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There is currently worldwide pressure to establish Marine Protected Area (MPA) networks which are self-sustaining and will persistently protect habitats and species. In order for MPA networks to be effective, the species targeted for conservation must be able to disperse between protected areas and maintain a gene-flow necessary for population sustainability and persistence. This warrants new research on how to quantify and map faunal dispersal to ensure that protection will be effective and sustainable. Population genetic methods have merit, with the ability to track parentage and gene flow between areas directly. However the costs, quantity of samples, and time required to genetically quantify dispersal for multiple species make these approaches prohibitive as the only method of assessment, especially in relatively inaccessible offshore waters. Dispersal modelling is now becoming more accessible and may fulfil immediate needs in this field (although ground truthing will be necessary in the future). There have been very few dispersal modelling studies focussed on deep sea or offshore areas, predominantly due to the lack of high resolution hydrodynamic models with sufficient geographic extent away from shore. Current conclusions have been drawn based on shallow water coastal studies, informing offshore MPA network size and spacing. However the differences between these two environments may mean that dispersal abilities are not comparable. Deep water receives less influence from wind and weather, and the scales are vastly different in terms of a) the depth ranges covered, b) the planktonic larval durations (PLDs) of animals, and c) the geographic areas concerned as a consequence. Global hydrodynamic models with reasonable resolution are now becoming more accessible. With the outputs from these models, and freely available particle simulators, it is becoming more practical to undertake offshore deep water dispersal studies. This thesis aims to undertake an analysis of these accessible modelling tools within a deep sea context. The guidelines which are currently available to dispersal modellers are yet to encompass the needs of deep water modellers which may require some additional considerations given the extended depth range covered and the different hydrodynamic drivers away from the air/sea interface. Chapter 1 reviews the larval dispersal process, the factors which may affect dispersal success, and those which should be incorporated into future predictions of dispersal. The current methods for assessing larval dispersal are explored covering genetics, elemental tagging and modelling approaches with an extended look at modelling considerations. Existing marine conservation policy is also touched on in the context of connectivity and larval dispersal. Chapter 2 is designed to inform future deep sea modellers on how to parameterise and understand a dispersal model. As models appear as a ‘black box’ to the majority of users, sensitivity tests can offer a way of scaling model inputs and tempering expectations from model outputs. A commonly used model pairing (the HYCOM hydrodynamic model and the Connectivity Modeling System) is assessed, using parameters which link to the temporal and spatial scales of mixing in the modelled system: timestep of particle tracer, horizontal and vertical positioning of release points, release frequency of larvae, and temporal range of simulation. All parameters were shown to have a decreased sensitivity with depth, with patterns reflecting local watermass structure. Future studies observing similar hydrodynamic conditions seeking to optimise their model set up would be advised to stratify their model release locations with depth. A means to incorporate all sensitivity test results into optimal input parameters for future studies is demonstrated. Chapter 3 investigates whether dispersal models provide any advantage over a “sphere of influence” estimate based on average current speeds and PLDs: there is no use pursuing dispersal modelling if the outputs are too erroneous to provide any advantage over a back-of-the-envelope calculation. This chapter examines the outputs of two dispersal models driven by two different hydrodynamic models in order to observe the variability in prediction between models. This model comparison revealed a greater disparity between hydrodynamic model predictions than has been previously understood by ecologists. The two models compared (POLCOMS and HYCOM) may equally be considered as suitable to promote realism in the study region, but slight differences in resolution and numerical error handling resulted in dispersal predictions from which opposing conclusions can be drawn. This chapter therefore emphasises the necessity for model ground truthing before predictions can be trusted. Chapter 4 assimilates the findings of the previous chapters and applies their advice to a study of MPA network dispersal connectivity. Using the hydrodynamic model which performed best in chapter 3 (HYCOM), a simulation was undertaken for cold water coral (Lophelia pertusa (Linnaeus 1758)) larval dispersal between already established MPAs in the NE Atlantic. As larval characters have only been observed ex situ, dispersal was simulated using two null models (passive and active vertical migration) and averaged to provide an intermediate prediction. A method for assessing dispersal within MPAs and MPA networks is offered based on the intermediate prediction, as well as a network wide assessment of the difference in dispersal patterns for passive and active larvae. It was found that the existing network performs well at supplying larvae to non-networked sites, but performs poorly at supplying other MPAs. The ‘best’ MPAs were central to the network and facilitated the traverse of regional gaps in suitable habitat. The ‘worst’ MPAs were peripheral to the network and small in size. Network-wide passive and active dispersal matrices had no significant difference between them. However site specific variability in the effect of vertical migration was detected subject to variability in local topographic barriers to dispersal, only some of which could be surmounted with vertical migration. All chapters aim to inform future deep sea dispersal modellers, and encourage exploration of this tool in other contexts, as well as marine conservation. The thesis cautions against the transplantation of shallow water assumptions to deep water environments, and advocates region specific studies and mandatory ground truthing of predictions. An upcoming study will ground truth the findings of this thesis with both genetic and oceanographic data, allowing the accuracy of study results to be quantified.

Heat-release in CI engines occurs in the presence of concentration and temperature gradients. Recognizing the need for a validation of chemical kinetic models in transport-affected systems, this study employs non-premixed systems to better understand complex couplings between low/high temperature oxidation kinetics and diffusive transport. This dissertation is divided into two sections. In the first section, a two-stage Lagrangian model compares model prediction of ignition delay time and experimental data from the KAUST ignition quality tester, and ignition data for liquid sprays in constant volume combustion chambers. The TSL employed in this study utilizes detailed chemical kinetics while also simulating basic mixing processes. The TSL model was found to be efficient in simulating IQT in long ignition delay time fuels; it was also effective in CVCC experiments with high injection pressures, where physical processes contributed little to ignition delay time. In section two, an atmospheric pressure counterflow burner was developed and fully validated. The counterflow burner was employed to examine the effects of molecular structure on low/high temperature reactivity of various fuels in transport-affected systems. These effects were investigated through measurement of conditions of extinction and ignition of various fuel/oxidizer mixtures. Data generated were used to validate various chemical kinetic models in diffusion flames. Where necessary, suggestions were made for improving these models. For hot flames studies, tested fuels included C3-C4 alcohols and six FACE gasoline fuels. Results for alcohols indicated that the substituted alcohols were less reactive than the normal alcohols. The ignition temperature of FACE gasoline was found to be nearly identical, while there was a slight difference in their extinction limits. Predictions by Sarathy et al. (2014) alcohol combustion model, and by the gasoline surrogate model (Sarathy et al., 2015), agreed with the experimental data. For cool diffusion flames studies, tested fuels included butane isomers, naphtha, gasolines and their surrogates. Results revealed that the addition of ozone successfully established cool flames in the fuels at low and moderate strain rates. Numerical simulations were performed to replicate the extinction limits of the cool flames of butane isomers. The model captured experimental trends for both fuels; but over-predicted their extinction limits.

In a field of environmental protection there is a very important question about the options to determine impact of different pollution sources on air quality in areas more or less distant from those sources. For those predictions we can use physical or computer modelling. In this paper a computer model (Lagrangian Dispersion Model, or LDM) of air pollution propagation is developed and described. The LDM was created in order to work within the CLMM - Charles University Large-Eddy Microscale Model. In this paper we discuss theory of those models as well as technical solutions used to develop the LDM. The model is validated and subsequently applied on several cases with different degree of geometry complexity. Powered by TCPDF (www.tcpdf.org)

Groundwater abstraction for irrigation use has steadily increased over the past decades, resulting in additional evaporation to the atmosphere, and increased precipitation. The precipitation stemming from groundwater irrigation (or Groundwater-dependent precipitation) has received little attention during recent years and is solely researched by the Eulerian model. This study aims to provide a supplement and improvement of the global fate of groundwater-dependent precipitation with the Lagrangian model outcome. The analysis combines the UTrack model output between 2008 to 2017, a global groundwater irrigation area map, groundwater abstraction from PCR-GLOBWB version 1, and groundwater irrigation efficiency to generate the global groundwater-dependent precipitation trajectory from 2001 to 2010. The primary assumption is that atmospheric factors do not change significantly in all pressure levels during 2001 – 2010 and 2008 – 2017. The simulation result shows that groundwater-dependent precipitation is generally more substantial in Asia than in other continents. Bhutan, Bangladesh, Nepal, India, Yemen, and Afghanistan are the top six countries receiving high groundwater-dependent precipitation contributions monthly and yearly. Moreover, groundwater-dependent precipitation in the continent and country shows a significant seasonal change in the monthly average. A country or continent with a high groundwater abstraction does not necessarily receive a massive amount of groundwater-dependent precipitation regardless of monthly and yearly scale. For instance, China has a yearly average groundwater abstraction of 100 km3 year-1 but receives less than 1% groundwater-dependent precipitation contribution per year. Approximately 75% of groundwater-dependent precipitation falls into the land, and 25% ends in the ocean from 2001 to 2010. The groundwater-dependent precipitation does not significantly contribute to land and ocean, with 0.16% and 0.015%, respectively. Consequently, the study suggests groundwater-dependent precipitation does not have a greater effect on downwind area precipitation on a yearly scale but a larger effect during a specific month. The highest monthly average groundwater-dependent contribution is 18% in January, whereas the highest yearly groundwater-dependent contribution is 2.5% in 2006. Major regions with high groundwater-dependent precipitation contributions are found along the Himalayas Range from January to April and moving eastward to Arabic Peninsula in July.

Made available in DSpace on 2016-12-23T14:04:33Z (GMT). No. of bitstreams: 1 Henery Ferreira Garcao.pdf: 1679065 bytes, checksum: 8e3210947a7d17cb14363973810da116 (MD5) Previous issue date: 2010-08-31 / A modelagem computacional é uma importante ferramenta para estimar a trajetória e destino final de manchas de óleo em diferentes condições ambientais, visto a complexidade dos processos que atuam nesse poluente. O presente trabalho concentrou os esforços no desenvolvimento de um modelo lagrangiano de trajetória de partículas que simule o movimento de manchas de óleo em ambiente marinho. O modelo utilizado é o Modelo Lagrangiano de Partículas com Deslocamento Aleatório (MLPDA), que é baseado na equação de Langevin. Em princípio, o algoritmo da advecção da mancha de óleo devido ao vento é implementado no MLPDA, visto sua importância ao deslocamento das partículas. É considerado que 3% da velocidade do vento a 10 metros de altura permite uma boa representação da deriva de manchas de óleo em ambiente marinho. Os testes para este algoritmo apresentaram resultados satisfatórios. Posteriormente, é implementado um algoritmo que representa o processo físico de espalhamento do óleo, conhecido também por espalhamento mecânico, que é definido como o movimento horizontal devido às forças gravitacionais, viscosas e inerciais. No presente estudo, esse processo é fundamentando nas equações definidas por Lehr et al. (1984), onde os resultados dos testes mostraram que as partículas espalham conforme exposto por esse mesmo autor e são influenciadas até cerca de 100 h de simulação. Ainda neste estudo, é avaliado o módulo de cálculo de área implementado no MLPDA. É advertido que malhas grosseiras podem resultar em áreas superestimadas, sendo aconselhável o uso de malhas mais refinadas para o cálculo dessas áreas. Por fim, três cenários de simulação de um derrame hipotético de óleo na Baía do Espírito Santo, no interior do Porto de Tubarão, são conduzidos para ilustrar uma aplicação do modelo desenvolvido. As simulações expõem que há grandes diferenças entre os resultados obtidos, principalmente entre o cenário que desconsidera o vento e os outros dois com a consideração desta forçante. O primeiro cenário, as partículas tenderam a permanecer na Baía do Espírito Santo, enquanto para os demais cenários as partículas caminharam para os canais do sistema estuarino da Grande Vitória (Canal da Passagem e Canal de Acesso aos Portos). / The computational modeling is an important tool to predict the trajectory and fate of the slick oil in different environmental conditions, since the complexity of processes involving oil spill. Thus, the present study has concentrated efforts on developing of a particle tracking lagrangian model that simulate the oil slick movement in the marine environment. The model used is Lagrangian Particles Random Walk Model (MLPDA), that it is based on the Langevin equation. First, the algorithm of the advection of the oil slick due to wind is implemented in the Random Walk Particle Lagrangian Model (MLPDA), seen its importance to the displacement of particles. It is considered that 3% of the wind velocity at 10 meters height allows a good representation of the drift of the slicks. The tests for this algorithm presented satisfactory results. Posteriorly, is implemented an algorithm that represents the physical process of spreading, also known as mechanic spreading, that is defined as the horizontal movement due to gravitational, viscous and inertial forces. In the present study, this process is based on the equations defined by Lehr et al. (1984), where the results of the tests showed that the particles spread as shown by this author and they are influenced up to 100 hours of simulation. In addition, it is evaluate the module for calculation the area implemented in MLPDA. It is adverted that very coarse grid may result in overrated areas, being advisable to use fine grid for calculation of these areas. Finally, three scenarios of simulation of a hypothetic oil spill at the Espírito Santo Bay, in the Tubarão Port, are conducted to illustrate an application of the model development. The simulations show large differences among the results obtained, mainly among the scenario that neglect the wind and the other two with the consideration of this forcing. The first scenario, the particles tended to remain at the Espírito Santo Bay, while other scenarios the particles walked to the channels of the Great Vitória estuarine system (Passage Channel and Access Channel to Ports).