Satellite observations estimating the effects of river discharge and wind‐driven upwelling on phytoplankton dynamics in the Chesapeake Bay

Satellite observations estimating the effects of river discharge and wind‐driven upwelling on phytoplankton dynamics in the Chesapeake Bay
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卫星观测估计河流流量和风驱动的上升流对切萨皮克湾浮游植物动态的影响

DOI:
10.1002/ieam.4597
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发表时间:
2022
影响因子:
3.1
通讯作者:
DiGiacomo, Paul M.
DiGiacomo, Paul M.
中科院分区:
环境科学与生态学4区
文献类型:
--
作者:
Nezlin, Nikolay P.;Testa, Jeremy M.;Zheng, Guangming;DiGiacomo, Paul M.

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河口浮游植物的生长受到多种物理因素的复杂组合的调节,其中淡水流量通常在控制营养盐和光的可用性方面发挥主导作用。其他因素的作用,包括产生上升流的风,仍然不清楚,因为大多数河口太小,无法出现上升流。在这项研究中,我们使用遥感代理浮游植物生物量和悬浮矿物颗粒的浓度比较河流排放的影响与持续的沿着通道南风在切萨皮克湾,一个大的河口,上升流及其对地球化学动力学的影响已被报道的上升流事件的影响。地表叶绿素浓度(Chl‐a)是根据可见光红外成像辐射计套件(VIIRS)卫星数据,使用广义叠加约束模型(GSCM)通过比较遥感和实地测量数据校正季节效应来估计的。浮游植物生长的光限制是根据悬浮矿物质颗粒的浓度评估的,悬浮矿物质颗粒的浓度是根据遥感的蓝色(443 nm)波长的后向散射估算的bbp(443)。Chl‐aandbbp(443)的九年时间序列(2012-2020)证实,调节这一近岸富营养化区域浮游植物生长的主要因素是萨斯奎汉纳河的排放,以及它提供的营养物质,时滞长达四个月。持续的南风事件(2-3天,风速>4 m/s)影响了海湾中部的水柱分层,但没有导致遥感叶绿素a的显着增加。对选定的上升流有利风事件的模型模拟分析表明,强烈的南风导致了明确的横向(东西)响应,但不足以将高营养水输送到表层以支持浮游植物水华。我们的结论是,在切萨皮克湾,这是一个大型的富营养化河口,与河流排放相比,在大多数条件下,风力驱动的深水上升流在推动浮游植物生长方面发挥的作用有限。Integr Environ ManagAssess 2022;18:921-938。© 2022 SETACKEY POINTS河流流量是调节切萨皮克湾浮游植物生长的主要因素。产生上升流的风事件不足以支持浮游植物水华。广义堆叠约束模型(GSCM)是处理近岸海域海洋水色卫星图像的有用方法。
Phytoplankton growth in estuaries is regulated by a complex combination of physical factors with freshwater discharge usually playing a dominating role controlling nutrient and light availability. The role of other factors, including upwelling‐generating winds, is still unclear because most estuaries are too small for upwelling to emerge. In this study, we used remotely sensed proxies of phytoplankton biomass and concentration of suspended mineral particles to compare the effect of river discharge with the effect of upwelling events associated with persistent along‐channel southerly winds in the Chesapeake Bay, a large estuary where upwelling and its effects on biogeochemical dynamics have been previously reported. The surface chlorophyll‐aconcentrations (Chl‐a) were estimated from Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data using the Generalized Stacked‐Constraints Model (GSCM) corrected for seasonal effects by comparing remotely sensed and field‐measured data. Light limitation of phytoplankton growth was assessed from the concentration of suspended mineral particles estimated from the remotely sensed backscattering at blue (443 nm) wavelengthbbp(443). The nine‐year time series (2012–2020) of Chl‐aandbbp(443) confirmed that a primary factor regulating phytoplankton growth in this nearshore eutrophic area is discharge from the Susquehanna River, and presumably the nutrients it delivers, with a time lag up to four months. Persistent southerly wind events (2–3 days with wind speed >4 m/s) affected the water column stratification in the central part of the bay but did not result in significant increases in remotely sensed Chl‐a. Analysis of model simulations of selected upwelling‐favorable wind events revealed that strong southerly winds resulted in well‐defined lateral (East–West) responses but were insufficient to deliver high‐nutrient water to the surface layer to support phytoplankton bloom. We conclude that, in the Chesapeake Bay, which is a large, eutrophic estuary, wind‐driven upwelling of deep water plays a limited role in driving phytoplankton growth under most conditions compared with river discharge.Integr Environ Assess Manag2022;18:921–938. © 2022 SETACKEY POINTSRiver discharge is a primary factor regulating phytoplankton growth in the Chesapeake Bay.Upwelling‐generating wind events were insufficient to support phytoplankton blooms.Generalized Stacked‐Constraints Model (GSCM) is a useful method for processing ocean color satellite imagery in the nearshore areas.
切萨皮克湾初级生产的季节到年际变化:扭转富营养化和改变营养分类的前景
DOI: 10.1038/s41598-020-58702-3
发表时间: 2020
期刊: Scientific Reports
影响因子: 4.6
作者:
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DOI: 10.1007/s00227-017-3126-9
发表时间: 2017
期刊: Marine biology
影响因子: 2.4
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通讯作者: Santos MD
DOI: 10.1002/2015jc011191
发表时间: 2016-02
影响因子: --
作者:
Long Jiang;Meng Xia
通讯作者: Long Jiang;Meng Xia
DOI: 10.1007/s12237-008-9128-6
发表时间: 2009-03-01
影响因子: 2.7
作者:
Brown, Cheryl A.;Ozretich, Robert J.
通讯作者: Ozretich, Robert J.
DOI: 10.1016/j.ecolmodel.2017.08.026
发表时间: 2017-11
影响因子: 3.1
作者:
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通讯作者: Long Jiang;Meng Xia