Tropical beaches provide coastal flood protection, income from tourism, and habitat for flagship species. They urgently need protection from erosion, which is being exacerbated by changing climate and coastal development. Traditional coastal engineering solutions are expensive, provide unstable temporary solutions, and often disrupt natural sediment transport. Instead, natural foreshore stabilization and nourishment may provide a sustainable and resilient long-term solution. Field flume and ecosystem process measurements, along with data from the literature, show that sediment stabilization by seagrass in combination with sediment-producing calcifying algae in the foreshore form an effective mechanism for maintaining tropical beaches worldwide. The long-term efficacy of this type of nature-based beach management is shown at a large scale by comparing vegetated and unvegetated coastal profiles. We argue that preserving and restoring vegetated beach foreshore ecosystems offers a viable, self-sustaining alternative to traditional engineering solutions, increasing the resilience of coastal areas to climate change.@en

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model- or application-specific, and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, to ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics. Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/Shingle</a. Further details of the project presented at http://shingleproject.org.@en

The Kralendijk Declaration is the outcome of the conference on "Coastal Dynamics and Ecosystem Change: Caribbean, Quo Vadis?" that was held on Bonaire, October 18-21 2016.@en

Computational simulations of physical phenomena rely on an accurate discretisation of model domains. Numerical models have increased in sophistication to a level where it is possible to support terrain-following boundaries that conform accurately to real physical interfaces, and resolve a multi-scale of spatial resolutions. Whilst simulation codes are maturing in this area, pre-processing tools have not developed significantly enough to competently initialise these problems in a rigorous, efficient and recomputable manner. In the relatively disjoint field of Geographic Information Systems (GIS) however, techniques and tools for mapping and analysis of geographical data have matured significantly. In order to ensure model simulations are reproducible and the provenance of the data required is recorded, the manipulation and agglomeration of initialisation data needs to be standardised and automated. With the typical constraints on simulation domains for geophysical models consisting of bounding paths and surface scalar fields in a two dimensional plane, GIS frameworks potentially offer exactly what is required to formalise this processing. A new approach to the discretisation of geophysical domains is presented (Candy et al. [1], and [2] — [3]), and introduced with verified implementations. A complex domain example with a multi scale discretisation will be presented with scales ranging from 2000 km down to 5m details of the manmade structures of Portland harbour (Figure 1) and lagoon flats behind Chesil beach on the south coast (Figure 2). This brings together the fields of geospatial analysis, meshing and numerical simulation models. This platform enables us to combine and built up features, quickly drafting and updating mesh descriptions with the rigour that established GIS tools provide. This, combined with the systematic workflow, supports a strong provenance for model initialisation and encourages the convergence of standards. @en

Computational simulations of physical phenomena rely on an accurate discretisation of model domains. Numerical models have increased in sophistication to a level where it is possible to support terrain-following boundaries that conform accurately to real physical interfaces, and resolve a multi-scale of spatial resolutions. Whilst simulation codes are maturing in this area, pre-processing tools have not developed significantly enough to competently initialise these problems in a rigorous, efficient and recomputable manner. In the relatively disjoint field of Geographic Information Systems (GIS) however, techniques and tools for mapping and analysis of geographical data have matured significantly. In order to ensure model simulations are reproducible and the provenance of the data required is recorded, the manipulation and agglomeration of initialisation data needs to be standardised and automated. With the typical constraints on simulation domains for geophysical models consisting of bounding paths and surface scalar fields in a two dimensional plane, GIS frameworks potentially offer exactly what is required to formalise this processing. A new approach to the discretisation of geophysical domains is presented (Candy et al. [1], and [2] — [3]), and introduced with verified implementations. A complex domain example with a multi scale discretisation will be presented with scales ranging from 2000 km down to 5m details of the manmade structures of Portland harbour (Figure 1) and lagoon flats behind Chesil beach on the south coast (Figure 2). This brings together the fields of geospatial analysis, meshing and numerical simulation models. This platform enables us to combine and built up features, quickly drafting and updating mesh descriptions with the rigour that established GIS tools provide. This, combined with the systematic workflow, supports a strong provenance for model initialisation and encourages the convergence of standards. @en

Computational simulations of physical phenomena rely on an accurate discretisation of model domains. Numerical models have increased in sophistication to a level where it is possible to support terrain-following boundaries that conform accurately to real physical interfaces, and resolve a multi-scale of spatial resolutions. Whilst simulation codes are maturing in this area, pre-processing tools have not developed significantly enough to competently initialise these problems in a rigorous, efficient and recomputable manner. In the relatively disjoint field of Geographic Information Systems (GIS) however, techniques and tools for mapping and analysis of geographical data have matured significantly. In order to ensure model simulations are reproducible and the provenance of the data required is recorded, the manipulation and agglomeration of initialisation data needs to be standardised and automated. With the typical constraints on simulation domains for geophysical models consisting of bounding paths and surface scalar fields in a two dimensional plane, GIS frameworks potentially offer exactly what is required to formalise this processing. A new approach to the discretisation of geophysical domains is presented (Candy et al. [1], and [2] — [3]), and introduced with verified implementations. A complex domain example with a multi scale discretisation will be presented with scales ranging from 2000 km down to 5m details of the manmade structures of Portland harbour (Figure 1) and lagoon flats behind Chesil beach on the south coast (Figure 2). This brings together the fields of geospatial analysis, meshing and numerical simulation models. This platform enables us to combine and built up features, quickly drafting and updating mesh descriptions with the rigour that established GIS tools provide. This, combined with the systematic workflow, supports a strong provenance for model initialisation and encourages the convergence of standards. @en

Recent observational studies (Jenkins et al. 2010 [1] and Dutrieux et al. 2013 [2]) have helped to constrain estimates of the melt behaviour underneath Pine Island Glacier (PIG) ice shelf, in western Antarctica. Generally however, observations are limited, due to the relatively inaccessible and inhospitable environment. A solid ice cover, up to many kilometres thick, bars access to the water column, so that observational data can only be obtained by inference from above, drilling holes through, or launching autonomous vehicles beneath the ice. This is further exacerbated by the fact that results of these recent studies have implied a significant proportion of the melting (~80%) occurs in networks of sub-kilometre scale basal channels close to the grounding line, some of the most inaccessible parts of sub-ice shelf ocean cavities. Accurately representing these small-scale processes in conventional ocean models is a huge challenge even in focused regional studies, and will not be possible in global coupled climate simulations in the near future. I will present the development of a new model of PIG that is capable of resolving the range of scales necessary to evaluate the melt distribution and forming processes that dominate. This is built on the Fluidity model (Piggott et al. 2008 [8]) that simulates non-hydrostatic dynamics on meshes that, like the FESOM model of Timmermann et al. 2012 [4], can be unstructured. In this case, the grid can be unstructured in all three dimensions and use an anisotropic adaptive-in-time resolution to optimise the mesh and calculation in response to evolving solution dynamics. The parameterisation of melting in this model has been validated in idealised cavity domains (Kimura et al. 2013 [5]) and a validation is underway for the dynamic treatment of the ice-ocean interface (Candy et al. 2016 [7]). Additionally, the model is not limited to a specific vertical coordinate system and can capture purely vertical features, which enables it to accurately represent ice fronts, and small shallow features. I will discuss the development of this model of PIG; including the cavity domain, conforming to appropriately filtered boundaries generated from data collected during the British Antarctic Survey Autosub 2009 expedition, and the simulation of non-hydrostatic dynamics to date (see Figure 1). This will include validation to observations and MITgcm model results in a Circumpolar Deep Water forcing scenario from measurements in 2012, as recently presented in Science (Dutrieux et al. 2014 [3]). The unstructured nature of the developed model (Candy et al. 2016 [6]) captures the high spatial variation seen in melt rates in the small-scale channels, that its difficult to resolve in other fixed-mesh state-of-the-art models @en

A new approach to modelling free surface flows is developed that enables, for the first time, 3D consistent non-hydrostatic baroclinic physics that wets and drys in the large aspect ratio spatial domains that characterise geophysical systems. This is key in the integration of physical models to permit seamless simulation in a single consistent arbitrarily unstructured multiscale and multi-physics dynamical model. A high order continuum representation is achieved through a general Galerkin finite element formulation that guarantees local and global mass conservation, and consistent tracer advection. A flexible spatial discretisation permits conforming domain bounds and a variable spatial resolution, whilst atypical use of fully implicit time integration ensures computational efficiency. Notably this brings the natural inclusion of non-hydrostatic baroclinic physics and a consideration of vertical inertia to flood modelling in the full 3D domain. This has application in improving modelling of inundation processes in geophysical domains, where dynamics proceeds over a large range of horizontal extents relative to vertical resolution, such as in the evolution of a tsunami, or in urban environments containing complex geometric structures at a range of scales.@en

The Yucatan Channel connects the Caribbean Sea with the Gulf of Mexico and is the main outflow region of the Caribbean Sea. Moorings in the Yucatan Channel show high-frequent variability in kinetic energy (50–100 days) and transport (20–40 days), but the physical mechanisms controlling this variability are poorly understood. In this study, we show that the short-term variability in the Yucatan Channel transport has an upstream origin and arises from processes in the North Brazil Current. To establish this connection, we use data from altimetry and model output from several high resolution global models. A significant 40–70 day variability is found in the sea surface height in the North Brazil Current retroflection region with a propagation toward the Lesser Antilles. The frequency of variability is generated by intrinsic processes associated with the shedding of eddies, rather than by atmospheric forcing. This sea surface height variability is able to pass the Lesser Antilles, it propagates westward with the background ocean flow in the Caribbean Sea and finally affects the variability in the Yucatan Channel volume transport.@en

Renewable energy is the cornerstone of preventing dangerous climate change whilst main- taining a robust energy supply. Tidal energy will arguably play a critical role in the renewable energy portfolio as it is both predictable and reliable, and can be put in place across the globe. However, installation may impact the local and regional ecology via changes in tidal dynamics, sediment transport pathways or bathymetric changes. In or- der to mitigate these e ects, tidal energy devices need to be modelled in order to predict hydrodynamic changes. Robust mesh generation is a fundamental component required for developing simulations with high accuracy. However, mesh generation for coastal domains can be an elaborate procedure. Here, we describe an approach combining mesh generators with Geographical Information Systems. We demonstrate robustness and e - ciency by constructing a mesh with which to examine the potential environmental impact of a tidal turbine farm installation in the Orkney Islands. The mesh is then used with two well-validated ocean models, to compare their ow predictions with and without a turbine array. The results demonstrate that it is possible to create an easy-to-use tool to generate high-quality meshes for combined coastal engineering, here tidal turbines, and coastal ocean simulations. @en

Shallow tropical bays in the Caribbean, like Orient Bay and Galion Bay in Saint Martin, are often sheltered by coral reefs. In the relatively calm environment behind the reefs, seagrass meadows grow. Together, these ecosystems provide valuable ecosystem services like coastal protection, biodiversity hotspots, nursery grounds for animals and enhancing tourism and fisheries. However, sea-level rise imperils these ecosystems and the services they provide because of changing hydrodynamic conditions, with potential effects on the interdependencies between these ecosystems. By means of a hydrodynamic model that accounts for the interaction with vegetation (Delft3D Flexible Mesh), the impact of sea-level rise (0.87 m in 2100) is investigated for three scenarios of future reef development (i.e. keep-up, give-up and catch-up). If coral reefs cannot keep up with sea-level rise, the wave height and flow velocity increase significantly within associated bays, with the wave height doubling locally in case of eroding reefs in our model simulations. Since the presence of seagrass strongly depends on the hydrodynamic conditions, the response of seagrass to the future hydrodynamic conditions is projected using a habitat suitability model that is based on a logistic regression. The spatial character of the bays determines the response of seagrass. In Orient Bay, which is deeper and partly exposed to higher waves, the seagrass will likely migrate from the deeper parts to shallow areas that become suitable for seagrass because of the surf zone moving landward. In contrast, the conditions for seagrass worsen in Galion Bay for the catch-up and give-up scenario; due to the shallowness of this bay, the seagrass cannot escape to more suitable areas, resulting in significant seagrass loss. It is shown that healthy coastal ecosystems are able to limit the change in hydrodynamic conditions due to sea-level rise. Therefore, preserving these ecosystems is key for ensuring the resilience of shallow tropical bays to sea-level rise and maintaining their ecosystem services.@en

This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.@en

This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.@en

Jamaica is one of the few remaining countries in the Caribbean region with an abundant population of Lobatus gigas (queen conch) able to sustain a lucrative fishery. Efforts to understand and maintain queen conch populations must involve an investigation into genetic connectivity. This connectivity facilitates population replenishment and continuity via the transport of veliger larvae by ocean currents. Due to the lack of knowledge in this regard to queen conch populations in Jamaica, the fine-scale population structure of Lobatus gigas populations in the country's Exclusive Economic Zone (EEZ) has been analysed by comparing the allele frequencies of nine microsatellite loci on a total of 459 individuals collected across twelve sites encompassing nearshore and offshore locations. Samples were grouped into five broad scale geographic clusters for statistical analysis. Our findings indicate that a weak but significant population structure exists (Global Fst = 0.004, p = 0.01) suggesting that mainland Jamaica acts as a weak divide between populations north and south of the island. Greater levels of connectivity are suggested between north coast populations and those present at the Formigas Bank, an offshore site northeast of the island. The island's primary conch fishing ground located offshore on Pedro Bank, receives limited gene flow from the other sampled populations and may be heavily dependent on local recruitment or receive recruits from sources external to Jamaica's EEZ. An analysis of surface ocean currents strongly supports these three findings and further that conch populations on Pedro Bank very likely receive recruits from sources distinct to those that supply nearshore populations. Further genetic studies into the recruitment patterns and sources for the community on Pedro Bank are therefore critical to ensure sustainable management of this commercially threatened population. Decades of intense fishing pressure has resulted in the establishment of the Allee effect on the island shelf, significantly hampering reproduction and consequently recruitment. If the question of recruitment on Pedro Bank is not addressed, further development of the Allee effect there and eventual population exhaustion are inevitable. These findings, their implications and recommendations for the management of the queen conch fishery in Jamaica are discussed.@en

Jamaica is one of the few remaining countries in the Caribbean region with an abundant population of Lobatus gigas (queen conch) able to sustain a lucrative fishery. Efforts to understand and maintain queen conch populations must involve an investigation into genetic connectivity. This connectivity facilitates population replenishment and continuity via the transport of veliger larvae by ocean currents. Due to the lack of knowledge in this regard to queen conch populations in Jamaica, the fine-scale population structure of Lobatus gigas populations in the country's Exclusive Economic Zone (EEZ) has been analysed by comparing the allele frequencies of nine microsatellite loci on a total of 459 individuals collected across twelve sites encompassing nearshore and offshore locations. Samples were grouped into five broad scale geographic clusters for statistical analysis. Our findings indicate that a weak but significant population structure exists (Global Fst = 0.004, p = 0.01) suggesting that mainland Jamaica acts as a weak divide between populations north and south of the island. Greater levels of connectivity are suggested between north coast populations and those present at the Formigas Bank, an offshore site northeast of the island. The island's primary conch fishing ground located offshore on Pedro Bank, receives limited gene flow from the other sampled populations and may be heavily dependent on local recruitment or receive recruits from sources external to Jamaica's EEZ. An analysis of surface ocean currents strongly supports these three findings and further that conch populations on Pedro Bank very likely receive recruits from sources distinct to those that supply nearshore populations. Further genetic studies into the recruitment patterns and sources for the community on Pedro Bank are therefore critical to ensure sustainable management of this commercially threatened population. Decades of intense fishing pressure has resulted in the establishment of the Allee effect on the island shelf, significantly hampering reproduction and consequently recruitment. If the question of recruitment on Pedro Bank is not addressed, further development of the Allee effect there and eventual population exhaustion are inevitable. These findings, their implications and recommendations for the management of the queen conch fishery in Jamaica are discussed.@en

Jamaica is one of the few remaining countries in the Caribbean region with an abundant population of Lobatus gigas (queen conch) able to sustain a lucrative fishery. Efforts to understand and maintain queen conch populations must involve an investigation into genetic connectivity. This connectivity facilitates population replenishment and continuity via the transport of veliger larvae by ocean currents. Due to the lack of knowledge in this regard to queen conch populations in Jamaica, the fine-scale population structure of Lobatus gigas populations in the country's Exclusive Economic Zone (EEZ) has been analysed by comparing the allele frequencies of nine microsatellite loci on a total of 459 individuals collected across twelve sites encompassing nearshore and offshore locations. Samples were grouped into five broad scale geographic clusters for statistical analysis. Our findings indicate that a weak but significant population structure exists (Global Fst = 0.004, p = 0.01) suggesting that mainland Jamaica acts as a weak divide between populations north and south of the island. Greater levels of connectivity are suggested between north coast populations and those present at the Formigas Bank, an offshore site northeast of the island. The island's primary conch fishing ground located offshore on Pedro Bank, receives limited gene flow from the other sampled populations and may be heavily dependent on local recruitment or receive recruits from sources external to Jamaica's EEZ. An analysis of surface ocean currents strongly supports these three findings and further that conch populations on Pedro Bank very likely receive recruits from sources distinct to those that supply nearshore populations. Further genetic studies into the recruitment patterns and sources for the community on Pedro Bank are therefore critical to ensure sustainable management of this commercially threatened population. Decades of intense fishing pressure has resulted in the establishment of the Allee effect on the island shelf, significantly hampering reproduction and consequently recruitment. If the question of recruitment on Pedro Bank is not addressed, further development of the Allee effect there and eventual population exhaustion are inevitable. These findings, their implications and recommendations for the management of the queen conch fishery in Jamaica are discussed.@en

In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model.@en

The Dutch Caribbean consists of two island groups, the Leeward Antilles off the Venezuelan coast separated from the Windward Islands east of Puerto Rico over distances of the scale of the Caribbean Sea itself. Climate change in the Caribbean Sea is predicted to lead to rising sea levels, warming waters and changing eddy fields. Warming waters lead to an increase in the intensity and occurrence of tropical storms and hurricanes, and are linked to an increased risk of surge flooding. Changing eddy fields are likely to affect the path of storm tracks. All of which further influence the environment of the Caribbean, and hence the stability of its ecosystems.@en