Flexible Biological Module
Various ecosystem models have been implemented in FVCOM, including NPZ, NPZD, NPZDB, and water quality models. To make FVCOM more flexible for various needs of ecosystem studies, we have built a generalized Fortran 95 biological module into FVCOM to allow users to select either a pre-built biological model (such as NPZ, NPZD, etc) or construct their own biological model using the pre-defined pool of biological variables and parameterization functions. The generalized biological module includes seven groups: (1) nutrients, (2) autotrophy, (3) heterotrophy, (4) detritus, (5) DOM, (6) bacteria, and (7) auxiliary. A biological model can be constructed using “function pointers” to select both model structures and parameterization functions. This model can be run simultaneously together with FVCOM with parallelization (so-called “online” mode) or driven separately by FVCOM output (“offline” mode). Named the Flexible Biological Module (FBM), this new module could be driven by the physical model in FVCOM or by other popular ocean models. This module acts like a platform that allows us to examine the relative importance of different physical and biological processes under well-calibrated physical fields. Validation tests are presently being conducted to use the generalized biological module platform to re-build the state-of-the-art NPZ (Franks and Chen, 1996, 2001) and multi-species NPZD models (Ji, 2003; Ji et al., 2005a-c) for use in GB/GoM applications. FBM can be run for 1-D (vertical) and 3D cases. For the 1-D case, a special setup was built into FVCOM to allow the model to be run with tidal forcing. This module provides users an easy and fast way to check their own built biological model with pre-defined biological pool in FBM. A brief description of this module is given below. |
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1. Flow Chart of FBM The structure of the Flexible Biological Module was developed by dividing lower trophic food web processes into 7 state variable groups: 1) nutrients [N(i), i=1, nn], 2) phytoplankton [P(i) , i=1,np], 3) zooplankton [Z(i), i=1, nz], 4) detritus [D(i), i=1,nd], 5) dissolved organic matter [DOM(i), i=1,nm], 6) bacteria [B(i), i=1, nb], and 7) auxiliary state variables [Y(i), i=1, ny]. The flow chart of the transformation among these variables is shown in Fig. 8.1. We named this system “Flexible Biological Module (FBM)” to emphasize two points. First, this module provides a platform that allows users to build their own parallelized biological model from a discrete set of functions that is independent of the physical model. This module can be run simultaneously with linkage to unstructured-grid (e.g.: FVCOM) and structured-grid ocean models through the connection to the physical model dependent 3-D advection and diffusion variables or it can be run separately by itself in 1-D applications. The second reason we chose the descriptor “Flexible” is that we realized that the range of existing biological models is too vast and complex to try to encompass in a generalized way. |
Schematic of Flexible Biological Module (FBM). This is an example of our original module. This module can be easily modified to add more components. A DO module, for example, was recently added into FBM for the study of hypoxia. |
In the FBM code, the biological module is an independent 1-D system that is self-maintained and upgraded without linking to a physical model. It is easy to extend the FBM to a 3-D case by linking to the advection and diffusion modules of any physical model. It can be also converted to a Lagrangrian-based biological model by linking it with the 3-D Lagrangian particle-tracking module. The biological module in FVCOM includes point source input from rivers, nudging at lateral boundaries, air-sea interaction at the surface and benthic flux at the bottom. Because these physical processes are already documented in the FVCOM manual and code, we will focus our description of the FBM here on the internal biological processes. |
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2. Examples of FBM Application to Gulf of Maine |
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We used FBM to build a NPZ model for the Gulf of Maine with the same structures as Franks and Chen (1996). Numerical experiments were made to examine the influences of model geometrical fitting and turbulence parameterization on the temporal and spatial distributions of simulated phytoplankton in the Gulf of Maine. The assessment of the role of geometrical fitting was made by running a state-of-the-art Nutrient-Phytoplankton-Zooplankton (NPZ) model with physical fields provided from FVCOM (unstructured-grid, finite-volume coastal ocean model) and ECOM-si (structured-grid, finite-difference coastal ocean model), respectively. The impact of turbulence parameterization was studied by running a coupled NPZ-FVCOM system with various vertical turbulence modules implemented in the General Ocean Turbulence Model (GOTM). Comparisons were focused on three large tidal dissipation regions: Georges Bank (characterized by strong tidal rectification over steep bottom topography and tidal mixing fronts), Bay of Fundy (featuring large semidiurnal tidal oscillations due to the gulf-scale resonance) and Nantucket Shoals (a tidal energy flux convergence zone). For the same given tidal forcing and initial physical and biological conditions, the ability of a model to accommodate irregular coastal geometry and steep bottom topography is critical to determine the robustness of the simulated spatial and temporal structure of N and P. For the same given external forcing in FVCOM, turbulence parameterizations have less impact on N and P in mixed regions than in stratified regions. In mixed regions, both q–e and q–ql models reproduced the observed vertical mixing intensity. Since biological variables remained vertically mixed in these regions, their structures were little affected by turbulence closure schemes. In stratified regions, q-e models predicted stronger mixing than q–ql models, which produced more nutrient fluxes over the slope and thus influenced the growth and distribution of P around the tidal mixing front. A direct comparison between observed and model-predicted turbulence dissipation rates suggested that q-e models with a mixing cutoff at Richardson number of 1.0 predicted more realistic mixing intensity than q–ql models in stratified regions on Georges Bank. A brief of description of the model results are shown here. For details, please go to our recent manuscript that is in press on Deep Sea Research II-GLOBEC/GB special issue: Tian, R. and C. Chen, 2006. Influence of model geometrical fitting and turbulence parameterization on phytoplankton simulation in the Gulf of Maine. Deep Sea Research II, in press. |
Why does FVCOM predict different results from POM/ECOM-si in the Gulf of Maine, particularly on Georges Bank, Bay of Fundy and Nantucket Shoal? The reasons are: |
Selected NPZ comparison results of FVCOM and ECOM-si |
Georges Bank | ![]() |
Cross-bank section | ![]() |
Bay of Fundy | ![]() |
Which turbluence closure schedule is suitable for the biological study in the Gulf of Maine? |
Vertical Eddy Viscosity | ![]() |
T&N&P |
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Uptake f-ratio |
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