystem model in simulating the Indian coastal marine ecosystem dynamics
ABSTRACT This study describes an assessment of an ocean-ecosystem model in simulating marine ecosystem dynamics in the Indian coastal waters. Long-term sustained in-situ observations of temperature, salinity, chlorophyll-a and dissolved oxygen (DO) collected in the coastal waters of India, and ship-based observations are used for this assessment. The model captures observed trend of temperature, salinity and chlorophyll-a with high correlation in both eastern and western shelf waters except for salinity and DO along west and DO along east coast. The model performs very well in simulating the Indian coastal shelf ecosystem dynamics. The seasonal occurrence of prominent phytoplankton bloom in the central-east coast during pre-monsoon, south-west coast during monsoon and north-west coast during post-monsoon is realistically reproduced by the model. The model also reproduces the seasonal presence of significantly low DO content in the upwelled water at a shallow depth leading to coastal hypoxia in the south-west and central-east coast of India during summer monsoon. A fine-tuned model is useful in understanding better Indian coastal shelf ecosystem and predicting future changes in time and space, like the occurrence of coastal hypoxia or anticipating phytoplankton blooms. Such information is useful for predicting potential fishing grounds in coastal waters and fisheries management.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.50
自引率
9.70%
发文量
8
审稿时长
>12 weeks
期刊介绍: The Journal of Operational Oceanography will publish papers which examine the role of oceanography in contributing to the fields of: Numerical Weather Prediction; Development of Climatologies; Implications of Ocean Change; Ocean and Climate Forecasting; Ocean Observing Technologies; Eutrophication; Climate Assessment; Shoreline Change; Marine and Sea State Prediction; Model Development and Validation; Coastal Flooding; Reducing Public Health Risks; Short-Range Ocean Forecasting; Forces on Structures; Ocean Policy; Protecting and Restoring Ecosystem health; Controlling and Mitigating Natural Hazards; Safe and Efficient Marine Operations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信