336
Influences of physical processes on the ecosystem of Jakarta Bay: a
coupled physical – ecosystem model experiment
Alan F. Koropitan, Motoyoshi Ikeda, Ario Damar, and Yasuhiro Yamanaka
Koropitan, A. F., Ikeda, M., Damar, A., and Yamanaka, Y. 2009. Influences of physical processes on the ecosystem of Jakarta Bay: a coupled
physical – ecosystem model experiment. – ICES Journal of Marine Science, 66: 336 – 348.
A coupled physical– ecosystem model is applied to Jakarta Bay to examine the role of physical processes on the ecosystem. The simulated physical processes include tides, river discharge, and monsoon winds. The potential sources of nitrogen to Jakarta Bay are
through river inputs and wet deposition flux. The model separates the detritus compartment into pelagic and benthic components,
based on cohesive sediment processes. Physical model results agree well with the observed tidal amplitude and phase, as well as tidal
currents. The biological model can produce reasonable spatial and temporal patterns in lower trophic level characteristics of the ecosystem in some areas of the bay, although a lack of observed data limits confidence in model predictions. Model results show that the
physical processes associated with monsoons produce an intensification of Chl a and nutrient concentrations in the eastern and
western parts of the bay during northwest monsoon and southeast monsoon, respectively. The physical and biological characteristics
of bay waters are controlled by influxes from offshore; the influence of river discharge is limited to the coastal area. The sensitivity of
model predictions to the open boundary, river fluxes, and benthic detritus is discussed.
Keywords: coupled physical– ecosystem model, Jakarta Bay, monsoon wind.
Received 3 September 2007; accepted 19 December 2008
A. F. Koropitan and A. Damar: Faculty of Fisheries and Marine Science, Bogor Agricultural University (IPB), Kampus IPB Darmaga, Bogor 16680,
Indonesia. A. F. Koropitan, M. Ikeda, and Y. Yamanaka: Graduate School of Environmental Sciences, Hokkaido University, Kita 10 Nishi 5, Kita-ku,
Sapporo 060-0810, Japan. Correspondence to A. F. Koropitan: tel: þ62 251 420973; fax: þ62 251 623644; e-mail: [email protected].
Introduction
Jakarta Bay is located north of the Jakarta metropolitan area
(JMA) on the western part of the island of Java. It is a shallow
bay with a total area of 490 km2, a shoreline of 40 km, and
an average water depth of 15 m. Jakarta Bay is geographically constrained by the capes of Tanjung Karawang to the east and Tanjung
Pasir to the west (Figure 1).
The main physical processes that control the temporal and
spatial variations of the Jakarta Bay ecosystem include tides,
wind, and river discharges. Eight principal tidal constituents (O1,
K1, M2, S2, P1, Q1, N2, and K2) explain 83% of the total variance
of observed sea level at the coastal tide gauge in Jakarta Bay
(Koropitan and Ikeda, 2008). Tides are dominated by diurnal components, especially the K1 tide, which propagates from the Flores
Sea at the eastern end of the Java Sea, resulting in co-oscillation
tides (Ali, 1992; Hoitink, 2003; Koropitan and Ikeda, 2008) in
the central part as a consequence of the solid boundary of
Sumatra at the western end. The adjacent deep Pacific and Indian
Oceans are dominated by semi-diurnal components.
Winds over the Java Sea are mainly influenced by the monsoon
regime, common features of Indonesian seas. Wyrtki (1961) classified two distinct monsoons, namely the northwest monsoon
(NWM) between December and February and the southeast
monsoon (SEM) from June to August. The other months constitute transition times between these distinct monsoon periods.
Particularly over the Java Sea, the monsoon system affects the
variability of rainfall and river discharges, with dry conditions
during the SEM and wet conditions during the NWM (Aldrian
and Susanto, 2003). The monsoon winds control circulation in
the Java Sea, including Jakarta Bay (Ningsih et al., 2000). The
flow pattern in Jakarta Bay is mainly driven by monsoon winds,
the currents flowing east during the NWM at a magnitude of
0.8 –1.4 m s21 and west during the SEM at a magnitude of 0.8 –
1.2 m s21 (Setiawan and Putri, 1998).
Several rivers flow into Jakarta Bay. The major source of fresh
water (a mean discharge of 112 m3 s21) to the bay is the
Citarum River, with a catchment of 6000 km2. Another 13
smaller rivers contribute a total mean discharge of 112.7 m3 s21,
their catchments covering the JMA (2000 km2); they empty
into the central part of the bay.
Jakarta Bay is heavily impacted by human activities. It has
become a wastewater disposal site for the JMA, and domestic
sewage, and industrial and agricultural waste are deposited in it.
Based on Statistics Indonesia, the urban population in the JMA
increased by 93% from 1971 to 2005, to total 8.86 million people
by 2005. Organic waste is delivered directly to the rivers without
special treatment and is then transported into the bay. There have
been significant land-use changes, with conversion of 80% of vegetative areas into urban use (see satellite images from 1971 and
2004 at http://earthobservatory.nasa.gov/Newsroom/NewImages/
images.php3?img_id=16979). Organic non-point source pollution
has also affected the bay, likely through riverine input. For
example, concentrations of nitrate increased by five times in
Jakarta Bay during the period 1976–2003 (Z. Arifin, pers. comm.).
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337
Influences of physical processes on the ecosystem of Jakarta Bay
Additional anthropogenic changes to Jakarta Bay are planned
by the JMA government. Land reclamation is planned, and it
will shift 32 km of the current coastline offshore by 1.5 km along
the central region of the northern coast. Land reclamation,
land-use changes, and organic waste disposal impact the circulation patterns and the ecosystem in the bay. Although it is clear
that Jakarta Bay is heavily impacted by human activities, it is
not clear how the organic inputs interact with physical conditions
to influence the lower trophic level ecosystem. In particular, the
physical processes that control the ecosystem are not well understood, so here we examine the interaction between the physical
and biological processes that drive the temporal variation and
spatial distribution of nutrients and phytoplankton in Jakarta
Bay, using a coupled hydrodynamic –ecosystem model.
Coupled physical–ecosystem model
Figure 1. Jakarta Bay bathymetry (m) and the model domain
(dashed line). Locations of observations of Chl a and nutrient
concentrations from Damar (2003) are indicated by black squares
and are numbered. The large black dot and the triangle indicate the
location of a current-meter mooring (buoy) and a tide gauge,
respectively.
The biological properties of the Jakarta Bay ecosystem have
changed dramatically over the past three decades. From 1976 to
1979, average concentrations of Chl a during the wet season
were 7.5 mg l21 (Arinardi, 1995), but they rose to 14 –
30 mg l21 during the same season of 2000 and 2001 (Damar,
2003). These enhanced Chl a concentrations, likely attributable
to greater nutrient input from the rivers, probably triggered the
hyper-eutrophic conditions observed around river mouths close
to the coast area (Damar, 2003). Damar also concluded that the
hyper-eutrophic levels in the region were caused by the heavy
inputs of domestic wastewater derived from the JMA.
Hydrodynamic model
The physical model used for this study is the same Princeton
Ocean Model (POM) that has been implemented for the Java
Sea already (Koropitan and Ikeda, 2008). Here, though, we
restricted the model domain to 58550 – 6870 2000 S and 1068400 4500 –
107810 1900 E (Figure 1), and we increased the grid resolution to
250 250 m (142 93 21 grid points), based on a bathymetric
chart of Jakarta Bay provided by DISHIDROS TNI –AL (the
Hydro –Oceanographic Division of the Indonesian Navy). The
model time-steps are 3 and 90 s for the external and internal
modes, respectively, and it should be noted that the model uses
a homogenous layer approach (similar to the Java Sea model).
Ecosystem model
The ecosystem model (Figure 2) has six compartments, namely
nitrate (NO3), ammonium (NH4), phytoplankton (F), zooplankton (Z), pelagic detritus (PD), and benthic detritus (BD). The first
five compartments were mainly adopted from Wroblewski (1977),
Figure 2. Schematic diagram of the ecosystem model. Nitrogen is the currency of material flow.
338
A. F. Koropitan et al.
with the following modifications:
@NO3
NO3
expðCNH4 ÞF;
¼ RN2 NH4 G
@t
KN1 þ NO3
ð1Þ
@NH4
NH4
F RN2 NH4 ;
¼ RPD PD þ mZ Z G
@t
KN2 þ NH4
ð2Þ
k ¼ a1 þ a2 F:
@F
NO3
NH4
F
¼G
expðCNH4 Þ þ
@t
KN1 þ NO3
KN2 þ NH4
Rm ð1 expðLFÞÞZ mF F;
ð3Þ
@Z
¼ ð1 gÞRm ð1 expðLFÞÞZ mZ Z;
@t
ð4Þ
@PD
¼ gRm ð1 expðLFÞÞZ þ mZ Z RPD PD;
@t
ð5Þ
@BD
¼ ðS EÞ RBD BD;
@t
ð6Þ
where the definitions and values of the parameters are given in
Table 1. Several parameters are adopted from Damar (2003),
including constants of half saturation for nitrate (KN1) and
ammonium (KN2), and the optimum light intensity for phytoplankton growth (Iopt).
In addition, we define the photosynthetic factor (G) as
G ¼ Vm
Iz
Iz
:
exp 1 Iopt
Iopt
ð7Þ
In Equation (7), the light intensity at specific depth (Iz) is
defined using an estimate of solar radiation at the surface (I0)
and the formula of Steele (1962):
ðz
Iz ¼ I0 exp kdz ;
0
where z is the specific depth. Climatological monthly mean data
for solar radiation are derived from Oberhuber (1988). The light
extinction coefficient (k) is a function of light attenuation by seawater (a1) and phytoplankton self-shading (a2):
ð8Þ
ð9Þ
The ecosystem compartments, except benthic detritus, are
influenced by advection and diffusion in three dimensions.
Pelagic detritus is assigned a vertical sinking velocity (Wd)
(Table 1), and the vertical sinking velocity of phytoplankton
(Wp) is set at zero, as in Newberger et al. (2003).
The detritus compartment is separated into pelagic and benthic
detritus, following Guan et al. (2001a), who calculated pelagic and
benthic detritus in the bottom boundary layer based on the cohesive sediment processes. In our model, benthic detritus is an
ordinary differential equation (with no advection or diffusion),
and it acts as a pool for the deposited pelagic detritus. The
exchanges between benthic pools and pelagic detritus take place
through sedimentation (S) and erosion (E). Resuspension and
sedimentation are important for controlling the mud “blankets”
in the bottom layer and the nitrogen content of the water
column (Floderus and Håkanson, 1989). In addition, inorganic
fluxes are higher before resuspension than after it, and the difference in flux before and after resuspension could increase in
shallow water (Christiansen et al., 1997). To accommodate the
inorganic fluxes, we modified the sediment model of Guan et al.
(2001a) by adding direct inorganic fluxes in the sediment –water
layer through regeneration processes in the benthic detritus pool
(RBD BD). A detailed description of the cohesive sediment
model is given in the Appendix.
Design of numerical experiments
The coupled hydrodynamic–ecosystem model was forced by the
main K1 tidal component, monthly river discharges, climatological
mean wind (Hellerman and Rosenstein, 1983), monthly solar radiation (Oberhuber, 1988), and the monthly riverine input of nutrients. The tidal forcing was taken from the output of the Java Sea
tidal model (Koropitan and Ikeda, 2008) at two points (northeast
Table 1. Parameters for the ecosystem model.
Parameter
RN2
Vm
Iopt
a1
a2
KN1
KN2
C
RPD
RBD
mF
mZ
Rm
L
G
tce
tcd
Wd
Wp
Description
NH4 oxidation rate
Phytoplankton maximum uptake rate
Optimal light intensity for phytoplankton growth
Light dissipation coefficient of seawater
Phytoplankton self-shading coefficient
Phytoplankton half-saturation constant for NO3
Phytoplankton half-saturation constant for NH4
NH4 inhabitation parameter
Decomposition rate for pelagic detritus
Remineralization rate for benthic detritus
Phytoplankton specific mortality rate
Zooplankton specific excretion/mortality rate
Zooplankton maximum grazing rate
Ivlev constant
Fraction of zooplankton grazing egested
Critical shear stress for erosion
Critical shear stress for deposition
Sinking rate for detritus
Sinking rate for phytoplankton
Value
0.04 d21
1.5 d21
143.9 W m22
0.04 (mmol N m23)21 m21
0.046 m21
2.5 mmol N m23
0.47 mmol N m23
1.46 (mmol N m23)21
0.02 d21
0.02 d21
0.05 d21
0.05 d21
0.52 d21
0.5 (mmol N m23)21
0.3
0.14 N m22
0.25 N m22
4 m d21
0 m d21
339
Influences of physical processes on the ecosystem of Jakarta Bay
and northwest) along the northern open boundary of Jakarta Bay.
The tidal elevations were then specified by linear interpolation
along the northern open boundary. We used a radiation boundary
condition for the narrow western open boundary.
Nutrient inputs from rivers were calculated as a product of the
fresh-water discharge rate and nutrient concentrations at the river
mouths. Several representative rivers were included in the coupled
model: the Angke, Priok, and Marunda Rivers, which represented
the western, central, and eastern catchments area of the JMA,
respectively. The Citarum River, with its large catchment area,
was also included in the model. For simplicity, atmospheric wet
deposition of nitrate and ammonium was considered to be
uniform across the bay for the whole time-integration. The wet
deposition flux of nutrients was calculated from monthly rainfall
and nutrient concentration in rainwater taken from the central
region of the JMA and estimated to be 30% of the total deposition
on the land of the JMA (Smith et al., 2005). Table 2 lists the modelforcing information for river discharge, rainfall, solar radiation,
and nutrient concentrations. Nutrient concentrations from the
Citarum River were not collected at the same time as the other
river data, so we interpolated their monthly concentrations
based simply on the availability of data.
The initial concentrations of nitrate, ammonium, zooplankton,
and detritus were set to 0.1 mmol N m23 throughout the model
domain. The initial concentration of benthic detritus was
15 mmol N m22 for the whole bottom boundary layer, and the
initial concentration of Chl a (0.31 mmol N m23) was taken
from the minimum value of bimonthly field observation from
Table 2. River discharge, rainfall, and nutrient concentrations for
each monsoon season.
Parameter
Angke river discharge (m3 s21)a
Priok river discharge (m3 s21)a
Marunda river discharge (m3 s21)a
Citarum river discharge (m3 s21)a
Rainfall (mm)b
Solar radiation (W m22)c
NH4 concentration, Angke Estuary
(mmol N m23)d
NH4 concentration, Priok Estuary
(mmol N m23)d
NH4 concentration, Marunda Estuary
(mmol N m23)d
NH4 concentration, Citarum Estuary
(mmol N m23)e
NO3 concentration, Angke Estuary
(mmol N m23)d
NO3 concentration, Priok Estuary
(mmol N m23)d
NO3 concentration, Marunda Estuary
(mmol N m23)d
NO3 concentration, Citarum Estuary
(mmol N m23)e
NH4 concentration, rainwater (meq m23)f
NO3 concentration, rainwater (meq m23)f
a
December
2000
38
21.3
17.6
418.58
225
168.4
38.8
July
2001
19.55
10.48
8.08
97.61
30.4
167.45
78.3
64.5
143.2
11.7
112.1
34.9
34.7
7.5
11.1
35.2
18.6
6.4
15
27.3
37.1
37.2
23.5
37.2
23.5
Research Centre for Water Resources, Ministry of Public Works, Indonesia.
Geophysical and Meteorology Agency (BMG), Indonesia.
Oberhuber (1988).
d
Damar (2003).
e
Provided by BPLH-Jabar http://bplhdjabar.go.id/index.cfm.
f
Gillett et al. (2000).
b
c
Damar (2003). The ratio of C/Chl a was set to 50, within the
range 27 –67 reported by Riemann et al. (1989). The N/C ratio
was 16/106, based on the Redfield ratio. At the open boundary,
a radiation boundary condition was used for ocean currents. For
nutrients (ammonium and nitrate) and Chl a, values at the open
boundary were taken from observations (Damar, 2003) made at
adjacent stations (Figure 1). Zero-gradient boundary conditions
were employed for other ecosystem compartments.
The coupled model was run for 2 months that capture the
monsoons: December 2000 (NWM; wet season) and July 2001
(SEM; dry season). Analysis of the model results for ecosystem
compartments was conducted after a spin-up time of 35 d.
Model predictions were time-averaged for ten tidal cycles of the
K1 tide, so to retrieve the K1 amplitude and phase distributions
over the model domain, a 30-d period after spin-up was employed
for harmonic analysis. Unlike the hydrodynamic model, the
transport model of ecosystem compartments in the present
simulation used a coefficient of 2 for the shear-dependent
Smagorinsky formulation (Smagorinsky, 1963) to calculate horizontal eddy viscosity. According to Griffies and Hallberg (2000),
the ocean models are often more stringently constrained by
Doppler-shifted gravity wave speeds, whereas the coefficient of
Smagorinsky (1963) was originally developed for the atmospheric
model. We found that the use of 0.2 as the hydrodynamic model
coefficient produced an unstable distribution for the contour of
tracer concentration (ecosystem compartments), particularly
near the coast.
The 35-d duration of model spin-up was determined using
hourly ecosystem model predictions at three grid points in the
model domain: near the mouth of the Marunda River, in the
central region, and near the western open boundary. The concentrations of phytoplankton (Chl a) and nutrients (ammonium and
nitrate) at the three locations revealed the steady (tidal) fluctuation after 15– 20 d of simulation. We selected a spin-up time of
35 d to ensure stability in phytoplankton and nutrient concentrations. The other compartments (zooplankton and pelagic and
bottom detritus) showed unstable fluctuations during the whole
time-integration.
Model results
Tidal simulation
Model predictions were used to calculate the co-amplitude and
co-phase chart of the K1 tidal elevations (Figure 3) and to demonstrate that the tide propagates west along the coast. The westward
propagation is similar to the main stream of the Java Sea, as summarized by Koropitan and Ikeda (2008). The amplitude of the tide
is 28 cm, with the greatest amplitude in the southeastern part of
the bay. The maximum phase difference between eastern and
western open boundaries is 38 (relative to the Greenwich meridian), so it takes only 12 min for the tidal peak to travel from the
eastern to the western part of Jakarta Bay.
The simulated amplitude and phase of K1 tide agree well with
observed data. Table 3 lists comparisons of amplitude and phase
between model results and the observed K1 tide, where the amplitude and phase differences are 3.37 cm and 0.558, respectively.
Hourly tidal elevation information was collected at a coastal tidegauge station in Jakarta Bay (Figure 1) and was analysed using the
least-squares method of the t_tide program of Pawlowicz et al.
(2002), as detailed by Koropitan and Ikeda (2008). Data were collected in 1984 and 1985 and obtained from the Joint Archive for
340
A. F. Koropitan et al.
Figure 3. Modelled co-amplitude and co-phase (relative to the Greenwich meridian) distributions for K1 tidal constituents. Solid and dashed
lines denote amplitude and phase, respectively.
Sea Level of the University of Hawaii, contributed by
BAKOSURTANAL (National Coordinating Agency for Surveys
and Mapping, Indonesia).
Typical tidal ellipses in Jakarta Bay have a nearly rectilinear flow
around the capes in the eastern and western parts and are more circular in the central region towards the northern open boundary
(Figure 4a). Circulation is dominantly clockwise in the central
region, but anticlockwise near the eastern and western capes.
Tidal ellipses are influenced by the topography of Jakarta Bay,
where the flood and ebb currents follow the shoreline. In
general, model results demonstrate that the magnitude of the K1
current in the surface layer varies between 1 and 25 cm s21 and
that the strongest tidal currents are in the northern part of the bay.
Comparison of surface tidal current ellipses of the K1 tide
between model result and observations show good agreement
(Figure 4b). Current velocity observations were taken from
current meters 2 m deep at a buoy-mooring in Jakarta Bay
(Figure 1) between November 1996 and February 1997. For
details on buoy deployments, the reader is referred to Koropitan
and Ikeda (2008). The analysis of velocity data was conducted
using the least-squares method of the t_tide program
(Pawlowicz et al., 2002), and the record length was 1607 h with
gap numbers of 225 h. The differences of major axis, minor axis,
and phase between the calculated and observed ellipse were
0.05 cm s21, 0.53 cm s21, and 5.328, respectively. The use of a
high-resolution model grid in the present tidal simulation
greatly improved the prediction of tidal elevation and current
compared with the earlier model of Koropitan and Ikeda (2008),
with its lower resolution grid.
Table 3. Comparison of observed and modelled K1 tidal elevations
at the coastal tide-gauge station at Jakarta.
Parameter
Amplitude (cm)
Phase (8G)
Observeda
25.17
34.73
Model
28.54
35.28
a
Koropitan and Ikeda (2008).
NWM, corresponding to stronger wind speeds during July 2001
than during December 2000. It should be noted that the calculated
residual circulation induced by tidal currents is very small, with a
maximum magnitude of 5 cm s21. Therefore, most of the patterns in surface residual circulation are likely induced by
monsoon winds.
The direction of near-bottom residual currents is similar to that
of residual surface currents (Figures 5b and 6b). However, nearbottom currents are weaker because of bottom friction, with magnitudes that are 79 –93% less than surface currents. The strongest
near-bottom residual currents were at the eastern and western portions of the open boundary, with magnitudes of 2.0 and
3.8 cm s21, respectively.
The modelled residual circulation suggests that the monsoon
wind has the greatest effect on residual circulation. Least-squares
analysis of observations of tidal currents at the mooring station
(Figure 1), using the t_tide program of Pawlowicz et al. (2002),
show that 35 tidal constituents explain just 22.5% of the total variance in current velocities. This suggests that the non-tidal variance of the mooring data may be controlled by monsoon wind
effects, as was suggested by Setiawan and Putri (1998).
Residual circulation
Depth-averaged nutrients and Chl a distributions
Model results indicate that surface residual flow patterns move in
the direction of monsoon winds (Figures 5a and 6a). During the
NWM and the SEM, residual currents tend to move east and
west, respectively. Surface residual currents are strongest near the
western boundary during both monsoon seasons, with magnitudes
of 18.2 and 27.6 cm s21 for the NWM and the SEM, respectively.
The SEM residual currents are stronger in general than those of the
Comparisons of model predictions with field observations from
Damar (2003; locations shown in Figure 1) indicate that simulated
Chl a concentrations and nutrient concentrations (nitrate and
ammonium) are often within the range of observed values, but
that they can be under- (Chl a) or overestimated (nutrients), particularly in July 2001 at stations near the river mouths (Figure 7).
Correlation analysis for these comparisons show good agreement
Influences of physical processes on the ecosystem of Jakarta Bay
341
Figure 4. (a) Modelled K1 tidal ellipse at the surface layer. The bold and the light ellipses denote anticlockwise and clockwise circulations,
respectively. (b) Comparison between field observation (line) and the model result (dashed line) for K1 tidal currents. The line within each
ellipse represents the direction of current velocity at maximum flood tide, and the arrow attached to the tip of each of these lines indicates
the direction of the current vector rotation.
for Chl a (correlation coefficient, r ¼ 0.7), but low values of r for
nutrients (nitrate and ammonium; 20.1 and 0.3). An exception,
though, is that for ammonium in July 2001, with a value of r of
0.9. This inconsistency likely stems from (i) the fact that the
model predictions are ten K1 tidal cycle averages, whereas the
observations represent the value of a single sample, (ii) the open
boundary conditions and the riverine inputs likely influence the
downstream distributions, and (iii) the nutrient input data from
the Citarum River used to force the model were collected at a
different time from that when nutrient inputs for other rivers
were derived (see above).
The comparison of primary production (PP) between model
output and observations seems more reasonable than comparisons
for other parameters. The overestimation of calculated PP at
Station 3 in December (Figure 7g) is likely caused by the fact
that there is no sediment resuspension in the model, so light penetration and PP are too high in the water column (i.e. the model
does not simulate the effect of turbid water that usually occurs
in Jakarta Bay during the wet season).
We use depth-averaged model predictions for further analysis.
The present model, with its homogenous layer approach, does not
reproduce the stratification effect. Additionally, Jakarta Bay is generally shallow with a mean depth of just 15 m, so the effect of any
turbulence caused by wind and tide likely results in well-mixed
concentrations. The depth-averaged concentrations of nutrients
and Chl a during December 2000 (NWM) and July 2001 (SEM)
are generally higher along the coast area and intensified near
river mouths (Figures 8 –10). The concentrations of ammonium,
nitrate, and Chl a tend to decrease dramatically towards the northern open boundary, except for ammonium during the NWM,
which decreases more gradually owing to the influx from the
western open boundary.
During the NWM, there is an intensification of nutrients and
Chl a in the eastern part of the bay, but during the SEM, nutrients
and Chl a increase in the western part. Especially for the Citarum
River, the nutrient input is transported out of the bay during the
NWM, whereas it tends to be distributed inside the bay during
the SEM. The values of ammonium, nitrate, and Chl a during the
NWM are 5.0, 6.0, and 7.0 mmol N m23 in the eastern
coastal area, respectively, but they decrease to 4.9, 0.8, and
0.8 mmol N m23 in the central region. On the other hand, the
values of ammonium, nitrate, and Chl a during the SEM are
342
A. F. Koropitan et al.
Figure 5. NWM residual circulation (a) in the surface layer and (b) in the near-bottom layer calculated from the model results over ten K1
tidal cycles.
30.0, 6.4, and 1.9 mmol N m23, respectively, in the western
coastal area, but just 0.9, 0.6, and 0.6 mmol N m23 in the
central region. These patterns suggest that physical processes associated with monsoon wind likely play an important role in nutrient
and Chl a distributions. However, the intensification of nutrient
concentrations in the eastern and western parts of the bay caused
by monsoon flow patterns was not found by Damar (2003),
perhaps because of differences in the locations of observation
stations between the two studies (Figure 1); perhaps the stations
of Damar (2003) were not located in regions of high concentrations.
Discussion
The ecosystem dynamics of Jakarta Bay have been examined
using a three-dimensional coupled hydrodynamic–ecosystem
model. Comparison of the physical model with field observations shows good agreement in the amplitude and phase of
K1 tide, as well as tidal currents. The feature of K1 tide is
associated with the main features of the Java Sea, as suggested
by Koropitan and Ikeda (2008). The coupled physical–ecosystem model shows that physical processes have a direct impact
on temporal and spatial distributions of ecosystem compartments. The monsoon flow patterns produce intensification of
nutrient and Chl a concentrations in the eastern and western
parts of the bay during the NWM and the SEM, respectively.
Similar intensifications are also found near river mouths and
close to the coast. Concentrations decreased markedly towards
the northern open boundary.
Comparisons of model predictions and field observations
indicate that the model performs best for December. In July,
there are discrepancies between model predictions and observations made at selected regions near the coast. Information
about material fluxes at the open boundary is crucial in determining the downstream values inside the bay, a statement supported by Damar (2003), who showed that influx from
Influences of physical processes on the ecosystem of Jakarta Bay
343
Figure 6. SEM residual circulation (a) in the surface layer and (b) in the near-bottom layer calculated from the model results over ten K1 tidal
cycles.
offshore sites played an important role in controlling the ecosystem of the entire bay.
The higher concentrations of nutrients and Chl a along the
coast and near river mouths suggest that river discharge is a
major source of the nutrients that support phytoplankton
growth in the region. Our model predictions are generally consistent with previous results that found a strong linear correlation
between nutrient loads and inshore nutrient concentrations
(Damar, 2003). However, linear correlations between nutrient
loads and the offshore nutrient concentrations were weaker.
Therefore, Damar (2003) suggested that the temporal variability
of riverine nutrient input was not regulating the temporal variability of nutrient concentrations offshore in the bay and that the
influence of riverine input on nutrient concentrations was
limited to areas close to river mouths.
During both monsoon seasons, the calculated concentration of
ammonium was generally higher than nitrate concentrations
throughout Jakarta Bay. Damar (2003) reported that ammonium
constituted .85% of dissolved inorganic nitrogen (DIN) and
that the large proportion of ammonium at the inshore stations
344
A. F. Koropitan et al.
Figure 7. Comparison of model results (dashed line) and field observations (solid line) for the sampling stations of Jakarta Bay (after Damar,
2003): (a) Chl a (December 2000), (b) Chl a (July 2001), (c) nitrate (December 2000), (d) nitrate (July 2001), (e) ammonium (December 2000),
(f) ammonium (July 2001), (g) PP (December 2000), and (h) PP (July 2001). Station locations are indicated in Figure 1, and r is the correlation
coefficient. Model results are ten K1 tidal cycle averages, observations represent one sample.
and next to river mouths was mainly supplied by rivers. Therefore,
he concluded that there was a strong indication that bay waters
were profoundly influenced by urban domestic waste of the
JMA. However, as discussed above, sources from outside the bay
were also important.
Because the influence of nutrient inputs from rivers was
restricted to the coastal area near river mouths, we investigated
the influence of open boundary fluxes in Jakarta Bay using two
case studies with different nitrogen concentrations at the boundary. First, we used observed values based on Damar (2003) to
specify conditions along the open boundary (hereafter referred
to as the observed boundary case). Second, we used a low value
of 0.1 mmol N m23 (the minimum observed value) for nitrate,
ammonium, and Chl a concentrations along the open boundary
Influences of physical processes on the ecosystem of Jakarta Bay
345
Figure 8. Mean depth-averaged concentrations of ammonium
(mmol N m23) during (a) the NWM and (b) the SEM calculated
from the model results over ten K1 tidal cycles.
Figure 9. Mean depth-averaged concentrations of nitrate
(mmol N m23) during (a) the NWM and (b) the SEM calculated
from the model results over ten K1 tidal cycles.
(hereafter referred to as the minimum boundary case). However,
the zooplankton and pelagic detritus still used the zero-gradient
boundary conditions in both cases. The nitrogen budget was
calculated as the nitrogen stock within compartments and the
nitrogen fluxes among compartments, as shown in Figures 11
and 12.
During both monsoons, both case studies (Figures 11 and 12)
show that nutrient uptake by phytoplankton represented the
largest flux in the nitrogen cycle. The nutrient sources from
rivers and atmospheric wet deposition were strongly influenced
by wet (NWM) and dry (SEM) seasons. However, the two case
studies produced a different response in nutrient uptake. The
observed boundary case produced much more nutrient uptake
than the minimum boundary case during both NWM and SEM.
As discussed above, open boundary influxes played an important
role in controlling the DIN concentration inside the bay.
Figures 8 and 9, which correspond to the observed boundary
case, clearly show that the DIN stock consists mainly of
ammonium and that it is strongly influenced by the open boundary influxes. Therefore, when the open boundary influxes were set
to low concentrations (the minimum boundary case), the DIN
stock decreased and the uptake of nutrients decreased. On the
other hand, the two case studies produced opposite results for
the NWM and SEM periods with respect to nutrients. Nutrient
concentrations for the minimum boundary case were higher
during the SEM than during the NWM, but lower during the
SEM for the observed boundary case, because river fluxes are
more important in the minimum boundary case. Nutrient concentrations are higher during the SEM for the minimum boundary
case because outflows from the major river (the Citarum) are
well distributed within the bay during the SEM, but are transported out of the bay during the NWM.
Based on this numerical experiment, we conclude that Jakarta
Bay is primarily controlled by its adjacent waters. The impact of
nutrient inputs from rivers is limited to the coastal regions, as
indicated by earlier findings (Damar, 2003). Although model
results appear to reproduce the basic characteristics of the
Jakarta Bay ecosystem fairly well, more observations, particularly
along the open boundary, are needed to confirm the conclusions
reached here. The use of zero gradients for zooplankton and
pelagic detritus has caused unstable fluctuations during the
whole time-integration, so the budget calculations after spin-up
do not reflect steady conditions for the zooplankton and pelagic
detritus compartments, which in turn influence the benthic detritus compartment. These conditions produced unbalanced fluxes
of nitrogen among compartments, as shown in Figures 11 and
12. The model calculations could be improved in future using
specific observed data along the open boundary, including fluxes.
In addition, the pathways (Figures 11 and 12) can be used to
investigate the sensitivity of the model results to boundary conditions, in terms of nitrogen stocks and fluxes under mean conditions (residual currents), especially with respect to nutrient
sources and sinks. For the observed boundary case, the contributions of regenerated sources (zooplankton excretion and
346
Figure 10. Mean depth-averaged concentrations of Chl a
(mmol N m23) during (a) the NWM and (b) the SEM calculated
from the model results over ten K1 tidal cycles.
A. F. Koropitan et al.
benthic and pelagic detritus) to nutrient uptake are just 2.1 and
6.0% for the NWM and the SEM, respectively. These results
indicate that the contribution of open boundary fluxes may
play an important role in production in the whole of Jakarta
Bay. However, there is as yet little observational evidence to
support these results, particularly with respect to benthic
regeneration.
Evidence from other regions shows that bottom regeneration
can make a significant contribution to the nitrogen cycle
(Moll, 1998; Wei et al., 2004). The incorporation of benthic
regeneration into ecosystem models provides significant challenges. Guan et al. (2001b) used cohesive sediment processes
for the benthic compartment, but still found some discrepancies
with observations, particularly for phytoplankton. Other models
retained the parameterization method for benthic coupling with
moderate success, e.g. in applications to the North Sea (Moll,
1998) and the Bohai Sea (Wei et al., 2004). Another model
(Xu and Hood, 2006) tried to modify the sediment resuspension
processes without a benthic detritus compartment, but still used
dummy resuspension as a source of inorganic sediment in the
bottom layer. That model was applied to Chesapeake Bay, but
the model results still showed some discrepancies with
observations.
In our model, we accommodated direct inorganic fluxes in the
bottom layer. However, pelagic detritus was the only source for
benthic detritus in the model. This situation presented us with
problems, because instabilities in pelagic detritus resulting from
open boundary conditions may affect the benthic detritus compartment. In future, we need to account for other biochemical
sediment processes in addition to physical processes, e.g. the cohesive sediment.
Figure 11. The pathway of nitrogen through the model system for the observed boundary case. DIN is dissolved inorganic nitrogen
(ammonium plus nitrate). Left and right values are the calculated stock and flux for December 2000 and July 2001, respectively.
347
Influences of physical processes on the ecosystem of Jakarta Bay
Figure 12. The pathway of nitrogen through the model system for the minimum boundary case. DIN is dissolved inorganic nitrogen
(ammonium plus nitrate). Left and right values are the calculated stock and flux for December 2000 and July 2001, respectively.
Acknowledgements
We thank three anonymous reviewers and Elizabeth North and
Franz Mueter (the guest editors) for their critical comments and
constructive suggestions. AFK was supported by a scholarship
from the Ministry of Education, Culture, Sports, Science and
Technology (MEXT), Japan, for a PhD programme at Hokkaido
University. The study was also partially supported by the JSPS
Core University Programme between Hokkaido University and
the Indonesian Institute of Science. We also thank Hary
Budiarto and Ressy Oktivia of the Seawatch Programme, BPPT
Indonesia, for providing the hourly current data. The code of harmonic analysis embedded in the POM was provided by Agus
Setiawan of BPPT Indonesia.
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Appendix
Governing formulations for pelagic and bottom detritus
based on the cohesive sediment process
The bottom boundary conditions for pelagic detritus (PD) are
determined by the cohesive sediment process, following Guan
A. F. Koropitan et al.
et al. (2001a). To calculate PD distribution, the rates of erosion
and sedimentation of the cohesive sediment model are used. In
the model, it is assumed that when bottom shear stress/friction
(tb) is smaller than a critical shear stress for deposition/sedimentation (tcd), there is a reduction in PD concentration in the water
column because of settling and conversion to bottom detritus
(BD) in the bottom layer [Equation (A2)]. On the other hand,
when the bottom friction is higher than a critical shear stress for
erosion (tce), then erosion takes place, and there is an addition
of PD from the bottom and a corresponding reduction in BD in
the bottom layer [Equation (A3)]. Between those values, erosion
and deposition balance each other out [Equation (A4)]. Bottom
friction is defined (He and Weisberg, 2002) as
tb ¼ rCD juju;
ðA1Þ
where r is the water density, CD the bottom drag coefficient, and u
the near-bottom current. The algorithm of bottom friction calculation is also provided in POM.
Sedimentation (S) was accounted for using a modified algorithm, based on Krone (1962). The algorithm assumes that the
PD reaching the bottom has a probability of remaining there
ranging from 0 to 1 as the bottom shear stress varies between its
upper limit for deposition and zero. Sedimentation is calculated
as the product of the settling of PD and the probability of PD
remaining on the seabed:
tb
S ¼ Wd PDb 1 for tb tcd :
tcd
ðA2Þ
Erosion (E) was described by a modified algorithm based on
the classical approach of Partheniades (1965). The flux of eroded
BD is determined by the remineralization rate of BD and the concentration of BD on the surface of the bottom layer as follows:
tb
E ¼ RBD BD
1 for tb tce :
tce
ðA3Þ
Under equilibrium conditions,
S ¼ E ¼ 0 for tcd , tb , tce :
ðA4Þ
For the cohesive sediment processes, all parameter values are
similar to Ningsih (2000), who applied them to the Java Sea.
However, the present model does not consider the effect of windwave on the bottom shear stress in calculating the erosion and
sedimentation fluxes. The parameter definitions and the values
of the above formulae are given in Table 1.
doi:10.1093/icesjms/fsp011