A Decision Support System to Monitor Hypoxic Blooms of Noctiluca in the Coastal Waters of Oman

Dale Adolph Kiefer, University of Southern California, Los Angeles, CA, United States, Zachary C. Siegrist, System Science Applications, Renton, United States, Sergio DeRada, Naval Research Laboratory, Stennis Space Center, MS, United States, Fei Chai, Second Institute of Oceanography, State Oceanic Administration, State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China, Joaquim I Goes, Lamont Doherty Earth Obs, Palisades, United States and Lubna Al-Kharusi, Ministry of Agriculture and Fisheries Wealth, Oman
Abstract:
Of the many anthropogenic and climate-driven changes reported in oceanic ecosystems, the Arabian Sea is one of the most dramatic. Within a decade and half, its autotroph-dominated base of the food chain has transitioned to one dominated by a single species of a mixotrophic dinoflagellate, Noctiluca scintillans. Since their advent in early 2000s, Noctiluca blooms have become increasingly pervasive, due to the increased intrusion of hypoxic waters into surface waters. Although Noctiluca blooms are non-toxic, they can cause fish mortality by exacerbating seawater oxygen deficiency and ammonification of seawater. Their continued expansion thus represents a significant and growing threat for regional fisheries, freshwater supply, and the general welfare of coastal communities bordering the Arabian Sea.

We will soon deliver a decision support system to the Sultanate of Oman to help them manage their marine resources as their coastal ecosystem comes under the increasing threats of surfacing hypoxic water. The decision support system, which will soon be running on a server in Oman, was developed with EASy (Environmental Analysis System), an advanced geographic information system that controls data flow, integration, storage, processing, and distribution of satellite products and output from the NCOM-COSINE biogeochemical circulation model that runs on the U.S. Navy Supercomputer Resource Center. The Omani Decision Support System provides a 4-dimensional home for data and imagery in which all data are referenced by latitude, longitude, depth, and time. It includes many tools to explore and statistically analyze the data maps including drilling and slicing 3-dimensional fields of information and plotting the results. Plug-ins to EASy consist of custom algorithms and models. Plug-ins that are already available in EASy include a fish and kelp farm model, the tracking of sensor-tagged organisms and VMS-transmitting vessels, and fish habitat identification and mapping of fishing grounds.

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