Control of spatial discretisation in coastal oil spill modelling

Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Search” method based on GIS zone design principles is shown to significantly improve discretisation of bathymetric data and hydrodynamic modelling outputs. The concepts and methods developed in the study are expected to have general relevance for a range of applications in numerical environmental modelling

Spatial Discretisation Technology in Coastal Oil Spill Modelling

Spatial discretisation plays an important role in many numerical environmental models. This paper studies the technology of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “automatic search” method based on GIS zone design principles is shown to significantly improve discretisation of bathymetric data and hydrodynamic modelling outputs. The concepts and methods developed in the study are expected to have general relevance for a range of applications in numerical environmental modelling

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

Proceedings of the XIXth TELEMAC-MASCARET User Conference 2012, 18 to 19 October 2012, St Hugh's College, Oxford

Water Qualit

Autonomous Monitoring of Contaminants in Fluids

The litigation and mitigation of maritime incidents suffer from a lack of information, first at the incident location, then throughout the evolution of contaminants such as spilled oil through the surrounding environment. Prior work addresses this through ocean and oil models, model directed sensor guidance and other observation methods such as satellites. However, each of these approaches and research fields have short-comings when viewed in the context of fast-response to an incident, and of constructing an all-in-one framework for monitoring contaminants using autonomous mobile sensors. In summary, models often lack consideration of data-assimilation or sensor guidance requirements, sensor guidance is specific to source locating, oil mapping, or fluid measuring and not all three, and data assimilation methods can have stringent requirements on model structure or computation time that may not be feasible. This thesis presents a model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. In the first of a four-stage process, simulation of a combined ocean, wind and oil model provides a state trajectory over a finite time horizon, used in the second stage to solve an adjoint optimisation problem for sensing locations. In the third stage, a reduced-order model is identified from the state trajectory, utilised alongside measurements to produce smoothed state estimates in the fourth stage, which update and re-initialise the first-stage simulation. In the second stage, sensors are directed to optimal sensing locations via the solution of a Partial Differential Equation (PDE) constrained optimisation problem. This problem formulation represents a key contributory idea, utilising the definition of spill uncertainty as a scalar PDE to be minimised subject to sensor, ocean, wind and oil constraints. Spill uncertainty is a function of uncertainty in (i) the bespoke model of the ocean, wind and oil spill, (ii) the reduced order model identified from sensor data, and (iii) the data assimilation method employed to estimate the states of the environment and spill. The uncertainty minimisation is spatio-temporally weighted by a function of spill probability and information utility, prioritising critical measurements. In the penultimate chapter, numerical case-studies spanning a 2500 km2 coastal area are presented. Here the monitoring framework is compared to an industry standard method in three scenarios: A spill monitoring and prediction problem, a retrodiction and monitoring problem and a source locating problem

Hydrodynamic and sediment transport numerical modelling and applications at Tairua Estuary, New Zealand

Tairua Estuary is a partially mixed estuary located on the east coast of the Coromandel Peninsula, North Island, New Zealand. Like many estuaries worldwide, Tairua Estuary is experiencing rapid sedimentation, which is causing a range of environmental and management issues. This study was undertaken to develop a combined hydrodynamic and sediment transport numerical model as a tool for improving management of sedimentation issues within Tairua Estuary. Two field campaigns were undertaken in July 2010 and June 2011 to obtain calibration and verification datasets for two suites of numerical models – DHI MIKE and ASR 3DD – that were both used for hydrodynamic and sediment transport simulations to allow comparisons of the model suites. Additional models were used to simulate additional processes such as oil dispersal. Observations and numerical modelling showed the tidal wave was distorted as it propagated into the Tairua Estuary, becoming increasingly asymmetrical with distance from the entrance. The tidal wave underwent more distortion during spring tides than during neap tides, resulting in the influx of more oceanic water in the upper Tairua Estuary during a spring tidal cycle, causing a greater increase of salinity. The changing tidal distortion with tidal range also resulted in spring tides being flood dominated and neap tides being ebb dominated. The fate of fine sediments introduced into the lower estuary was, therefore, dependant on the state of the spring-neap cycle, with spring tides favouring deposition in the upper estuary, and neap tides favouring export to the continental shelf. The effects of tidal behaviour in the estuary were modified by river discharge, with increasing discharge resulting in increased ebb dominance and export of sediment from the estuary. Further, as river discharge increased, the estuary became more stratified, particularly during periods of low tidal velocities. However, areas of Tairua Estuary with tidal current velocities higher than 0.5 m s-1, mostly around the tidal inlet, remained partially mixed, even when the river discharge reached a peak value of 200 m3 s-1. During flood events, the upper part of estuary becomes highly stratified due to the large increase in freshwater discharge. Observations and numerical modelling showed that instabilities can develop in the resulting pycnocline in response to wind forcing and fluctuations in flood discharge, and these propagate as forced seiches within the estuary. The seiches interact with the turbid floodwaters and the underlying salt wedge to influence the locations where fine sediment is deposited within the estuary, with enhanced deposition at the nodes of the seiches. Sediment transport modelling indicated that suspended sediment from the river and sediment eroded from the estuary bed, primarily is transported seaward along the main channel of the estuary and through the northern side of the tidal inlet. Subsequently, coarser suspended sediment tends to deposit on the terminal lobe of the ebb tidal delta due to lower current speeds. Meanwhile, sediment suspended along Pauanui Beach by wave action enters the estuary along the southern side of the tidal inlet, and this sediment is mostly deposited on the flood tidal delta. The interaction of the sediment transport entering the estuary and exiting the estuary forms a large eddy over the ebb tidal delta, which acts as a sediment deposition-centre. The model results were consistent with the field observations. The numerical models were calibrated against one field dataset and verified against the second using a variety of statistical measures for the goodness of fit. The results were characterised as excellent for elevation changes over most of the estuary, apart from the elevation for the Tairua River channel around the limit of tidal influence. The calibration and verification of current velocities resulted in a range of results from reasonable to excellent, depending on the numerical grid resolution and the complexity of the local bathymetry. The finest 20 m grids produced the best results, with some minor problems with the current velocity directions along the seaward boundary of the models. Overall the MIKE software suite was easier to use and ran faster, but the 3DD suite produced better results for shallow areas with narrow channels in the upper estuary. Once calibrated and verified, the models were used to simulate a range of scenarios requested by the Waikato Regional Council. These included assessing the impact of potential sea level rise, development including channel realignment and marina construction, and the effects of oil spills within the estuary. As sea level rises, the estuary is predicted to become increasingly flood dominated, which would result in greater sediment transport into the estuary from the Pauanui Beach system, and hence, subsequent deposition on the intertidal flats. Effectively, sea level rise would reverse the normal sequence of estuarine evolution, turning the clock back towards a more youthful estuary. It is also likely that saltwater intrusion was more frequent with increased sea level. However, the influx of sediment would also compensate for some of the sea level rise, and reduce the tendency for flood dominance

Proceedings of the XVIIIth Telemac & Mascaret User Club 2011, 19-21 October 2011, EDF R&D, Chatou

Water Qualit

Bayesian estimation and reconstruction of marine surface contaminant dispersion

Discharge of hazardous substances into the marine environment poses a substantial risk to both public health and the ecosystem. In such incidents, it is imperative to accurately estimate the release strength of the source and reconstruct the spatio-temporal dispersion of the substances based on the collected measurements. In this study, we propose an integrated estimation framework to tackle this challenge, which can be used in conjunction with a sensor network or a mobile sensor for environment monitoring. We employ the fundamental convection-diffusion partial differential equation (PDE) to represent the general dispersion of a physical quantity in a non-uniform flow field. The PDE model is spatially discretised into a linear state-space model using the dynamic transient finite-element method (FEM) so that the characterisation of time-varying dispersion can be cast into the problem of inferring the model states from sensor measurements. We also consider imperfect sensing phenomena, including miss-detection and signal quantisation, which are frequently encountered when using a sensor network. This complicated sensor process introduces nonlinearity into the Bayesian estimation process. A Rao-Blackwellised particle filter (RBPF) is designed to provide an effective solution by exploiting the linear structure of the state-space model, whereas the nonlinearity of the measurement model can be handled by Monte Carlo approximation with particles. The proposed framework is validated using a simulated oil spill incident in the Baltic sea with real ocean flow data. The results show the efficacy of the developed spatio-temporal dispersion model and estimation schemes in the presence of imperfect measurements. Moreover, the parameter selection process is discussed, along with some comparison studies to illustrate the advantages of the proposed algorithm over existing methods

A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column

A common task in oceanography is to model the vertical movement of particles such as microplastics, nanoparticles, mineral particles, gas bubbles, oil droplets, fish eggs, plankton, or algae. In some cases, the distribution of vertical rise or settling velocities of the particles in question can span a wide range, covering several orders of magnitude, often due to a broad particle size distribution or differences in density. This requires numerical methods that are able to adequately resolve a wide and possibly multi-modal velocity distribution. Lagrangian particle methods are commonly used for these applications. strength of such methods is that each particle can have its own rise or settling speed, which makes it easy to achieve a good representation of a continuous distribution of speeds. An alternative approach is to use Eulerian methods, where the partial differential equations describing the transport problem are solved directly with numerical methods. In Eulerian methods, different rise or settling speeds must be represented as discrete classes, and in practice only a limited number of classes can be included. Here, we consider three different examples of applications for a water-column model: positively buoyant fish eggs, a mixture of positively and negatively buoyant microplastics, and positively buoyant oil droplets being entrained by waves. For each of the three cases we formulate a model for the vertical transport, based on the advection-diffusion equation with suitable boundary conditions and in one case a reaction term. We give a detailed description of an Eulerian and a Lagrangian implementation of these models, and we demonstrate that they give equivalent results for selected example cases. We also pay special attention to the convergence of the model results with increasing number of classes in the Eulerian scheme, and the number of particles in the Lagrangian scheme. For the Lagrangian scheme, we see the 1/√Np convergence as expected for a Monte Carlo method, while for the Eulerian implementation, we see a second order (1/N2k) convergence with the number of classes