Assessing Carbon Properties in Coastal Waters with a New Observing System Testbed

被引:0
|
作者
Clark, J. Blake [1 ]
Uz, Stephanie Schollaert [2 ]
Tsontos, Vardis [3 ]
Huang, Thomas [3 ]
Scott, Joel [4 ]
Rogers, Laura [5 ]
机构
[1] Univ Maryland, Baltimore Cty GESTAR 2, NASA, Goddard Space Flight Ctr, Greenbelt, MD 21250 USA
[2] NASA, Earth Sci Div, Goddard Space Flight Ctr, Greenbelt, MD USA
[3] CALTECH, NASA, Jet Prop Lab, Pasadena, CA USA
[4] NASA Headquarters, Ocean Biol & Biogeochem, Washington, DC USA
[5] NASA Headquarters, Earth Sci Div, Washington, DC USA
来源
2022 OCEANS HAMPTON ROADS | 2022年
关键词
land-ocean continuum; carbon cycle; distributed sensing; data fusion; satellite remote sensing; ALGORITHM; ESTUARINE;
D O I
10.1109/OCEANS47191.2022.9977358
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Large rainfall events over land can lead to a substantial flux of carbon and nutrients to estuaries and the coastal ocean. In the mid-Atlantic on the east coast of North America (35 degrees - 42 degrees N), these events often happen due to tropical storm activity as well as less predictable anomalously large midlatitude storms or abrupt spring snow melt runoff. Storms can directly impact the coastal carbon cycle via export of carbon from land to sea, while also stimulating phytoplankton production due to the influx of nutrients to the coastal ocean. To assess the impact of high precipitation and river flow events on the coastal carbon cycle, we have integrated multiple observing platforms in an analytical framework to dynamically observe carbon-related properties. The new observing system testbed (NOS-T) allows for an estimate of phytoplankton and organic carbon stocks in the surface ocean, with a goal of providing near real time analytical capability. A case study of high river discharge in the mid-Atlantic in the summer and fall of 2018 and 2021 were used to examine how riverine carbon manifests along the land-estuary-ocean continuum particularly in Chesapeake Bay. Chesapeake Bay was chosen as the study site because of long-term monitoring by the Chesapeake Bay Program and a robust and well-developed regional biogeochemical modeling system, the Chesapeake Bay Environmental Forecast System (CBEFS), that is publicly available. There is also new capability in Chesapeake Bay to combine hyperspectral radiometric data from the Aerosol Robotic Network site that is online in 2022 with in-water observations of optical properties. Precipitation data from the Global Precipitation Measurement IMERG data set was used to establish triggering criteria for storm carbon flux observation. USGS and in-water carbon data were used to establish statistical models to estimate the mass flux of organic carbon into Chesapeake Bay using measured river discharge. The year 2018 was examined as there was record rainfall and near record river flow in the late summer. The CBEFS modeling was compared with in-water estimates of dissolved organic carbon (DOC) to assess the model's ability to capture storm fluxes of carbon to the ecosystem. Multiple satellite platforms were also used to assess how remote sensing using passive Earth orbiting sensors can be used to observe carbon in this complex coastal region. A set of recommendations have been established to improve sensing capability to measure aquatic carbon during storm events. A unique challenge in these dynamic inland waters is how rapidly carbon fluxes can evolve in space and time, with many sources contributing to the water leaving reflectance that satellites can observe. To improve our ability to quantify carbon stocks and fluxes in near real time, a suite of satellite sensors and high-resolution modeling capability is needed, all supplemented by in-situ monitoring. Future inclusion of in-water observations that would be deployed when the system reaches triggering criteria, as well as taskable orbital instruments, will improve estimates of ocean carbon properties and provide the ability to calculate major carbon stocks and fluxes in near real time following episodic storm events.
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页数:6
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