Special Issue "Modelling for Sustainable Marine Management"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editor

Prof. Grant Bigg
E-Mail Website
Guest Editor
Department of Geography, University of Sheffield, Sheffield S10 2TN, United Kingdom
Interests: Marine climate change specialising in polar and tropical regions, with special interest in using ocean and climate modelling to understand the changing ocean; modelling icebergs, and their role in the ocean's freshwater flux, both today and in the Quaternary; modelling marine biological systems for fisheries management; modelling the interactions between climate change and society

Special Issue Information

Dear colleagues,

The marine world has long been seen as a vast, unchanging and inexhaustible place, available for exploitation but not requiring management of its resources. However, in recent decades it has become clear that this is no longer true, if it ever was, and that humanity needs sustainable methods of managing ocean resources and interacting with the ocean environment in general. Informed sustainable management requires sufficient knowledge of a system for modelling to both understand and predict how the system changes when perturbed. Therefore modelling is a key requirement for sustainable marine management. This need crosses the wide range of ways in which humans interact with the ocean, whether it is directly through fisheries, shipping, offshore renewables, sea-bed mining and drilling, leisure and tourism, or indirectly through interactions of the ocean with climate, hazards originating from the ocean, such as tsunamis, hurricanes, sea level rise, or ecosystem conservation. The range of the nature of these interactions and dependencies means that there is a diversity of spatial and temporal scales where management is needed, from the local to the global and the seasonal to the centennial.

The special issue focuses on the modelling of the marine environment for its sustainable management, covering the full range of processes, environments, space and timescales encompassed within this field. Papers covering the modelling of sustainable marine management of climate risks, natural hazards, fisheries and marine ecosystems within the context of the world’s changing climate are particularly welcomed but any topic from this wide field will be considered for publication. This special issue is particularly timely as it coincides with, and will inform, the onset of the UN’s International Decade for Sustainable Ocean Science, 2021–2030.

Prof. Grant Bigg
Guest Editor

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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sustainability 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 1900 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

  • climate change
  • marine environment
  • fisheries
  • offshore renewables
  • natural hazards
  • shipping
  • offshore mineral resources

Published Papers (2 papers)

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Research

Article
A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland
Sustainability 2021, 13(14), 7705; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147705 - 09 Jul 2021
Viewed by 844
Abstract
Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would [...] Read more.
Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year. Full article
(This article belongs to the Special Issue Modelling for Sustainable Marine Management)
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Article
Modernization of the Infrastructure of Marine Passenger Port Based on Synthesis of the Structure and Forecasting Development
Sustainability 2021, 13(7), 3869; https://0-doi-org.brum.beds.ac.uk/10.3390/su13073869 - 31 Mar 2021
Viewed by 567
Abstract
Passenger seaports are new starting-points of urban development. They form a new independent industry, become new incentives for improving urban infrastructure and increase the tourist attractiveness of the city itself and the region. In view of changes in passenger service processes, changes in [...] Read more.
Passenger seaports are new starting-points of urban development. They form a new independent industry, become new incentives for improving urban infrastructure and increase the tourist attractiveness of the city itself and the region. In view of changes in passenger service processes, changes in route ferry and cruise networks, due to COVID-19, the heads of ports and terminals set new strategic tasks to determine the directions for infrastructure modernization and forecast development. The regions of the Adriatic and Baltic Seas were chosen as the experimental base. To find new answers, it is necessary to solve the problem of synthesizing the structure of a sea passenger port, taking into account all processes and services, the influence of the external environment, building a system of target functions and limiting conditions. Thus, the necessity of forming informed decisions on modernization based on the construction of new mathematical models is substantiated. A new function has been introduced that describes the influence of the external environment. Particular attention is given to the study of the mutual influence of the city and the sea passenger port in order to determine the need to improve transport accessibility and change the near-port transport space. The presented models of structure synthesis and target functions, models including functions of the influence of the external environment on the system “city infrastructure-sea passenger port-ferry company” allow at a qualitatively new level to solve the problem of forecasting development and form a system making decisions to improve the position of the passenger terminal in the sea region. The developed models and synthesis problem formation are applicable to sea passenger ports and terminals in other regions of the seas. The models are applicable both at the stage of creating a new marine terminal and during the study and subsequent modernization of the infrastructure. The presented new models allow the port manager to give answers to the questions of strategic development of sea passenger ports in sea regions. Full article
(This article belongs to the Special Issue Modelling for Sustainable Marine Management)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfound
Authors: Jennifer B. Ross; Grant R. Bigg; Yifan Zhao; Edward Hanna
Affiliation: Department of Geography, University of Sheffield, Sheffield S10 2TN, UK; School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK; School of Geography and Lincoln Centre for Water and Planetary Health, University of Lincoln, Lincoln, UK
Abstract: Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, knowledge of the severity of a season months in advance would be useful for planning monitoring strategy but also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increase the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase and the number of peaks in iceberg numbers experienced within a given season. They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year.

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