# Forecasting the stranded area of marine debris in Indonesian coasts using mobidrift model and floating drifter

B G Gautama, A Rizal, R Rahmania, A R Farhan, D Berlianty, B Priyono, K Siong, M R Harjono, C Maes, D Dobler, et al.

► To cite this version:

B G Gautama, A Rizal, R Rahmania, A R Farhan, D Berlianty, et al. Forecasting the stranded area of marine debris in Indonesian coasts using mobidrift model and floating drifter. 5th EMBRIO International Symposium: Sustainable Development of Fisheries and Marine Resource Amidst Covid-19 Era and Beyond, EIS 2021, Sep 2021, Virtual conference, France. pp.012039, 10.1088/1755-1315/1033/1/012039. [https://doi.org/10.5502/ie.2021.00018](https://doi.org/10.5502/ie.2021.00018)

HAL Id: hal-05311309

https://hal.univ-brest.fr/hal-05311309v1

Submitted on 13 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Forecasting the stranded area of marine debris in Indonesian coasts using mobidrift model and floating drifter

B G Gautama<fnref n="1" />*, A Rizal<fnref n="1" />, R Rahmania<fnref n="1" />, A R Farhan<fnref n="2" />, D Berlianty³, B Priyono³, K Siong<fnref n="4" />, M R Harjono<fnref n="5" />, J B Voisin<fnref n="5" />, C Maes<fnref n="6" />, D Dobler<fnref n="6" />, C Dufau<fnref n="7" />, M Lucas<fnref n="7" /> and O Fauny<fnref n="7" />

**Abstract.** The problem of marine debris became a concern of world scientists that placed Indonesia as the second contributor of the world. Plastic litter that leaked from the land and came through the river is a major source of Indonesia marine debris. This marine debris is a serious threat for ocean and coastal ecosystems. The Ministry of Marine Affairs and Fisheries of Indonesia supports the Presidential Regulation to reduce the plastic litter to 70% by 2025 by developing numerical modeling of marine plastic litter trajectory. Using a user-friendly web portal interface, we can simulate the trajectory of marine plastic litter from different points of source at different seasons. Wind, current, and tide data were used to force the particle on the simulation. The result of the model was compared with the record of the real time movement of twenty drifters that were released from three rivers mouth. The development of this trajectory model will become a useful tool to provide the information of the movement of marine debris in Indonesian sea waters and beyond. Knowing the path and stranded location in each season will improve our policy of planning and mitigate the impact of marine debris.

**Keywords:** Marine debris, forecasting, mobidrift model, floating drifter

## 1. Introduction

Indonesia is the largest archipelago country in the world with fisheries as one of the sectors that support national economic development. The economic gross value of fisheries export and import activities reached more than USD 5 billion yearly [1]. In the domestic market, fish consumption and estimated employment in fisheries sectors continue to increase. Sustainable development on the marine and fisheries concept is strongly becoming an important factor on the development of the policies. Related

<figure><img src="image_2.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

<footnote n="1">¹ Marine Research Center, Ministry of Marine Affairs and Fisheries Jakarta, Indonesia</footnote>

<footnote n="2">² Directorate General of Surveillance of Marine Resources and Fisheries, Ministry of Marine Affairs and Fisheries Jakarta, Indonesia</footnote>

<footnote n="2">² Institute for Marine Research and Observation, Ministry of Marine Affairs and Fisheries Bali, Indonesia</footnote>

<footnote n="4">⁴ The World Bank, Indonesia Country Office Jakarta, Indonesia</footnote>

<footnote n="5">⁵ CLS Argos Indonesia Jakarta, Indonesia</footnote>

<footnote n="6">⁶ Institute for Sustainable Development - Laboratory for Ocean Physics and Satellite Remote Sensing (IRD-LOPS) Brest, France</footnote>

<footnote n="7">⁷ CLS Headquarter Toulouse, France</footnote>

<footnote>*Correspondence author: budhi_gunadharma@yahoo.com</footnote>
to the sustainable development of marine and fisheries, marine debris particularly plastic litter is now becoming the world concern. Marine debris is not only damaging the ecosystem but also impacting directly to human health [2]. Indonesia lacks a waste management system in major cities located in the coastal area and is identified as the main contributor to marine debris. This condition is the reason that a study conducted in 2010 was placing Indonesia as the second contributor to the world marine debris [3].

The Government of Indonesia has some regulations to reduce the marine debris source from land and sea by President Regulation Number 15/2008 and 83/2018. The Ministry of Marine Affairs and Fisheries (MMAF) addressed the issue of marine debris to answer the relation between source and stranded area. The plastic debris moved around from time to time in the ocean by forcing wind and current. Although there have been some study findings of marine plastic in Jakarta Bay [4, 5, 6, 7], Banten [8, 9, 10, 11] or other regions in Indonesia i.e Pangandaran [12] and Sawu [13]. It is important to have a numerical model that can predict the stranded location on a national level. Although there was a study using model and monitoring in the trajectory of marine debris in Indonesia sea [14, 15], the operational level still needs an easy-to-use trajectory model. With the cooperation with the World Bank, MMAF had developed the integrated web portal to simulate the trajectory of the plastic debris from the mouth river. In order to validate the model, we use the drifter data position that was released at the same point. Cisadane river was chosen because it is a great river in Java Island that is suspected as the big contributor of the plastic debris.

This study aims to improve the performance of the model with tuning the parameters of the model. The validation of the model with the trajectory of the drifter will also improve the performance of the model. With the result of this study, the Government of Indonesia can have a valid model that produces the trajectory of the plastic debris. This spatial and statistical information allows central and local governments to create improved coordinative actsin dealing with marine debris problems by developing more applicable cleaning actions.

## 2. Materials and methods

The simulation of marine debris trajectory is using the numerical model named Mobidrift developed by CLS and the position of drifter Argos based used in the validation was monitored in the integrated web portal.

### 2.1. Mobidrift numerical model

Mobidrift is the numerical model implemented by CLS (Collecte Localisation Satellites) France that simulates the trajectory of various objects in the sea. The objects can be marine debris, icebergs, oil spill slick [16], Search and Rescue object and containers. In the calculation of trajectory of the object, Mobidrift uses a forward approach. The simulation in this model is based on a 2D Lagrangian advection scheme. Wind and/or current velocities are used as input for the advective component. In Mobidrift model, fluids are modeled by a set of deterministic particles. For each particle released from the initial position, we apply the considered 2D transport model to derive its trajectory up to the final date of the simulation. In our simulations, we typically consider about 10 particles released. Formally, Mobidrift model relies on the two-dimensional advection of particles by a surface displacement given by a weighted sum of tidal current and wind-induced current velocities

$$uadvp=Cc \cdot Vc+Cw \cdot Vw \qquad (1)$$

where in Equation 1 uadvp denotes the advection speed, Vc denotes the sea surface current and Vw represents the sea surface wind speed at 10 meters height. Coefficients Cc and Cw are the current and wind drift factors. As sea surface wind and current data in our study, we use METOC data provided in the web portal.
2.2.1. *Feature and input data.* This study used the integrated web portal that combined MDC (MobiDrift Client) online simulation, monitored drifter position, and METOC data. The configuration of the MDC shown in the figure 1. The system in this web portal integrates the model, Metoc data and Drifter data. The METOC data is NCEP for Wind, FES2014 for Tide, and CMEMS for Current. There is a form of user interface in the web portal that allows users to interact with the MDC through graphical icons. With the form shown in figure 2, users can simply input the configuration of input data setting for MobiDrift simulation.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Data platform scheme.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Input data setting for mobidrift simulation.</figcaption></figure>

## 2.2. Drifter specification

Type of drifter applied for this study is MAR-GE/T as shown in figure 3. Argos satellites is employed as the primary link for communication. The drifter is powered with four lithium batteries set to 7.2 volts and 26 Ah. Tracking data is documented per hour, which will be transmitted with positions during the last 3 hours recorded at every 180 seconds. The drifters are projected to communicate their positions for approximately one year. Accordingly, accurate movements of marine litter at sea can be gathered, while
the drifter's movement can be observed properly without any restriction or communication problem caused by rough weathers.

<figure><img src="image_5.png" /><figcaption>**Figure 3.** MAR-GE/T Drifter's.</figcaption></figure>

## 2.3. *Simulation scenarios and validation*

Marine debris trajectory can be approached using the Lagrangian model [14]. In this study, we developed the simulation of marine debris trajectory based on the Lagrangian model of the Mobidrift Numerical Model [17]. We built the model based on the parameters as mentioned here:

1. Position of the source: The same with the release of the drifter from the Cisadane River, the position of release of particle was on -5.98968°, 106.63574°.

2. Start of simulation is the same with the release of the drifter on 16/07/2020.

3. Metoc parameters: Coefficient wind: 0, 1.5, 3 % and Coefficient current 100 and 160%.

4. Probabilistic calculation from particles released.

5. Plot result with trajectory line and heat map of probabilistic particles.

We validate the result of the simulation using the drifter named Cisadane01 that was released on 16/07/2020 from the position -5.98968°, 106.63574° and its trajectory movement until 18/07/2020.

# 3. Results and discussion

Based on the methods and simulation scenarios described before, here we are showing the result of our work. First, we discuss the wind and current pattern of the region of interest of the study. Then we show the simulation result using a Mobidrift numerical model and its validation with the drifter movement monitoring data. Then at the end is the conclusion and some perspectives of the marine debris study in Indonesia.

## 3.1 *Wind and current pattern*

As source of marine debris in this study, Cisadane outflow empties into the Java Sea. Generally, the Java Sea is characterized by 40 m depth continental shelf, on average, which is highly influenced by the monsoon cycle. The dominant climate in the Java Sea is a common monsoon climate indicated with a complete reversal of the wind regime. It is because the temperature differences between the oceanic and continental zones. The key feature of the climate are as follows: from December to February, the wind blows from the north-west, and from June to August it blows from the south to south-east in more regular pattern. Meanwhile, in between, the winds are light and variable. The wind rose of the Java Sea surrounding Cisadane estuary for each monsoon period is shown in figure 4. Wind rose visualizes the percentage of the time the wind blows from each direction during 2020, which is the Mobidrift model's implementation time. Wind rose is based on the estimation of the 6-hourly blended wind products that
provide by the IFREMER CERSAT Global Blended Mean Wind Fields. The average speed of wind blows during December to February is 4.29 m/s, March to May is 3.26 m/s, June to August is 4.9 m/s, and September to November is 3.61 m/s.

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Wind rose of Java Sea surrounding Cisadane outflow during 2020. Top left, top right, bottom left, and bottom right wind rose represent December to February, March to May, June to August, and September to November wind blows.</figcaption></figure>

The orientation of current basin in relation to the direction the monsoon blows affects the development of a system of currents related to the winds in the Java Sea. The settling of the currents is directly correlate to the winds. In the Java Sea surrounding Cisadane outflow, during the South-East monsoon (June to August) the currents flow to the West at low speed (average 0.27 m/s), whereas during the North-West monsoon (December to February) the pattern mostly reverses and currents flow toward the East (average 0.21 m/s). During the first inter monsoon (March to May), the current would flow more variable in direction. Different currents appear during the second inter monsoon (September to November) which are more homogeneous to the west. Visualization of current magnitude and direction frequency in Cisadane outflow surrounding waters is shown as current rose in figure 5. The used current
data is daily 3D global ocean forecasts provided by CMEMS. This data is used in advection terms of
Mobidrift Lagrangian trajectory model that implemented in this study.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Current rose of Java Sea sorunding Cisadane outflow during 2020. Top left, top right, bottom left, and bottom right current rose represent December to February, March to May, June to August, and September to November current flows.</figcaption></figure>

## 3.2 Mobidrift simulation result and validation using drifter monitoring data

We showed the result of the model and the comparison with the drifter trajectory in this section. Each simulation scenario with the variations of wind and current coefficient showed a different trajectory, but in general the movement of the probabilistic particle is matched with the drifter.

### 3.2.1 Simulation using 3% of coefficient of wind and 100-160% of coefficient of current.
Using the variations of 3% of wind coefficient and 100% and 160% of current coefficient was shown in the figure 6 and 7 respectively. With the 3% of wind coefficient and 100% current coefficient, the result is better than variations of 3% of wind coefficient and 160% current coefficient where the particles are more pushed to the south.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Visualization of mobidrift simulation using the 3% of coefficient of wind and 100% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Visualization of mobidrift simulation using the 3% of coefficient of wind and 160% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

### 3.2.2 Simulation using 1.5% of coefficient of wind and 100-160% of coefficient of current.
Using the variations of 1.5% of wind coefficient and 100% and 160% of current coefficient was shown in figure 8 and 9 respectively. With the 3% of wind coefficient and 100% current coefficient, the result is more dispersed than variations of 3% of wind coefficient and 160% current coefficient.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Visualization of mobidrift simulation using the 1.5% of coefficient of wind and 100% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 9.** Visualization of mobidrift simulation using the 1.5% of coefficient of wind and 160% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

### 3.2.3 Simulation using 0% of coefficient of wind and 100-160% of coefficient of current.
Using the variations of 3% of wind coefficient and 100% and 160% of current coefficient was shown in figure 10 and 11 respectively. With the 3% of wind coefficient and 100% current coefficient, the best pattern match with the movement of the drifter.
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Visualization of mobidrift simulation using the 0% of coefficient of wind and 100% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 11.** Visualization of mobidrift simulation using the 0% of coefficient of wind and 160% of coefficient of current with the movement of drifter data. The white lines are the trajectory of the particles from the simulation and the red line is the trajectory of the Cisadane01 drifter.</figcaption></figure>

### 3.3 Conclusion and perspective

The integrated marine debris client portal has offered an inclusive and combined platform for macro plastics drift research at national level. This platform allows data assimilation and visualization in a single interface. In-situ data is gathered from MAR-GE/T drifters which are installed with the Argos System, providing an accurate movement of the drifters. With the variations of wind and current coefficient factors can tune the model to fit the trajectory movement of the drifter. The performance of the model is quite good and fits the drifter movement and the forcing data (wind and current). The variations of 0% wind factor and 160% of current factor give the best performance. All acquired data and model simulation have been documented and kept in the MDC portal, hence scientists and other
stakeholders have access to a comprehensive knowledge about the movement pattern of marine debris at sea, and prediction on the most stranded locations.

## Acknowledgment and author statement

This research is funded by the World Bank and implemented by Collecte Localisation Satellites (CLS) Argos Indonesia, in collaboration with Marine Research Centre, The Agency for Research and Human Resources, Ministry of Marine Affairs and Fisheries Republic of Indonesia and Institut de Recherche pour le Développement (IRD) France. B G Gautama, A Rizal, R Rahmania, D Berlianty, and B Priyono have the equal contribution in writing this manuscript.

## References

[1] [FAO] Food and Agriculture Organization of the United Nations 2006 *Fishery and Aquaculture Country Profiles: The Republic of Indonesia* Available at (http://www.fao.org/fishery/docs/DOCUMENT/fcp/en/FI_CP_ID.pdf) Accessed on 13 September 2021

[2] Lestari P and Trihadiningrum Y 2019 The impact of improper solid waste management to plastic pollution in Indonesian coast and marine environment *Mar. Pollut. Bull.* **149** 110505

[3] Jambeck J R, Geyer R, Wilcox C, Siegler T R, Perryman M, Andrady A, Narayan R and Law K L 2015 Plastic waste inputs from land into the ocean *Science* **347** 768–71

[4] Purba N P, Handyman D I W, Pribadi T D, Syakti A D, Pranowo W S, Harvey A and Ihsan Y N 2019 Marine debris in Indonesia: A review of research and status *Mar. Pollut. Bull.*

[5] Jasmin H H, Purba N P, Harahap S A, Pranowo W S, Syamsudin M L and Faizala I 2019 The model of macro debris transport before reclamation and in existing condition in Jakarta Bay *J. Ilmu dan Teknol. Kelaut. Trop.*

[6] Hastuti A R, Lumbanbatu D T F and Wardiatno Y 2019 The presence of microplastics in the digestive tract of commercial fishes off pantai Indah Kapuk coast, Jakarta *Indonesia Biodiversitas* **20** 1233–42

[7] Manalu A A, Hariyadi S and Wardiatno Y 2017 Microplastics abundance in coastal sediments of Jakarta Bay *Indonesia AACL Bioflux* **10** 1164–73

[8] Rahmania R, Setiawan A, Tussadiah A, Kusumaningrum P D, Yulius, Prihantono J, Gautama B G, Pranowo W S, Aisyah, Nugraha A W, Gusmawati N F, Widjanarko E and Arifin T 2021 Mapping seasonal marine debris patterns and potential hotspots in Banten Bay *Indonesia IOP Conf. Ser. Earth Environ. Sci.* **763**

[9] Prihantono J, Fajrianto I A and Kurniadi Y N 2018 Hydrodynamics and sediment transport modelling around Tanjung Pontang coastal waters, district of Serang-Banten *J. Kelaut. Nas.*

[10] Wisha U J, Husrin S and Prihantono J 2015 Hydrodynamics Banten Bay during transitional seasons (August-September) (hidrodinamika Perairan Teluk Banten pada musim peralihan (Agustus–September) *ILMU Kelaut. Indones. J. Mar. Sci.*

[11] Maharani A, Purba N P and Faizal I 2018 Occurrence of beach debris in Tunda Island, Banten *Indonesia E3S Web Conf.* **47** 1–12

[12] Purba N P, Apriliani I M, Dewanti L P, Herawati H and Faizal I 2018 Distribution of macro debris at Pangandaran Beach, Indonesia *Int. Sci. J.* **103** 144–56

[13] Purba N P, Ihsan Y N, Faizal I and Handyman D I W 2018 Distribution of macro debris in Savu Sea Marine National Park (Kupang, Rote, and Ndana Beaches) *Int. Sci. J.* **21** 64–76

[14] Dobler D, Martinez E, Rahmania R, Gautama B G, Maes C and Farhan A R 2021 *Floating Marine Debris Along Indonesian Coasts: An Atlas of Strandings Based on Lagrangian Modelling.* “Monitoring and Modelling the Circulation of Marine Debris in Indonesia” project funded by AFD (Jakarta: IRD)

[15] Syakti A D, Bouhroum R, Hidayati N V, Koenawan C J, Boulkamh A, Sulistyo I, Lebarillier S, Akhlus S, Doumenq P and Wong-Wah-Chung P 2017 Beach macro-litter monitoring and floating microplastic in a coastal area of Indonesia *Mar. Pollut. Bull.* **122** 217–25
[16] Gautama B G, Longépé N, Fablet R and Mercier G "Assimilative 2-D Lagrangian transport model for the estimation of oil leakage parameters from SAR images: application to the montara oil spill" in IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing 9(11) 4962-4969 10.1109/JSTARS.2016.2606110

[17] CLS 2021 Technical Report of Marine Debris Monitoring and Services (PTCLS-ENV-NT-21-01)