Coastal Eutrophication and Marine Benthic Vegetation
87
The choice of the state variables in the ecological model was based on
their estimated involvement in the mass cycling. Only those functional
groups of organisms which contributed more than 10% to the total mass
flow or total pool size of organic carbon and nutrients were distinguished in the model. As a consequence, organisms at higher trophic
levels such as birds and fishes, and specific functional groups of
organisms with a low biomass such as benthic meiofauna and epibenthic
crustaceans, were not incorporated in this model analysis. Following
these rules, the state variables considered necessary to be introduced in
the ecological model are shown in Fig. 3.4.
Some of the variables were not incorporated and treated as prognostic
state variables but imposed as forcing functions derived from empirical
data. This was the case for the primary consumers as well as for the
eelgrass and Ulva spp. in Lake Grevelingen and Lake Veere. The forcing
function for eelgrass in Lake Grevelingen was derived from a separate
eelgrass model which was specifically developed for, and applied to, Lake
Grevelingen (Verhagen and Nienhuis 1983). Ulva spp. in the Venice
Lagoon were described in an intermediate way: the potential biomass
development in each of the segments was derived from empirical data
while the calculated biomass development could deviate from this
potential biomass curve due to nutrient constraints.
Table 3.2 lists the simplified budget equations for the state variables.
Processes such as denitrification, which directly influences mass balances
by taking material from the system, have been emphasized in the model
development and application. In addition to the state variables and
processes depicted in Fig. 3.4 and Table 3.2, an oxygen balance was
included in the model. Further details and parameter values are given in
the references for the applications, and in Anonymous (1990).
We adopted the ratios (by weight) used by Sfriso et al. (1989b) for the
stoichiometry of Ulva biomass:WW: DW: C: N: P "-' 30: 3.9: 1: 0.11 : 0.0 1.
3.3 Results
3.3.1 Model Calibration
Model calibration was performed for estimated average years by mixing
existing data from different years, and by using long-term averages for
meteorological forcing and other boundary conditions, since only a few
complete sets of data for specific years were available. As can be seen
87
The choice of the state variables in the ecological model was based on
their estimated involvement in the mass cycling. Only those functional
groups of organisms which contributed more than 10% to the total mass
flow or total pool size of organic carbon and nutrients were distinguished in the model. As a consequence, organisms at higher trophic
levels such as birds and fishes, and specific functional groups of
organisms with a low biomass such as benthic meiofauna and epibenthic
crustaceans, were not incorporated in this model analysis. Following
these rules, the state variables considered necessary to be introduced in
the ecological model are shown in Fig. 3.4.
Some of the variables were not incorporated and treated as prognostic
state variables but imposed as forcing functions derived from empirical
data. This was the case for the primary consumers as well as for the
eelgrass and Ulva spp. in Lake Grevelingen and Lake Veere. The forcing
function for eelgrass in Lake Grevelingen was derived from a separate
eelgrass model which was specifically developed for, and applied to, Lake
Grevelingen (Verhagen and Nienhuis 1983). Ulva spp. in the Venice
Lagoon were described in an intermediate way: the potential biomass
development in each of the segments was derived from empirical data
while the calculated biomass development could deviate from this
potential biomass curve due to nutrient constraints.
Table 3.2 lists the simplified budget equations for the state variables.
Processes such as denitrification, which directly influences mass balances
by taking material from the system, have been emphasized in the model
development and application. In addition to the state variables and
processes depicted in Fig. 3.4 and Table 3.2, an oxygen balance was
included in the model. Further details and parameter values are given in
the references for the applications, and in Anonymous (1990).
We adopted the ratios (by weight) used by Sfriso et al. (1989b) for the
stoichiometry of Ulva biomass:WW: DW: C: N: P "-' 30: 3.9: 1: 0.11 : 0.0 1.
3.3 Results
3.3.1 Model Calibration
Model calibration was performed for estimated average years by mixing
existing data from different years, and by using long-term averages for
meteorological forcing and other boundary conditions, since only a few
complete sets of data for specific years were available. As can be seen