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CoastWatch/OceanWatch satellite data and products support monitoring and forecasting applications as well as research studies across multiple disciplines including fisheries, ecology, and biological, chemical and physical oceanography.

The below posts highlight projects that use CoastWatch/OceanWatch data and products.




Identifying Climate-Driven Shifts in Jumbo Flying Squid Fishing Grounds - 07/17

Identifying Climate-Driven Shifts in Jumbo Flying Squid Fishing Grounds

The jumbo flying squid (Dosidicus gigas), also known as the Humbolt squid, is an economically important fisheries species in the Eastern Pacific, currently accounting for approximately one third of the world's squid landings.

A jumbo flying squid. Image courtesy of NOAA/MBARI
A jumbo flying squid. Image courtesy of NOAA/MBARI.

Understanding the impacts of climate and environmental variability on this commercial marine species is important for fisheries operations and fisheries management efforts. In order to determine the impact of climatic and oceanographic conditions on the spatial distribution of the jumbo flying squid fishery in the Southeast Pacific Ocean, Yu et al., (2017) mapped sea surface temperature and sea surface height data from NOAA OceanWatch Central Pacific node and chlorophyll-a data from the Asia-Pacific Data Research Center at the University of Hawaii with commercial fisheries data.

Spatial Distribution of catch per unit effort of jumbo flying squid
Spatial Distribution of catch per unit effort of jumbo flying squid superimposed on contour maps of sea surface temperature (SST top) and sea surface height (SSH bottom) from October to December in 2007 and 2009 (left two panels) and April to June in 2012 and 2013. Source: Yu et al., (2017).

The researchers analyzed the data to identify the environmental conditions that were most favorable for the squids and use this information to explain observed movements of the fishing ground.

    References and Related Reading

    •   Alabia, I. D., S. I. Saitoh, T. Hirawake, H. Igarashi, Y. Ishikawa, N. Usui, M. Kamachi, T. Awaji, and M. Seito. 2016. Elucidating the Potential Squid Habitat Responses in the Central North Pacific to the Recent ENSO Flavors. Hydrobiologia 772 (1): 215227.
    •   Arkhipkin, A.I., Rodhouse, P.G.K., Pierce, G.J., Sauer, W., Sakai, M., Allcock, L., Arguelles, J., Bower, J.R., Castillo, G., Ceriola, L., Chen,C-S., Chen, X., Diaz-Santana, M., Downey,N., González, A.F., Granados Amores, J., Green, C.P., Guerra, A., Hendrickson, L.C., Ibáñez, C., Ito, K., Jereb, P., Kato, Y., Katugin, O.N., Kawano, M., Kidokoro, H., Kulik, V.V., Laptikhovsky, V.V., Lipinski, M.R., Liu, B., Mariátegui, L., Marin, W., Medina, A., Miki, K., Miyahara, K., Moltschaniwskyj, N., Moustahfid, H., Nabhitabhata, J., Nanjo, N., Nigmatullin, Ch.M., Ohtani, T., Pecl, G., Perez, J.A.A., Piatkowski, U., Saikliang, P.j, Salinas-Zavala, C.A., Steer, M., Tian, Y., Ueta, Y., Vijai, D., Wakabayashi, T., Yamaguchi, T., Yamashiro, C., Yamashita, N. and Zeidberg, L.D. 2015. World squid fisheries. Reviews in Fisheries Science and Aquaculture 23: 92-252.
    •   Yu, W., Q. Yi, X. Chen, Y. Chen. 2017. Climate-driven latitudinal shift in fishing ground of jumbo flying squid (Dosidicus gigas) in the Southeast Pacific Ocean off Peru. International Journal of Remote Sensing 38 (12): 3531-3550.


Harmful Algal Bloom Monitoring and Forecasting in the Gulf of Mexico - 02/17

Harmful Algal Bloom Monitoring and Forecasting in the Gulf of Mexico - 02/17

Harmful algal blooms are a common occurrence in the Gulf of Mexico. Red tide blooms of the neurotoxin producing alga Karenia brevis are of particular concern. NOAA's National Ocean Service uses Coast Watch ocean color data along with cell counts and other environmental information to produce a Harmful Algal Blooms Observing System (HABSOS) and a Harmful Algal Bloom Operational Forecast System (HAB-OFS).

HABSOS is a combined data product distributed on an ArcGIS powered map. The system serves as a harmful algal bloom data resource for managers, scientists and the public. CoastWatch data available for visualization in HABSOS include chlorophyll-3 day composite data and chlorophyll anomaly data.

CoastWatch chlorophyll 3-day composite viewed on NOAA's HABSOS.
CoastWatch chlorophyll 3-day composite viewed on NOAA's HABSOS.

HAB-OFS produces bulletins and condition reports to inform Gulf of Mexico communities about Karenia brevis blooms. The bulletins provides information about Karenia brevis abundance and risk based on analysis of data including CoastWatch ocean color satellite imagery, field observations, buoy data, public health reports, models and forecasts. The condition reports provide a 3-4 day forecast of the potential levels of respiratory irritation from Karenia brevis blooms.

CoastWatch chlorophyll satellite image with possible K. brevis bloom areas shown by red polygon(s).
CoastWatch chlorophyll satellite image with possible K. brevis bloom areas shown by red polygon(s). Source: 30 November 2015 HAB-OFS Bulletin.

    References and Related Reading

    •   Cannizzaro, J., K. Carder, F. Chen, C. Heil, and G. Vargo. 2008. A novel technique for detection of the toxic dinoflagellate Karenia brevis in the Gulf of Mexico from remotely sensed ocean color data. Continental Shelf Research 28: 137-158.
    •   Kirkpatrick B., L.E. Fleming, D. Squicciarini, L.C. Backer, R. Clark., W. Abraham, J. Benson, Y.S. Cheng, D. Johnson, R. Pierce, J. Zaias, G.D. Bossart, and D.G. Baden. 2004. Literature review of Florida red tide: implications for human health effects. Harmful Algae 3: 99-115.
    •   Pierce, R. H., and M. S. Henry. 2008. Harmful algal toxins of the Florida red tide (Karenia brevis): Natural chemical stressors in South Florida coastal ecosystems. Ecotoxicology 17 (7): 623-631.
    •   Stumpf, R.P., M.E. Culver, P.A. Tester, M. Tomlinson, G.J. Kirkpatrick, B.A. Pederson, E. Truby, V. Ransibrahmanakul, and M. Soracco. 2003. Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae 2: 147-160.
    •   Stumpf, R.P., M.C. Tomlinson, J.A. Calkins, B. Kirkpatrick, K. Fisher, K. Nierenberg, R. Currier, and T.T. Wynne. 2009. Skill assessment for an operational algal bloom forecast system. Journal of Marine Systems 76: 151-161.
    •   Tomlinson, M.C., T.T. Wynne, and R.F. Stumpf. 2009. An evaluation of remote sensing techniques for enhanced detection of the toxic dinoflagellate Karenia brevis. Remote Sensing of the Environment 113: 598-609.
    •   Wynne, T.T., R.P. Stumpf, M.C. Tomlinson, V. Ransibrahmanakul, and T.A. Villareal. 2005. Detecting Karenia brevis blooms and algal resuspension in the western Gulf of Mexico with satellite ocean color imagery. Harmful Algae 4: 992-1003.


Juvenile Salmon Shark Habitat Use Research - 02/17

Juvenile Salmon Shark Habitat Use Research - 02/17

Salmon sharks are apex predators found in the North Pacific. Juvenile salmon sharks are known to utilize nursery areas in the North Pacific Transition Zone and the California Current System.

Salmon shark image courtesy of the Alaska Department of Fish and Game.
Salmon shark image courtesy of the Alaska Department of Fish and Game.

Standings of small juvenile salmon sharks have been reported between British Columbia, Canada and northern Baja California. A recent study used CoastWatch sea surface temperature data from the west coast regional node to explore the link between salmon shark strandings and water temperature. The study found that the probability of shark strandings was greatest when sharks were exposed to acute cold-water coastal upwelling events.

Salmon shark strandings in the California current overlaid on CoastWatch blended 8 day sea surface temperature.
Salmon shark strandings in the California current overlaid on CoastWatch blended 8 day sea surface temperature. Source: Carlisle et al., 2015.

    References and Related Reading

    •   Carlisle, A.B., S.Y. Litvin, E.L. Hazen, D.J. Madigan, K.J. Goldman, R.N. Lea, and B.A. Block. 2015. Reconstructing habitat use by juvenile salmon sharks links upwelling to strandings in the California Current. Marine Ecology Progress Series 525: 217-228. Cannizzaro, J., K. Carder, F. Chen, C. Heil, and G. Vargo. 2008.
    •   Compagno, L.J.V. 2001. Sharks of the world, Vol 2. Bullhead, mackerel and carpet sharks (Hetero dontiformes, Lamniformes and Orectolobiformes). FAO Species Catalogue for Fishery Purposes No. 1. FAO, Rome.
    •   Goldman, K.J., and J.A. Musick. 2006. Growth and maturity of salmon sharks (Lamna ditropis) in the eastern and western North Pacific, and comments on back-calculation methods. Fish Bulletin 104: 278-292.
    •   Schaffer P.A., B. Lifland, S. Van Sommeran, D.R. Casper, and C.R. Davis. 2013. Meningoencephalitis associated with Carnobacterium maltaromaticum-like bacteria in stranded juvenile salmon sharks (Lamna ditropis). Veterinary Pathology 50: 412-417.


TurtleWatch: a tool for reducing loggerhead turtle bycatch - 02/17

TurtleWatch: a tool for reducing loggerhead turtle bycatch - 02/17

Fisheries bycatch has been implicated as a contributing factor in the population decline of endangered Pacific loggerhead turtles. In order to reduce interactions between longline fishing vessels based in Hawaii and loggerhead sea turtles, the NOAA Pacific Islands Fisheries Science Center created an experimental information product called TurtleWatch.

A loggerhead turtle encountered by NOAA's Longline Observer Program.
A loggerhead turtle encountered by NOAA's Longline Observer Program. Image courtesy of NOAA Fisheries Pacific Islands Regional Office.

TurtleWatch provides fishers with information on the predicted location of water in the turtles' preferred temperature range (water cooler than 65.5°F and warmer than 63.5°F). Predicted temperatures for the product are calculated using sea surface temperature data and derived ocean current vectors from the OceanWatch Central Pacific node.

The TurtleWatch product is a map showing the predicted location of the loggerhead sea turtle's preferred temperature habitat.
The TurtleWatch product is a map showing the predicted location of the loggerhead sea turtle's preferred temperature habitat. Fishers are advised to fish in areas either warmer 65.5°F or cooler than 63.5°F. Grey arrows indicate the direction and strength of the average ocean currents over the most recent week of available data. Solid black lines mark the 63.5°F and 65.5°F temperature contours. The red area between these lines represents the region where more than 50% of loggerhead turtle interactions have occurred during the first quarter of the year.

    References and Related Reading

    •   Hatase, H., K. Goto, K. Sato, T. Bando, and Y. Matsuzawa. 2002. Using annual body size fluctuations to explore potential causes for the decline in a nesting population of the loggerhead turtle Caretta caretta at Senri Beach, Japan. Marine Ecology Progress Series 245:299-304.
    •   Hays, G.C., A.C. Broderick, B.J. Godley, P. Luschi, and W.J. Nichols. 2003. Satellite telemetry suggests high levels of fishing induced mortality in marine turtles. Marine Ecology Progress Series 262:305-309.
    •   Howell, E.A., A. Hooever, S.R. Benson, H. Bailey, J.J. Polovina, J.A. Seminoff, and P.H. Dutton. 2015. Enhancing the TurtleWatch product for leatherback sea turtles, a dynamic habitat model for ecosystem-based management. Fisheries Oceanography 24(1): 57-68.
    •   Howell, E.A., D.R. Kobayashi, D.M. Parker, G.H. Balazs, and J.J. Polovina. 2008. TurtleWatch: a tool to aid the bycatch reduction of loggerhead turtles Caretta caretta in the Hawaii-based pelagic longline fishery. Endangered Species Research 5: 267-278.
    •   Peckham, S., D. Diaz, A. Walli, G. Ruiz, L. Crowder, and W. Nicholas. 2007. Small-scale fisheries bycatch jeopardizes endangered Pacific loggerhead turtles. PLoS One 10:e1401.


Modeling Circulation in the Gulf of Maine - 02/17

Modeling Circulation in the Gulf of Maine - 02/17

The Gulf of Maine is a semi-enclosed sea in northeastern North America boarded by Nova Scotia in the northeast and Cape Code in the southwest. The variability of circulation in the Gulf of Maine drives many important ecological processes in the area.

VIIRS image of the Gulf of Maine on May 14, 2015
VIIRS image of the Gulf of Maine on May 14, 2015 . Image courtesy of NASA.

Modeling of ocean circulation is an important tool for understanding and predicting the circulation dynamics in this region. A recent study by Li et al. tested the effectiveness of assimilating temperature and salinity data into a Gulf of Maine model. Data assimilated into the model included the NOAA CoastWatch blended sea surface temperature product and in situ temperature and salinity profiles. By assimilating the data, the researchers were able to successfully hindcast a water mass anomaly that occurred in the Gulf of Maine in 2010.

Monthly averaged modeled surface temperatures in the Gulf of Maine for forward and posterior solutions of the data assimilative model.
Monthly averaged modeled surface temperatures in the Gulf of Maine for forward and posterior solutions of the data assimilative model. Source: Li et al., 2015.

    References and Related Reading

    •   Beardsley, R. C., B. Butman, W. R. Geyer, and P. Smith (1997), Physical oceanography of the Gulf of Maine: An update, in Proceedings of the Gulf of Maine Ecosystem Dynamics Scientific Symposium and Workshop, pp. 39-52, Reg. Assoc. for Res. in the Gulf of Maine, Hanover, N. H.
    •   Bigelow, H. B. (1927), Physical oceanography of the Gulf of Maine, Fish. Bull., 40, 511-1027.
    •   Brooks, D. A. (1994), A model study of the buoyancy-driven circulation in the Gulf of Maine, J. Phys. Oceanogr., 24(11), 2387-2412.
    •   Broquet, G., A. M. Moore, H. G. Arango, C. A. Edwards, and B. S. Powell (2009b), Ocean state and surface forcing correction using the ROMSIS4DVAR data assimilation system, Mercator Ocean Q. Newsl., 34, 5-13.
    •   Chen, K., R. He, B. Powell, G. Gawarkiewicz, A. M. Moore, and H. G. Arango (2014), Data assimilative modeling investigation on Gulf Stream Warm Core Ring interaction with continental shelf and slope circulation, J. Geophys. Res. Oceans, 119, 5968-5991, doi:10.1002/2014JC009898.
    •   Li, Y., H. Ruoying, K. Chen, D.J. McGillicuddy Jr. 2015. Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010. Journal of Geophysical Research: Oceans 120: 3522-3541.

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