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 August 2024 | Viewed by 10316
Special Issue Editor
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 UN’s International Decade for Sustainable Ocean Science, 2021–2030 (https://en.unesco.org/ocean-decade).
Prof. Grant Bigg
Guest Editor
Manuscript Submission Information
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Keywords
- climate change
- marine environment
- fisheries
- offshore renewables
- natural hazards
- shipping
- offshore mineral resources
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.