ASSESSMENT OF ECOSYSTEM RESPONSE TO WATERQUALITY CHANGE USING MEC MODEL COMBINED WITH
COMPETITION MODEL IN PUJADA BAY, PHILIPPINES
47196789 Li Tianye
Supervisor: Professor Shigure TABETA

1Obtained from the annual report[1]of Philippines project team

Backgroundn Project Title:
Development of a Comprehensive Coastal Ecosystem
Modelling, Mapping and Monitoring Systems (CCEMMMS)

n Project Concerns:
Coastal population growth and impact on ecosystem.
Coral-algae habitat and impact on tourism.
n Project Objective:
Future prediction and determination of criticalenvironmental factors on the marine ecosystems
through computational ecosystem-based Modeling2Research Objectives■ Collection and modification of relevant input data.
■ Conduct numerical simulations in Pujada Bay during certain time
period to reproduce the environment.
■ Introduce a coral-algae competition sub model to the water quality
model.
■ Based on the result of coupled model simulation, establish a rational
link between human activities, water quality changes and ecosystemchanges.

3

Structure of Models Used
[2]

[7]

4

Simulation Condition

Mati City21■ Simulation area is the rectangle range
between 6.74~6.98°N and 126.14~126.35°E,
with 30-degree rotation. (13km×23km)■ The only open boundary is located at the lower
side of this area.
■ Divided into 200m-size meshes (66×115)■ Divided into 31 layers■ Two points near the biggest city were selected
as the output points of the simulation results■ One month-long simulation (July & September)
5

Open Boundary Data & Initial Data
■ For these input data, the observation data from an out-of-bay observation site
are used because it is the nearest site from the open sea.
■ Input data of zooplankton was based on the ratio of the surface layer tophytoplankton to determine the value of full depth since there was no direct
data for the deep layer.
Parameter

Unit

Surface(2m)

July
Medium(150m)

Temperature℃

30.1482

28.04909.654430.6126

28.82039.4948

Salinitypsu

34.1044

34.5027

34.279934.279934.6278

34.3410

Phytoplankton

mmol-N/m3

0.33600.16700.00750.33660.14880.0064

Zooplankton

mmol-N/m3

0.01140.00560.00030.01140.00500.0002

Nutrient

mmol-N/m3

0.08210.006319.2773

0.04690.006722.8408

Fish density

mmol-N/m3000000

Bottom(300m)

Surface(2m)SeptemberMedium(150m)

Bottom(300m)(Refer to CMEMS[3])6Climate Data
■ The various climate data were mainly obtained from Solcast and PAGASA.
Data Type

Unit

Atmospheric Temperature℃

Atmospheric PressurehPa

Global Solar Radiation

J/m2 /s

Cloud Amount-

Relative Humidity-

Precipitation

mm/s

SourceSolcast[4]PAGASA[5]■ It should be noted that only the daily total rainfall was available for theprecipitation data, so the precipitation per second input here was basedon the daily total rainfall by converting them into average distribution overcertain hours.
710.00

6.00
4.00
2.00
0.00
-4.00

4.00
2.00
0.00
-2.00
-4.00
-6.00

Time (hour)

12.00

10.00

10.00

8.00
6.00
4.00
2.00
0.00
-2.00
-4.00
-6.00

Wind Speed Component
Toward North (Sep) (m/s)

12.00

048
96
14
4
19
2
24
0
28
833
638
4
43
2
48
052
857
662
467
272
0

Wind Speed Component
Toward North (Jul) (m/s)

-6.00

6.00

Time (hour)

6.00
4.00
2.00
0.00
-2.00
-4.00
-6.00

Time (hour)

Refer toCMEMS[3]

8.00

048
96
14419224028
833
638
443248052
857
662
467
2720

-2.00

8.00

048
96
144
19224028
833
638
443
2
48052
857
662
467
272
0

8.00

Wind Speed Component
Toward East (Sep) (m/s)

12.00

10.00

0
48
96
144
19224028833
638443248052
857
662
467
272
0

Wind Speed Component
Toward East (Jul) (m/s)

12.00

Refer toCMEMS[3]Time (hour)

The information of tide at Mati

Location
MATI, Davao Oriental(06°57′N, 126°13′E)

Constituent
SymbolPrincipal Lunar SemiM2
Diurnal Tide

Period (s)44714.164

Amplitude (m)1.30

Calculated from observed tidal data of TIDES4FISHING[6]8River Data
■ There are three rivers that flow into the bay.
■ The grid map shows the inlets of three rivers.
RiverParameter
Discharge(m3/s)
Temperature(℃)
Salinity(psu)
Phytoplankton(mmol-N/m3)
Zooplankton(mmol-N/m3)
Nutrient(mmol-N/m3)

Magum River (Magum)

Matiao Creek (Interco)

Pahamutang Creek(Dumping)

July

September

July

September

July

September

0.33

0.11

0.09

0.03

0.09

0.03

23.74

27.47

28.97

0.14

0.34

4.69

0.3366

0.3366

0.33660.01140.01140.01140.14350.14350.1435

Provided by Philippines project team (only for September 2020)9

Time series of the water temperature at surface
Site 2

Observation

Water Temperature (July) (℃)

31.1
30.9
30.7
30.5
30.3
30.1
29.9
29.7
29.5

Site 2

Observation

31.1
30.9
30.7
30.530.330.1
29.9
29.7
29.5302928272625242322212019181716151413121110987654321302928272625242322212019181716151413121110987654321Time (Day)Site 1

Water Temperature (September) (℃)

Site 1Time (Day)■ Simulated water temperature reproduced the diurnal fluctuation
tendency but remained difference between the time variations.
■ The fluctuation of the simulation results for both months were withinreasonable values.
■ possible reasons for errors:1) wind pressure or sudden short-lived rainfall; 2) the screening effectof aquatic organisms; 3) simulation approximations
10

Time series of the water salinity at surface
Site 2

Observation

Site 1

34.6

34.6

34.5

34.5

34.4

34.4

Salinity (July) (psu)

Salinity (July) (psu)

Site 1

34.3
34.2
34.1
34.0
33.9
33.8

Observation

34.3
34.2
34.1
34.0
33.9
33.8

33.7

33.7

33.6

33.6

30
2928
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
98
7
654
3
2
1

30
29
28
2726252423222120
1918171615141312111098765432
1Time (Day)Site 2Time (Day)■ Simulation of July did not show a decreasing trend, but two sudden
salinity dips on the 4th day and 23rd day.
This phenomenon can be attributed mainly to the precipitation in theclimate data.
■ Simulation of September performed better than July, which can beexplained by the less rainfall in September.
11

Time series of the nutrient at surface
Site 1Site 2

Observation

Nutrient (September) (mmol-N/m 3 )Nutrient (July) (mmol-N/m3 )0.016

Site 1

0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000

Observation

0.016
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000

30
2928
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
98
7
6
54
32
130
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

Time (Day)Site 2Time (Day)■ Simulation of July basically fit in time variation.
In the last five days of the simulation, there was a sharp increase in theobservation part, and here it is suspected that meteorological changes
caused the sudden changes in nutrient values.
■ September simulation results did not perfectly reproduce the fluctuationsof observational nutrient levels over time. However, under the existing
conditions, the error was within the tolerable range.
12Time series of the phytoplankton at surface
Site 2

Observation

0.35
0.30
0.25
0.20
0.15
0.10

Site 2

Observation

0.35
0.30
0.25
0.20
0.15
0.1030
2928
27
2625
2423
22
2120
1918
17
16151413121110987654321302928272625242322212019181716151413121110987654321

Time (Day)Site 1

Phytoplankton (September) (mmol-N/m 3 )Phytoplankton (July) (mmol-N/m 3 )Site 1Time (Day)■ The probability that the simulation results in July showed the opposite
trend to the observation was that the data related to zooplankton didn’t
match the real situation due to the lack of field data.
■ The simulation results in September were more consistent with theobservation. Although some peaks were not represented, the generaltrend and range of values were close to the observation.
13

Coral-Algae Competition Model
Each individual process probabilities can becalculated at every location for each time as:
𝑃 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 1 − 𝑒 !"! #$%𝑃 𝑎𝑙𝑔𝑎𝑙 𝑔𝑟𝑜𝑤𝑡ℎ = 1 − 𝑒 !"" & '!# (" $%𝑃 𝑎𝑙𝑔𝑎𝑙 𝑠𝑒𝑡𝑡𝑙𝑒𝑚𝑒𝑛𝑡 = 1 − 𝑒 !"# &('!#)$%
𝑃 𝑐𝑜𝑟𝑎𝑙 𝑟𝑒𝑐𝑟𝑢𝑖𝑡𝑚𝑒𝑛𝑡 = 1 − 𝑒 !"$ +$%𝑃 𝑐𝑜𝑟𝑎𝑙 𝑔𝑟𝑜𝑤𝑡ℎ = 1 − 𝑒 !"% ,% (% $%Model structure (refer to Muthukrishnan, R., O. Lloyd-Smith. J. & Fong, P.[7])■ The coral-algae competition model is a stochastic cellular automaton modelusing the R programming language to evaluate the dynamics of coral andalgal competition for space under varying environmental conditions.
■ Model is structured as a grid of cells where the state of each cell is definedby the main benthic community member occupying it.
14

Coral-Algae Competition Model■ In this model, feedback processes in local communities could modify
these conditions, so local nutrient supply (N) and herbivory pressure (H)for each cell are required and they are calculated from inputted Nutrient
supply and Consumer abundance.
SymbolParameter

Local nutrient supply
𝑁 = 𝑛 1 + 𝑦𝐿7

nNutrient supply

hConsumer abundance

yEfficiency of algal nutrient cycling

Local herbivory pressure
𝐻 = ℎ 1 − 𝑓 + 𝑓𝐿8

f

Herbivore spatial fidelity

LaAbundance of algae

LcAbundance of coral■ These parameters are taken as a range in the research.
■ One grid of MEC model was set as simulation area for competition model.
(200m×200m scale)
15Field Survey Result

Obtained from Mizuno’s project16

Field Survey Result■ The result of this field survey was used toset the background for the scenario setting.

LineCoralSeagrass

Sea urchin

StarfishOthers

L1

9.66

10.09

0.29

0.02

79.93

L2

2.23

20.45

0.68

0.02

76.62

L3

0.39

14.40

0.00

0.00

85.21

L4

0.62

8.69

0.02

0.00

90.67

All

3.29

13.07

0.24

0.01

83.39

■ The initial nutrient level and herbivory
pressure of Pujada Bay at the current
environmental background were measured
by comparing the field survey results withthe competition model simulation results.

17Simulation Result■ The final abundance of corals in thesimulation grid was calculated fromrandom initial abundance distributed in a
10%–90% range after 200 weeks.
(approximately 3.8 years long)■ At higher nutrient levels, the abundanceof corals was decreased, while it was also
limited by herbivory pressure.
■ By comparison with the field survey, it wasconcluded that the nutrient supply level in
Pujada Bay ranged from 0.6 to 0.7, while
the consumer abundance ranged from0.75 to 0.8.
18

Scenario Setup■ Generally, one of the key factors that have a large impact on theenvironment of the bay is the river that flows into the bay.
■ However, the flow of the rivers along Pujada Bay is very low and theimpact on the bay environment is negligible.
■ Other possible ways of inputting nutrient loads need to be considered.
RiverParameter
Discharge(m3/s)
Scene1: Nutrient(mmol-N/m3)
Scene2: Nutrient(mmol-N/m3)

Magum River (Magum)
Matiao Creek (Interco)
Pahamutang Creek (Dumping)
July
Same asprevious

SeptemberSame asprevious

0.1435 (Initial value)
0.4305 (initial value×3)

Horizontal distribution of the surface nutrient changes under impact of rivers
19

(Refer to Google Map)

Scenario Setup■ The waste from aquaculture, mainly from fish pens/cages, is considered as
one of the possible causes of water quality deterioration in Pujada Bay.
■ As a reference, data related to aquaculture in Manila Bay[8] were used inthis research to roughly estimate the nutrient emission.
Location

Area of fish pens/cages (ha)Feeds (kg/day)

Annual input of N (MT)Manila Bay[8]332.50

51910.00

32,530.62

Pujada Bay~1.00~156.12~97.8420

Simulation Result
Average nutrient (mmol-N/m3) for Site 1 and 2with and without the impact of aquacultureNO.

Site 1Aquaculture

No

Yes

July

0.00220.0030

September

0.00480.0053NO.

Site 2Aquaculture

No

Yes

July

0.00200.0029September

0.00450.0051

Basically 1.22 times higher
Horizontal distribution of the surface nutrient changes under impact of aquaculture■ The impact of aquaculture pens/cages and the transport of nutrients tothe whole Pujada Bay can be underpinned. In general terms, nutrient inputmainly affected the area in the northern part of the bay.
21

Simulation Result(Before)(After)■ In most cases, coral abundance eventually moved to an extremely low level,
which also implied a high probability that coral state would be replaced byother benthic communities under conditions of high nutrient supply.
22

■ The water quality and coral-algae habitat of Pujada Bay region wassimulated stably by MEC-NEST model combined with competition modelwith the available input data.
■ The nutrient level in the surface layer of water was elevated under the
impact of aquaculture, which indicated that aquaculture might be highly
potential as an environmental risk factor for Pujada Bay.
■ Due to the lack of observation data, the accuracy of the model could not
be confirmed, which requires follow-up research.

Conclusions
23

■ Collecting relevant observation data, inputting them into the modeland calculating simulations, and then comparing them with theobservation data to further minimize the errors is a necessary task inthe future.
■ A simulation of longer simulation period is also more in line with thecurrent need to show the seasonal changes if the required data areavailable.
■ More details need to be discussed on the linkage between the MECNEST model and the competition model.

Future Work24

1.

Lea A. J., Katsunori M. (2020) ANNUAL REPORT of CCEMMMS.

2.

MEC (2000) The Society of Naval Architects of Japan: MEC model workshop.

3.

Copernicus - Marine environment monitoring service,https://resources.marine.copernicus.eu/?option=com_csw&task=results

4.

Solar Forecasting & Solar Irradiance Data, https://solcast.com/

5.

Philippine Atmospheric, Geophysical and Astronomical Services Administration,
https://bagong.pagasa.dost.gov.ph/climate/climate-data/

6.

TIDES4FISHING, https://tides4fishing.com/as/philippines/mati-pujada-bay/

7.

Muthukrishnan, R., O. Lloyd-Smith. J. & Fong, P. (2016) Mechanisms of resilience: empirically quantified
positive feedbacks produce alternate stable states dynamics in a model of a tropical reef.

8.

Ulysses, M., Bernajocele, J.S., Karl, B.S., Flordeliza, D. & Lilian C. (2020) Estimation of Nutrient Load fromAquaculture Farms in Manila Bay, Philippines.

Reference25

Hydrodynamics and Water Quality Model
MEC-NEST Model (Hydrodynamics Model)■ The MEC Ocean Model was first proposed and developed by the Marine
Environmental Committee of the Society of Naval Architects and Ocean
Engineers of Japan[2], to numerically study the three-dimensional coastalhydrodynamics.
■ Here MEC-NEST model was adopted to calculate the three-dimension
tidal elevation, current flows, as well as water temperature and salinity.

26

Hydrodynamics and Water Quality Model
NPZF Pelagic Ecosystem Model (Water Quality Model)■ NPZF pelagic ecosystem model was introduced to calculate Nutrient
concentration (N), Phytoplankton density (P), Zooplankton density (Z),and Fish density (F).

27