20180907_Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom...

1. Title, Journal and Authors

- Title : Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts


- Authors : Daniel R. Obenour, Andrew D. Gronewold, Craig A. Stow, and Donald Scavia


2. Summary

- In this study, they relate the summer bloom observations to spring phosphorus load within a bayesian modeling framework

- It allows us to evaluate three different forms of the load-bloom relationship, each with a particular combination of statistical error distribution, along with an untransformed response, results in a model with relatively high predictive skill and realistic uncertainty characterization, when compared to model based on more common statistical formulations.

- the benefits of a hierarchical approach that enables assimilation of multiple sets of bloom observations within the calibration processes, allowing for more thorough uncertatinty quantification and explicit differentiation between measurement and model error.


3. Application to research

- It improves model's originality and generality by containing many data within different systems.

- And also, it considers the lead time concept so that bloom in fall can be predicted by total phosphorus loading in spring. 


4. Contact

Sungho Shin (Ph.D. program)

Environmental Systems Engineering Lab.

School of Environmental Science & Engineering

Gwangju Institute of Science and Technology

1 Oryong-dong Buk-gu Gwangju, 500-712, Korea

Email :  hogili89@gist.ac.kr

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