Bayesian Data Assimilative Ocean Forecasting, Learning, and
Optimal Sensing for Sustainable Fisheries Management in India
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A. Gupta, P.J. Haley, Jr., P.F.J. Lermusiaux Massachusetts Institute of Technology
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Project Summary Ongoing MIT-MSEAS Research Presentations and Meetings TATA-supported Publications Additional Project Links Background Information
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| This research is sponsored by the MIT Tata Center. | |||
Project Summary
This project will develop Bayesian data-driven estimation and model learning methods, stochastic forecasts, and analysis products for ocean physics, biogeochemistry and fisheries. Our data-assimilative ocean field and uncertainty estimates, optimal data collection guidance, and coastal ecosystem-based scenario and risk analyses will serve as quantitative technical decision aides for sustainable rights-based fisheries management. Our forecasts have diverse local commercial and societal applications involving not only fisheries but also coastal zone management, pollution mitigation, monitoring, ocean engineering, tourism, shipping, financial hedging, and re-insurance. In addition to educating students, we also collaborate with colleagues from IISc Bangalore (Prof. Deepak N. Subramani), governmental and non-governmental agencies to transfer our technology and train the users.
Background information is available below.
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Ongoing MIT-MSEAS Research
Long-Term Solution:
Our long-term solution is to develop a data‐assimilative ecosystem and physics‐based prediction system with quantified uncertainty for informing fisheries managers and fishers to make optimal decisions for sustainable fisheries co-management. We plan to provide accurate predictions and reanalyzes of fertile marine fishing grounds with quantified uncertainty, dynamic geographic range of species locations, rapid changes in productivity, ecosystem health and species‐specific prediction of potential fish availability.
Objectives:
The primary goal of our effort is to develop and provide Bayesian data‐assimilative ocean physical, bio‐geochemical and fish forecasting products, model learning capabilities, and adaptive sensing schemes for sustainable fisheries management in India. Our specific objectives are to:
- Implement the stochastic DO equations for marine ecosystem and predictive fish models and utilize these DO equations for probabilistic forecasting
- Further develop and implement GMM‐DO filter for such physical-biogeochemical‐fish probabilistic predictions systems
- Continue improving forecasts by utilizing blended data‐based objective analysis and global ocean model products as initial conditions to our regional modeling system, and by tuning model parameters to available data
- Develop physics-based species-specific forecasts by employing temperature-food models and implement our biogeochemical-ecosystem models
- Continue to educate students and scientists at MIT and at our partner agencies, and collaborate with partners in India.
Presentations and Meetings
- Talk at Indian Institute of Sciences (IISc) Bangalore on July 24th, 2019
- Talk at Space Application Center (SAC), Indian Space Research Organization (ISRO), Ahmedabad on July 23rd, 2019
- Meetings at Fisheries Survey of India (FSI), Centre for Marine Living Resources & Ecology (CMLRE), Dakshin Foundation in January, 2019
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Additional Project Links
- Coastal Ocean Sensing and Forecasting for Fisheries Management: Practical Systems for India
- Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data
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Background Information
In India, the coastal ocean provides multiple means of sustainable development. It affects many economic sectors including coastal management, fisheries, energy, tourism, conservation, shipping, security and marine operations. It is also essential to welfare, linking to climate regulation, carbon sequestration, habitat and biodiversity. The inland water resources and the coastal Indian oceans provide employment to more than 14 million people in the fisheries sectors alone. To successfully coexist with the ocean and optimally utilize and manage marine resources, India needs to monitor and predict impacts of ocean activities, and to operate efficient coastal sensing technologies. Better understanding and forecasting of environmental resources and human impacts requires novel synergies between sensing and modeling. Such innovations will lead to better ocean management. All of this is especially crucial for India. However, solutions must be suited to the Indian context. Systems should be practical, integrated with local organizations and populations, and geared towards the specifics of the Indian coastal zones, ecosystem, and livelihoods.
Increased demand for fish, coupled with unsustainable fishing practices lead to over-exploitation and fast depletion of fish stocks. Coastal fisheries and aquaculture stocks often thrive on very specific water conditions. Building capabilities for coastal ecosystem forecasting and for optimal ocean data collection will help in ensuring and managing the survival and reproduction of healthy stock. This is another motivation for our work.
- Multiscale regional dynamics and process modeling (e.g., what are key dynamics and exchanges occurring in the region among the mixed-layer dynamics, boundary currents and/or subsurface circulation features), and
- Multi-resolution modeling and data assimilation (e.g., what is the optimal way to utilize the information content collected by the new Bay of Bengal Coastal Observation System?), and
- Species-specific fish forecast (e.g., how to utilize and tune existing temperature-food model to provide species specific forecast of Bhetki, Pomphret, Hilsa and Mackarel?).
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