Title of research project:

Physical-ecosystem numerical model for Jakarta Bay (Phase 2)

Endro Soeyanto* and Akihiko Morimoto***Research Center for Oceanography, National Research and Innovation
Agency (BRIN), Jakarta Indonesia** Center for Marine Environmental Studies (CMES), Ehime University, Japan

Purposes
The purpose of this research project are:
1. To describe in detail a coupled physical-ecosystem numerical model for
the Jakarta Bay waters;
2. To apply the above model for the prediction of physical-ecosystemparameters using the archives observation data in the Jakarta Baywaters
3. To analyze the model results and looking for the relationship between thedynamics of physical processes and ecosystem parameters in theJakarta bay waters.
Methods
We build the physical-ecosystem ocean model for Jakarta Bay waters withcoupling between the physical process (hydrodynamic) model and ecosystemmodel. The hydrodynamic model constructed by some input observation data as
initial forcing such as tide, wind stress, river discharge, heat fluxes, watertemperature and salinity water masses, etc. The hydrodynamic model used inthis research was applied by Soeyanto, et.al (2023). Meanwhile, the ecosystemmodel is a nutrient-phytoplankton zooplankton-detritus (NPZD) model (e.g.,
Morimoto et.al, 2021) as shown in the Figure.1.
The physical-ecosystem ocean model combined the physical processes such as
ocean circulation, temperature and salinity distributions, etc can influence thedynamics in marine ecosystem, some ecosystem parameters such as nutrients,
dissolved inorganic nitrogen (DIN), nutrients, dissolved inorganic phosphorus(DIP), phytoplankton (PHY), zooplankton (ZOO), particulate organic nitrogen /detritus (PON) and dissolved oxygen (DO) need to consider and merge in theprevious physical model, and build a coupled physical-ecosystem numericalmodel for the Jakarta Bay.
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Figure.1. Configuration of ecosystem model (Morimoto, A., 2021)

Table.1 Parameters used on the ecosystem modelParameter

Valuesphytoplankton:
resprespiration rate of phytoplankton

0.03

vmmaximum photosynthetic rate

5

akdin

half saturation constant for DIN (0.5;11.4;3.0) (\mu M/l)

1.5

akdip

half saturation constant for DIP (\mu M/l)

0.1
2aIoptoptimum light intensity for phytoplankton (W/m )70

deadphy

Natural mortality rate of phytoplankton (mu molN/l)

0.03

Rmax

maximum grazing rate of zooplankton (1.0; 1.5; 0.3)

0.5

Deadzoonatural mortality rate of zooplankton (mu molN/L)

0.06

ctzm

Temperature coef. for zooplankton mortality

Aruf

assimilation efficiency for zoo mortality

0.1

Bet

excretion of zooplankton

0.01

sinkponSinking velocity of detritus (m/s)

1.5

decoponbacterial decomposition rate of detritus (s**(-1))

1.25

zooplankton:0.0693

Detritus:

Progress Results
During 2023, we examine the important role of river water supplies for driving theecosystem model results, because the comparison between the model results
and observation data is still unreliable (Figure 2). This is a reason we still pay
attention to find out the reliable input parameters that probably dominant in themodel, i.e. river discharge.

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To check how much the river discharge contribution to the model, we did asimulation without the ecosystem model as shown in Figure 4. For example,
dissolved inorganic nitrogen (DIN), which is calculated using physical(hydrodynamic) models without ecosystem model parameters. The model resultsin Figure 4 show the horizontal distribution of daily DIN at several simulation times.
The right-side panel shows the model results with daily river discharge input
smoothed by a 6-day running-mean process. An example of the original and after
6-day running-mean river discharges can be seen in Figure 3.

Figure.2. The model results observation for Sta-08 in Jakarta Bay

Figure.3. Cakung River Discharge. The purple graph is the result of the application's 6-dayrunning-mean process.

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After 6-day running meanat the river discharges

Figure.4. The horizontal distribution of DIN produced from only the hydrodynamic model in caseoriginal daily and after 6 day-running mean river discharges (left column panels)

The model results on Figure 4 show that the river discharges are important toreproduce observed parameters such as salinity and DIN distributions by thecouple the hydrodynamic-ecosystem model.

Future challenges
To meet the need for realistic river discharge input, a river discharge estimation
method is currently being carried out on rivers for which there is no discharge
measurement data in Jakarta area. Some steps, namely:
1. collecting the precipitation data
2. examine the basin area of each river that flowing to Jakarta Bay waters
3. applying a run-off model to estimate the river discharge data

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The above stages are currently being implemented gradually. The discharge
data results from the run-off model will be used as input data for thehydrodynamics-ecosystem model. The parameters that are produced will thenbe compared to the field measurements.

References
Soeyanto E., Morimoto A., Hayami Y. and Sudaryanto A. (2023) Seasonal
spreading and age of Citarum River water in the Jakarta Bay, IOP Conf. Series:
Earth and Environmental Science 1137 (2023) 012009, doi:10.1088/17551315/1137/1/012009
Hayami Y, Morimoto A, Sudaryanto A, Sachoemar S I, Soeyanto E, Rusdiansyah
A and Saleh M 2020 Estuarine, Coastal and Shelf Science 246 107030
Onitsuka G, Yanagi T, Yoon J-H (2007) A numerical study on nutrient sources inthe surface layer of the Japan Sea using a coupled physical-ecosystem model. JGeophys Res 112:C05042. https://doi. org/ 10. 1029/ 2006J C0039 81
Morimoto A., Mino Y., Buranapratheprat A.,Kaneda A., Tong-U-Dom
S.,Sunthawanic K.,Yu X. and Guo X (2021). Hypoxia in the Upper Gulf of Thailand:
Hydrographic observations and modeling. Journal of Oceanography Special
Section: Coastal Ecosystem Change in Asia: Hypoxia, Eutrophication, andNutrient Condition. https://doi.org/10.1007/s10872-021-00616-3.

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