The main scope of the Marine Strategy Framework Directive is to achieve good environmental status (GES) of the EU's marine waters by 2020, in order to protect the marine environment more effectively. The trophic index (TRIX) was developed by Vollenweider in 1998 for the coastal area of Emilia-Romagna (northern Adriatic Sea) and was used by the Italian legislation to characterize the trophic state of coastal waters. We compared the TRIX index calculated from in situ data (“in situ TRIX”) with the corresponding index simulated with a coupled physics and biogeochemical numerical model (“model TRIX”) implemented in the overall Adriatic Sea. The comparison between in situ and simulated data was carried out for a data time series on the Emilia-Romagna coastal strip. This study shows the compatibility of the model with the in situ TRIX and the importance of the length of the time series in order to get robust index estimates. The model TRIX is finally calculated for the whole Adriatic Sea, showing trophic index differences across the Adriatic coastal areas.

In this paper a coupled physical–biogeochemical one-dimensional numerical model (BFM–POM 1D) was implemented in the Gulf of Trieste, (northern Adriatic Sea) and its structure was tested in order to evaluate its usability as a tool to support coastal management and planning. The evaluation concerned the ability of the model to reproduce the main trophic pathways, as well as their temporal variability, in terms of seasonal variations. The ecosystem structure comprised three phytoplankton groups, four zooplankton groups, one bacterial group, and a simple benthic return in order to parametrise benthic processes. The dynamics and interactions between groups were studied, as well as the model’s sensitivity to different trophic web configurations. Results showed that the model was capable of replicating the behaviour of seasonal vertical profiles of the major biogeochemical elements, and the prevalence of the microbial food web shaping the trophic chain in the Gulf of Trieste. The model also responded to strong forcings at the surface and different trophic arrangements, thus providing initial evidence of its potential as a scientific tool aimed at marine coastal management

We present a newly developed upper-thermocline, open-ocean biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations and parameter optimization studies with limited additional computational cost. The model, which is derived from the full 56-state-variable Biogeochemical Flux Model (BFM56; Vichi et al., 2007), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on upper-thermocline, open-ocean, and non-iron-limited or non-silicate-limited conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After describing BFM17, we couple it with the one-dimensional Princeton Ocean Model for validation using observational data from the Sargasso Sea. The results agree closely with observational data, giving correlations above 0.85, except for chlorophyll (0.63) and oxygen (0.37), as well as with corresponding results from BFM56, with correlations above 0.85, except for oxygen (0.56), including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous models of similar size, BFM17 provides improved correlations between several model output fields and observational data, indicating that reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach. Notable additions to BFM17 over similar complexity models are the explicit tracking of dissolved oxygen, allowance for non-Redfield nutrient ratios, and both dissolved and particulate organic matter, all within the functional group framework.