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Drift Modeling at Sea: Applications to Marine Pollution, Harmful Algal...
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Drift Modeling at Sea: Applications to Marine Pollution, Harmful Algal Blooms, Sargassum, and Search and Rescue Operations

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1721

Special Issue Editors

Pierre Daniel
*
E-Mail Website
Guest Editor
Météo France, Toulouse, France
Interests: trajectory modelling; oil spill drift; leeway; search and rescue; sargassum
* Retired Senior scientist from Meteo France
Dr. Graig Sutherland

E-Mail Website
Guest Editor
Canadian Meteorological Center, Environment and Climate Change Canada, Dorval, QC H9P 1J3, Canada
Interests: physical oceanography; air-sea-ice interaction; tides; waves; trajectory modelling

Special Issue Information

Dear Colleagues,

Modeling the drift of substances and objects at sea is one of the most pressing and challenging topics in marine science. Drift models are powerful tools that play a key role in emergency responses, environmental protection, and search and rescue; they can guide efforts to locate missing people, track the movement of oil spills, or monitor the spread of sargassum and harmful algal blooms.

We invite authors to contribute to the Special Issue “Drift Modeling at Sea: Applications to Marine Pollution, Harmful Algal Blooms, Sargassum, and Search and Rescue Operations”, which aims to showcase the latest advances in drift modeling and highlight how these tools expand our knowledge and practical capabilities.

The ocean surface is a dynamic environment shaped by waves, winds, and air–sea exchanges. Predicting the fate of drifting objects or substances is complex; oil can be mixed beneath the surface, floating objects may capsize, and subsurface currents often transport material in a different manner to surface waters.

We particularly welcome studies that apply drift models informed by oceanic and atmospheric systems, investigate uncertainty related to drift models, utilize dynamic systems theory for drift prediction, and demonstrate realistic applications of drift models. We invite authors to join us in advancing scientific understanding in this field and establishing practical solutions to one of the ocean's most critical challenges.

Pierre Daniel
Dr. Graig Sutherland
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advances in trajectory modeling
  • process of drift and dispersion in coastal and open waters
  • uncertainty related to drift models
  • dynamic systems theory for drift prediction
  • accidental marine pollution
  • harmful algal bloom
  • sargassum
  • search and rescue

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Published Papers (2 papers)

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Research

23 pages, 3561 KB  
Article
Value and Limitations of Drift Modelling for Reconstructing the Loss of the Trawler Ravenel
by Pierre Daniel, Guy Claireaux, Nicolas Cormier, Michel L’Hour, Alexis Rochat and Cécile Sauvage
J. Mar. Sci. Eng. 2026, 14(5), 444; https://doi.org/10.3390/jmse14050444 - 27 Feb 2026
Viewed by 485
Abstract
The disappearance of the trawler Ravenel in January 1962, resulting in the loss of fifteen men from the Saint-Pierre-and-Miquelon archipelago, has long remained unresolved. This study integrates archival documentation, eyewitness testimony, atmospheric and oceanic reanalyses, and probabilistic drift modelling to reconstruct the circumstances [...] Read more.
The disappearance of the trawler Ravenel in January 1962, resulting in the loss of fifteen men from the Saint-Pierre-and-Miquelon archipelago, has long remained unresolved. This study integrates archival documentation, eyewitness testimony, atmospheric and oceanic reanalyses, and probabilistic drift modelling to reconstruct the circumstances of the loss and to constrain the wreck location. Backward and forward drift simulations were conducted using the MOTHY sea-drift model, incorporating high-resolution tidal dynamics and wind forcing from ERA-20C and ERA5 reanalysis. Results show that uncertainty in debris stranding time exerts a much stronger influence on reconstructed drift paths than uncertainty in stranding location. The discovery of the wreck in May 2025 enabled forward simulations that indicated a most probable sinking time, with ERA5 producing debris stranding times consistent with historical observations. These results confirm the predictive skill of the modelling framework while highlighting remaining uncertainties regarding the sequence of events preceding the sinking. Full article
(This article belongs to the Special Issue Drift Modeling at Sea: Applications to Marine Pollution, Harmful Algal Blooms, Sargassum, and Search and Rescue Operations)
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32 pages, 12376 KB  
Article
Drift Trajectory Prediction for Multiple-Persons-in-Water in Offshore Waters: Case Study of Field Experiments in the Xisha Sea of China
by Jie Wu, Zhiyong Wang, Liang Cheng and Chunyang Niu
J. Mar. Sci. Eng. 2026, 14(2), 144; https://doi.org/10.3390/jmse14020144 - 9 Jan 2026
Viewed by 594
Abstract
With the increasing frequency of maritime activities, large-scale man overboard incidents raise higher demands on maritime search and rescue (SAR) decision-making. Most existing drift models are designed for single-person-overboard situations and have limited ability to model multiple-persons-in-water (MPIW) scenarios. To address this gap, [...] Read more.
With the increasing frequency of maritime activities, large-scale man overboard incidents raise higher demands on maritime search and rescue (SAR) decision-making. Most existing drift models are designed for single-person-overboard situations and have limited ability to model multiple-persons-in-water (MPIW) scenarios. To address this gap, this study proposes a drift trajectory prediction method for MPIW based on full-scale field experiments in the Xisha Sea, South China Sea. In December 2023, six drift experiments were carried out, providing 57 h of tracking data under typical conditions with wind speeds from 0.17 to 7.77 m/s and surface current speeds from 0.06 to 0.96 m/s. Two basic MPIW scenarios were considered, side-by-side connection and random connection, and four MPIW drift models were built for upright 3-person (UP_3), upright 5-person (UP_5), upright–facedown–upright (U-F-U) and facedown 2-person (FD_2). The corresponding wind-induced drift coefficients were estimated. The stochastic variability of the crosswind leeway (CWL), including sign-change frequency and the probability of positive CWL, was systematically analyzed. For unconstrained regressions, the downwind leeway slope coefficients range from −2.96% to −12.61%, while CWL slope coefficients range from 1.01% to 2.78%, depending on group configuration. Monte Carlo simulations were then used to compare different model groups. In typical test cases, the proposed MPIW models reduce the normalized cumulative error for 11 h trajectory prediction from 0.18–0.23 to 0.08–0.17, indicating a clear improvement in the accuracy of search area delineation for group drowning scenarios. The results provide a useful reference for MPIW drift prediction and SAR decision-making in similar offshore and deep-water environments. Full article
(This article belongs to the Special Issue Drift Modeling at Sea: Applications to Marine Pollution, Harmful Algal Blooms, Sargassum, and Search and Rescue Operations)
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