4.5 Article

Ecological forecasting in Chesapeake Bay: Using a mechanistic-empirical modeling approach

期刊

JOURNAL OF MARINE SYSTEMS
卷 125, 期 -, 页码 113-125

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jmarsys.2012.12.007

关键词

Ecological forecasting; Ocean prediction; ROMS; Algal blooms; Pathogenic bacteria; USA; Maryland/Virginia/Delaware; Chesapeake Bay

资金

  1. NOAA Center for Sponsored Coastal Ocean Research's Monitoring for Event Response for Harmful Algal Bloom (MERHAB) [NA05NOS4781222, NA05NOS4781226, NA05NOS4781227, NA05NOS4781229]
  2. NOAA EcoForecasting Program
  3. NOAA Center for Satellite Applications and Research
  4. Maryland Sea Grant

向作者/读者索取更多资源

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic-empirical approach, whereby real-time output from the coupled physical-biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic-empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken. Published by Elsevier B.V.

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