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CCE

 
Funded Research

Decision and Information System for the Coastal Waters of Oman (DISCO)- An Integrative Tool for Managing Coastal Resources Under Changing Climate

Goés, Joaquim: Lamont-Doherty Earth Observatory (Project Lead)

Project Funding: 2017 - 2021

NRA: 2016 NASA: Ecological Forecasting   

Funded by NASA

Abstract:
Over the past 15 years the Arabian Sea ecosystem has witnessed a radical shift in the composition of winter phytoplankton blooms due to the warming of the Eurasian continent and the spread of hypoxia. Recurrent and trophically important, winter diatom blooms until 1990s, have since been replaced by widespread blooms of a large, green mixtrophic dinoflagellate Noctiluca scintillans (Noctiluca),a mixotrophic organism that is capable of combining carbon fixation by its chlorophyll (Chl a) containing endosymbiont, with aggressive ingestion of diatoms, smaller zooplankton as well as fish eggs. Noctiluca is not a preferred food for micro- and mesozooplankton. Instead its major consumers are mostly salps and jellyfish that are not members of the marine food chain that support commercially important pelagic fishes. In recent years, intense blooms of Noctiluca have become a recurrent feature off the coast of Oman often forcing closures of coastal aquaculture farms, desalination plants, refineries, etc. Their occurrence during periods that matter most to coastal fisheries and tourism is placing huge demands on public health and resource management agencies to develop early warning systems that reduce their socio-economic impacts. Fishing is one of the main activities of many coastal communities and Oman is one of the largest fish producers in the region. In this study we will partner with scientists from the Ministry of Agriculture and Fisheries Wealth, the Marine Sciences and Fisheries Centre and from Sultan Qaboos University to build upon our previous NASA funded efforts and develop Decision and Information System for the Coastal waters of Oman (DISCO)”, a system that will provide decision makers and stake holders in Oman with the tools necessary to manage coastal resources. This effort will make use of remote sensing and modeling tools that were developed as part of our previously NASA funded studies. We will enhance their capabilities and customize them specifically for resource management and conservation and environmental stewardship of the coastal waters of Oman. We will develop DISCO during the first year of the project and then transition it to the Ministry of Agriculture and Fisheries Wealth, so that it can be used to provide a continuous stream of satellite imagery, outputs of physical and biological oceanographic data from our 3-D coupled ocean circulation – biogeochemical model. These data streams will allow for assessments of ecosystem state, provide timely information on Noctiluca blooms with adequate lead times and guide realistic mitigation strategies that minimize the risks of Noctiluca blooms on coastal resources and human health. DISCO will have capabilities for providing information on fish habitats and will allow for forecasts of future conditions and assessments of potential climate change impacts on the coastal fisheries resources of Oman. We will work very closely with our partners in Oman to improve DISCO each year, taking into account feedbacks on specific user needs. Our proposal addresses Sub-element 3.1.3: “Managing Marine Ecosystems in a Time of Changing Climate through Better Forecasts”. It takes advantage of long-standing collaboration with scientists and policy makers in Oman to transition remote sensing tools and models developed earlier for basic research, for use in managing and mitigating coastal ecosystem problems that are unique to the coastal waters of Oman. DISCO will also incorporate satellite data products and ecological forecasting tools necessary for developing long-term management and stewardship strategies for the coastal ecosystem of Oman. The flexibility of DISCO will allow the tools that we develop to be adapted for other coastal ecosystems of the world. Validation and fine-tuning of data products from DISCO will be undertaken through crowd sourcing activities involving citizen scientists, fishermen and lay persons.

Publications:

Bausch, A., Boatta, F., Morton, P., McKee, K., Anderson, R., Gomes, H., Goes, J. 2017. Elevated toxic effect of sediments on growth of the harmful dinoflagellate Cochlodinium polykrikoides under high CO2. Aquatic Microbial Ecology. 80(2), 139-152. DOI: 10.3354/ame01848

Goes, Joaquim I., Helga do R. Gomes, Khalid Al-Hashimi, and Anukul Buranapratheprat. "Ecological drivers of green Noctiluca blooms in two monsoonally driven ecosystems." Glibert PM, Berdalet E, Burford Metal (eds) Global ecology and oceanography of harmful algal blooms. Springer, Cham (2018): 327-336

Gomes, H. D. R., McKee, K., Mile, A., Thandapu, S., Al-Hashmi, K., Jiang, X., Goes, J. I. 2018. Influence of Light Availability and Prey Type on the Growth and Photo-Physiological Rates of the Mixotroph Noctiluca scintillans. Frontiers in Marine Science. 5. DOI: 10.3389/fmars.2018.00374

Gomes, H. D. R., Xu, Q., Ishizaka, J., Carpenter, E. J., Yager, P. L., Goes, J. I. 2018. The Influence of Riverine Nutrients in Niche Partitioning of Phytoplankton Communities-A Contrast Between the Amazon River Plume and the Changjiang (Yangtze) River Diluted Water of the East China Sea. Frontiers in Marine Science. 5. DOI: 10.3389/fmars.2018.00343

Sahay, A., Ali, S. M., Gupta, A., Goes, J. I. 2017. Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon. Remote Sensing of Environment. 198, 286-296. DOI: 10.1016/j.rse.2017.06.017

Sahay, A., Ali, S. M., Gupta, A., Goes, J. I. 2017. Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon. Remote Sensing of Environment. 198, 286-296. DOI: 10.1016/j.rse.2017.06.017

Yan, Y., Jebara, T., Abernathey, R., Goes, J., Gomes, H. 2019. Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models. PLOS ONE. 14(6), e0218183. DOI: 10.1371/journal.pone.0218183


More details may be found in the following project profile(s):