Academic literature on the topic 'Lagrangian dispersal models'

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.

Parmi les outils qui permettent de mieux comprendre les systèmes naturels, la modélisation écologique a connu un essor particulièrement important depuis une vingtaine d’années. Les modèles écologiques, représentation simplifiée d’une réalité complexe, permettent de mettre en avant les facteurs environnementaux qui déterminent la niche écologique des espèces et de mieux comprendre leur réponse aux changements de l’environnement. Dans le cas des faunes marines antarctiques, la modélisation écologique fait face à plusieurs défis méthodologiques. Les jeux de données de présence des espèces sont très souvent agrégés dans le temps et dans l’espace, à proximité des stations de recherche. Ces données sont souvent trop peu nombreuses pour caractériser l’espace environnemental occupé par les espèces ainsi que leur physiologie. Enfin, les jeux de données environnementales manquent encore de précision pour finement représenter la complexité des habitats marins. Dans ces conditions, est-il possible de générer des modèles performants et justes à l’échelle de l’océan Austral ? Quelles sont les approches possibles et leurs limites ? Comment améliorer les méthodes afin de générer de meilleurs modèles ? Au cours de ce travail de thèse, trois types de modèles ont été étudiés et leurs performances évaluées. (1) Les modèles physiologiques de type DEB (Dynamic Energy Budget) simulent la manière dont l’environnement abiotique influe sur le métabolisme des individus et proposent une représentation de la niche fondamentale des espèces. (2) Les modèles de distribution d’espèces (SDMs pour Species Distribution Models) prédisent la probabilité de distribution des espèces en étudiant la relation spatiale entre données de présence et environnement. Ils proposent une représentation de la niche réalisée des espèces. Enfin (3), les modèles de dispersion de type lagrangien prédisent le mouvement de propagules dans les masses d’eau. Les résultats montrent que les modèles physiologiques réussissent à simuler les variations métaboliques des espèces antarctiques en fonction de l’environnement et à prédire les dynamiques de populations. Cependant, davantage de données sont nécessaires pour pouvoir caractériser finement les différences physiologiques entre populations et évaluer correctement les modèles. Les résultats obtenus pour les SDMs montrent que les modèles générés à l’échelle de l’océan Austral et leurs prédictions futures ne sont pas fiables du fait du manque de données disponibles pour caractériser l’espace occupé par les espèces, du manque de précision des scénarios climatiques futurs et de l’impossibilité d’évaluer les modèles. De plus, les modèles extrapolent sur une très grande proportion de l’espace projeté. L’apport d’information complémentaire sur les limites physiologiques des espèces (observations, résultats d’expériences, sorties de modèles physiologiques) permet de réduire l’extrapolation et d’augmenter la capacité des modèles à décrire la niche réalisée des espèces. L’agrégation spatiale des données, qui influençait les prédictions et l’évaluation des modèles a également pu être corrigée. Enfin, les modèles de dispersion ont montré un potentiel intéressant pour révéler le rôle des barrières géographiques ou à l’inverse, la connectivité spatiale, mais également le lien existant entre distribution, physiologie et histoire phylogénétique des espèces. Ce travail de thèse propose de nombreux conseils et fournit des codes annotés parfois sous forme de tutoriels, afin de constituer une aide utile aux futurs travaux de modélisation sur les espèces marines antarctiques<br>Among tools that are used to fill knowledge gaps on natural systems, ecological modelling has been widely applied during the last two decades. Ecological models are simple representations of a complex reality. They allow to highlight environmental drivers of species ecological niche and better understand species responses to environmental changes. However, applying models to Southern Ocean benthic organisms raises several methodological challenges. Species presence datasets are often aggregated in time and space nearby research stations or along main sailing routes. Data are often limited in number to correctly describe species occupied space and physiology. Finally, environmental datasets are not precise enough to accurately represent the complexity of marine habitats. Can we thus generate performant and accurate models at the scale of the Southern Ocean ? What are the limits of such approaches ? How could we improve methods to build more relevant models ? In this PhD thesis, three different model categories have been studied and their performance evaluated. (1) Mechanistic physiological models (Dynamic Energy Budget models, DEB) simulate how the abiotic environment influences individual metabolism and represent the species fundamental niche. (2) Species distribution models (SDMs) predict species distribution probability by studying the relationship between species presences and the environment. They represent the species realised niche. (3) Dispersal lagrangian models predict the drift of propagules in water masses. Results show that physiological models can be developed for marine Southern Ocean species to simulate the metabolic variations in link with the environment and predict population dynamics. However, more data are necessary to highlight detailed physiological contrasts between populations and to accurately evaluate models. Results obtained for SDMs suggest that models generated at the scale of the Southern Ocean and future simulations are not relevant, given the lack of data available to characterise species occupied space, the lack of precision and accuracy of future climate scenarios and the impossibility to evaluate models. Moreover, model extrapolate on a large proportion of the projected area. Adding information on species physiological limits (observations, results from experiments, physiological model outputs) was shown to reduce extrapolation and to improve the capacity of models to estimate the species realised niche. Spatial aggregation of occurrence data, which influenced model predictions and evaluation was also succefully corrected. Finally, dispersal models showed an interesting potential to highlight the role of geographic barriers or conversely of spatial connectivity and also the link between species distribution, physiology and phylogeny history. This PhD thesis provides methodological advices, annoted codes and tutorials to help implement future modelling works applied to Southern Ocean marine species

Dispersal, especially long-distance dispersal, is an important component in many disciplines within biology. Many species are passively dispersed by wind, not least spore-dispersed organisms. In this thesis I investigated the dispersal capacity of bryophytes by studying the colonization patterns from local scales (100 m) to landscape scales (20 km). The dispersal distances were measured from a known source (up to 600 m away) or inferred from a connectivity measure (1–20 km). I introduced acidic clay to measure the colonization rates over one season of a pioneer moss, Discelium nudum (I–III). I also investigated which vascular plants and bryophytes that had colonized limed mires approximately 20–30 years after the first disturbance (IV). Discelium effectively colonized new disturbed substrates over one season. Most spores were deposited up to 50 meters from a source but the relationship between local colonization rates and connectivity increased with distance up to 20 km (I–III). Also calcicolous wetland bryophyte species were good colonizers over similar distances, while vascular plants in the same environment colonized less frequently. Common bryophytes that produce spores frequently were more effective colonizers, while no effect of spore size was detected (IV). A mechanistic model that take into account meteorological parameters to simulate the trajectories for spores of Discelium nudum fitted rather well to the observed colonization pattern, especially if spore release thresholds in wind variation and humidity were accounted for (III). This thesis conclude that bryophytes in open habitats can disperse effectively across landscapes given that the regional spore source is large enough (i.e. are common in the region and produce spores abundantly). For spore-dispersed organisms in open landscapes I suggest that it is often the colonization phase and not the transport that is the main bottle-neck for maintaining populations across landscapes.<br><p>At the time of the doctoral defence the following papesr were unpublished and had  a status as follows: Paper 2: Epubl ahead of print; Paper 3: Manuscript; Paper 4: Manuscript</p>

Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>This study employed different autocorrelation functions and Maclaurin series expansions in the derivation of expressions describing the dissipation rate of turbulent kinetic energy. These expressions have the same functional form, but are described in terms of different numerical coefficients. The values obtained for the numerical coefficients were used in a Lagrangian stochastic dispersion model to simulate the dispersion of contaminants in the Planetary Boundary Layer (PBL). The simulation results were compared with concentration data observed in the Copenhagen experiment. The good performance of the parameterization and analysis through statistical indices showed that the mathematical relationships that describe the turbulent dissipation rate present an uncertainty. The analysis developed in this study indicates that there is no a universal functional form describing the dissipation rate of turbulent energy.<br>Neste estudo foram empregadas diferentes funções de autocorrelação e expansões em série de Maclaurin na derivação de expressões que descrevem a taxa de dissipação da energia cinética turbulenta. Estas expressões apresentam a mesma forma funcional, porém são descritas em termos de diferentes coeficientes numéricos. Os valores obtidos para os coeficientes numéricos foram empregados em um modelo de dispersão estocástico Lagrangiano para simular a dispersão de contaminantes na Camada Limite Planetária (CLP). Os resultados das simulações foram comparados com dados de concentração do experimento de Copenhagen. O bom desempenho da parametrização e a análise através de índices estatísticos permitiram concluir que as relações matemáticas que descrevem a taxa de dissipação da turbulenta, apresentam uma incerteza. A análise desenvolvida nesse estudo permite concluir que não existe uma forma funcional universal descrevendo a taxa de dissipação de energia turbulenta.

Neste trabalho, a partir de dados rotineiramente medidos em estações meteorológicas de superfície, estimamos os parâmetros de escala da Camada Limite Planetária (CLP) do experimento OLAD (Over Land Atmospheric Dispersion). Esses parâmetros são muito importantes no processo de dispersão, especialmente no cálculo das parametrizações para os modelos de dispersão atmosférica. Simular o processo de dispersão de poluentes na atmosfera sob a condição de vento fraco é uma tarefa difícil. Nesse sentido, realizamos a implementação e avaliação de um modelo de partícula lagrangeano semi-analítico, denominado ILS-LW (Iterative Langevin Solution for Low Wind) para investigar o processo de dispersão atmosférica em situações de vento fraco. A avaliação foi feita mediante comparação entre os resultados das simulações numéricas e os dados de concentração obtidos no experimento OLAD. Os dados experimentais foram coletados em um sítio experimental localizado no West Desert Test Center (WDTC), Utah, nos Estados Unidos, com a colaboração do exército americano e supervisões do National Oceanic and Atmospheric Administration (NOAA) e Air Resources Laboratory Field Research Division (ARLFRD), em setembro de 1997. Concluímos que o modelo ILS-LW reproduz satisfatoriamente o conjunto de dados testado.<br>In this work, we present estimates for Boundary Layer Planetary's scaling parameters for the data obtained by superficial meteorological stations of the Over Land Atmospheric Dispersion (OLAD) experimento These parameters are very important, specially for the estimate of parametrizations for the atmospheric dispersion models. The simulation of the atmospheric pollutant dispersion under low wind speed is not a trivial task. We have tested and evaluated a semi-analytic model with lagrangean particles, that we refer to as the Iterative Langevin Solution for Low Wind (ILS-LW), in order to investigate the atmospheric dispersion process in low wind speed conditions. The evaluation was done by comparing the results generated by the numerical simulations and the concentration dataset from OLAD experimento The experimental data were obtained on an experimental site at the West Desert Test Center (WDTC), Utah, USA, under colaboration ofthe american army and supervision by the National Oceanic and Atmospheric Administration (NOAA) and Air Resources Laboratory Field Research Division (ARLFRD) in September, 1997. We conclude that the model ILS-LW reproduces reasonably the tested data.

Os derramamentos de petróleo são consequência inevitável e indesejável da produção e transporte do petróleo e seus derivados. A maioria desses derramamentos são relativamente pequenos, mas alguns deles são grandes o suficiente para causar significativo impacto ambiental. Nessas situações, os modelos computacionais são ferramentas importantes para estimar a trajetória, dimensionamento e comportamento do óleo derramado no ambiente marinho, sendo determinantes na elaboração de planos de ação e trabalho das equipes de resposta. O transporte e destino de óleo offshore derramado são regidos majoritariamente, no curto período, por processos de transporte e de transformação físico-químicos e no longo período por processos de degradação biológica, de acordo com as condições ambientais locais (oceânicas e atmosféricas). Os principais processos que atuam sobre as manchas de óleo offshore incluem, no curto período, advecção, difusão turbulenta, espalhamento superficial, evaporação, dissolução, emulsificação, sedimentação e a interação de mancha de óleo com a linha da costa. O STFM (Spill, Transport and Fate Model) foi o modelo computacional desenvolvido nesse trabalho. Os algoritmos foram desenvolvidos com base em formulações físico-químicas propostas na literatura, sendo testadas as proposições de diversos autores e selecionadas as equações que apresentaram melhores resultado para integrar o conjunto físico-químico que compõe o STFM. Os resultados do trabalho mostraram que o STFM apresentou desempenho superior aos demais modelos testados na descrição do espalhamento e difusão dando mais estabilidade à mancha por utilizar a derivação de Dodge para a proposta de espalhamento de Fay e substituir o método usual de Randon Walk por Randon Flight (avançado no tempo) na forma canônica dada por Lynch. O algoritmo do STFM também traz outra evolução importante ao incluir um modelo de evaporação baseado nas equações empíricas de Fingas, substituindo as atuais parametrizações baseadas no ADIOS2 e nos métodos de pseudocomponentes.<br>Oil and its by-products spills are an inevitable and undesirable consequence of their production and transportation. Even though these spills are relatively small, some of them are large enough to cause significant environmental impact. Taken this into account, the computational models are important tools to estimate the trajectory, dimensioning and behavior of the oil spilled in the marine environment, being also determinants to elaborate action plans for response teams work. The transportation and fate of oil spills are governed in the short term by physical-chemical transport and transformation processes and in the long term by biological degradation processes, according to local environmental conditions (oceanic and atmospheric). The main processes that act on offshore oil spills include, in the short term, advection, turbulent diffusion, surface scattering, evaporation, dissolution, emulsification, sedimentation and the interaction of oil slick according to the coast line. The Spill, Transport and Fate Model (STFM) was the computational model developed in this work. The algorithms were developed based on physicochemical formulations proposed in literature, being the propositions of several authors tested and the equations which presented the best results were selected to integrate the physical-chemical set that makes up the STFM. The STFM results presented superior performance, giving more stability to the stain, compared to the other models tested in the scattering and diffusion description, by using the Dodge derivation for the Fay spreading proposal and by replacing the usual \"Randon Walk\" method by \"Randon Flight\" (advanced in time) in the canonical form given by Lynch. The STFM algorithm also brings forward another important evolution by including an evaporation model based on Fingas empirical equations, replacing the current parameterizations based on ADIOS2 and pseudo component methods.

Made available in DSpace on 2014-08-20T14:25:46Z (GMT). No. of bitstreams: 1 dissertacao_marieli_sallet.pdf: 231023 bytes, checksum: f445526b62fbfcde40fb1bc5ca90923a (MD5) Previous issue date: 2007-02-23<br>Currently, the search for analytical solutions for the dispersion problems is one of the main research subjects in the pollutant dispersion modeling. These solutions become important due to the intention to obtain dispersion models that generate reliable results in a small computational time, which are of great interest for regulatory air quality applications. Lagrangian particle models are an important and effective tool to simulate the atmospheric dispersion of airborne pollutants. These models are based on the Langevin equation, which is derived from the hypothesis that the velocity is given by the combination between a deterministic term and a stochastic term. In this work is presented a new Lagrangian particle model to simulate the pollutant dispersion in low wind speed conditions. During low wind speed, the diffusion of a pollutant in the planetary boundary layer (PBL) is indefinite and it has been observed that the plume is subject to a great deal of horizontal undulations, which are called plume meandering. The method proposed leads to a stochastic integral equation whose solution has been obtained through the Method of Successive Approximations or Picard s Iteration Method. The integral equation is written in terms of the real and imaginary parts of the complex function before performing the multiplication of the integrating factor, expressed by the Euler formula, inside and outside of the integral solution. To take account the meandering effect, the Frenkiel s Eulerian autocorrelation functions for low wind conditions is included naturally in the model. The new approach has been evaluated through the comparison with experimental data and other different dispersion models. Particularly, the results obtained by the model agree very well with the experimental data, indicating the model represents the dispersion process correctly in low wind speed conditions. It is also possible to verify that the new model results are better than ones obtained by the other models. The analytical feature of the technique and the natural inclusion of the Frenkiel s Eulerian autocorrelation function become the model more accurate than other models.<br>Atualmente, a busca por soluções analíticas para os problemas de dispersão é um dos principais assuntos de pesquisa na modelagem da dispersão de poluentes. Estas soluções tornam-se importantes devido à intenção de obter modelos de dispersão que geram resultados confiáveis em um tempo computacional pequeno, que são de grande interesse para aplicações no controle da qualidade do ar. Modelos de partícula Lagrangeano são uma ferramenta importante e eficaz para simular a dispersão atmosférica de poluentes do ar. Esses modelos são baseados na equação de Langevin, que é derivada da hipótese que a velocidade é dada por uma combinação entre um termo determinístico e um termo estocástico. Neste trabalho é apresentado um novo modelo de partícula Lagrangeano para simular a dispersão de poluentes em condições de velocidade de vento fraco. Durante a velocidade de vento fraco, a difusão de um poluente na Camada Limite Planetária (CLP) é indefinida e tem sido observado que a pluma está sujeita a grandes ondulações horizontais, que são chamadas meandro do vento. O método proposto leva a uma equação integral estocástica cuja solução é obtida através do Método das Aproximações Sucessivas ou Método Iterativo de Picard. A equação integral é escrita em termos das partes real e imaginária da função complexa antes de realizar a multiplicação do fator integrante, expresso pela fórmula de Euler, dentro e fora da solução integral. Para considerar o efeito do meandro, as funções de autocorrelação Euleriana de Frenkiel para condições de vento fraco são incluídas naturalmente no modelo. A nova aproximação foi avaliada através da comparação com dados experimentais e outros diferentes modelos de dispersão. Particularmente, os resultados obtidos pelo modelo concordam muito bem com os dados experimentais, indicando que o modelo representa o processo de dispersão corretamente em condições de velocidade de vento fraco. Também é possível verificar que os resultados do novo modelo são melhores do que os obtidos pelos outros modelos. A característica analítica da técnica e a inclusão natural da função de autocorrelação Euleriana de Frenkiel tornam o modelo mais exato que os outros modelos.

The description of the effects of the wind meandering in the scalar dispersion is a challenging task, since this type of flow represents a physical state characterized by multiple scales. In this study, a Lagrangian stochastic diffusion model is derived to describe the scalar transport during the horizontal wind meandering phenomenon, occurring in a PBL. The model is derived from the linearization of the Langevin equation and employs a heuristic functional form, which represents the autocorrelation functions of the meandering. The new solutions, which describe the longitudinal and lateral wind components, were used to simulate two experiments of contaminants dispersion in low-wind conditions, INEL (USA) and GRAZ (Austria). The results of the comparison indicate that the new model reproduces fairly well the observed concentrations of contaminants and, therefore, satisfactorily describes the enhanced dispersion due to the presence of meandering.<br>Descrever os efeitos provocados pelo meandro do vento na dispersão de escalares é uma tarefa desafiadora, uma vez que este tipo de escoamento representa um estado físico caracterizado por múltiplas escalas. Neste trabalho, deriva-se um modelo estocátisco Lagrangiano para descrever a dispersão de escalares, na camada limite planetária, durante o fenômeno de meandro do vento horizontal. O modelo é derivado a partir da linearização da equação de Langevin e emprega uma forma funcional heurística, que representa as funções de autocorrelação do meandro. As novas soluções, que descrevem as componentes longitudinais e laterais do vento, foram empregadas para simular dois experimentos de dispersão de contaminantes em condições de vento fraco, INEL (USA) e GRAZ (Áustria). Os resultados das comparações indicam que o novo modelo pode ser usado para reproduzir as concentrações observadas de contaminantes e, portanto descreve de forma satisfatória a difusão reforçada provocada pelo meandro do vento.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>A parameterization for the transport processes in a shear driven planetary boundary layer (PBL) has been established, employing turbulent statistical quantities measured during the north wind phenomenon in southern Brazil. Therefore, observed one-dimensional turbulent energy spectra are compared with a spectral model based on the Kolmogorov arguments. The good agreement obtained from this comparison leads to well defined formulations for the turbulent velocity variances, local decorrelation time scale and eddy diffusivities. Furthermore, for vertical regions in which the wind shear forcing is relevant, the eddy diffusivity derived from the north wind data presents a similar profile as those obtained from the non-extensive statistical mechanics theory. Finally, a validation for the present parameterization has been accomplished, using a Lagrangian stochastic dispersion model. Twind speed, is simulated. The analysis developed in this study shows that the turbulence parameterization constructed from wind data for north wind flow cases is able to describe the diffusion in a high wind speed, shear-dominated PBL.he Prairie Grass data set, which presents high mean<br>Foi realizada uma parametrização para os processos de transporte em uma camada limite planetária (CLP) dominada pela turbulência mecânica, empregando quantidades estatísticas turbulentas medidas durante eventos do Vento Norte no Sul do Brasil. Assim, espectros observados de energia turbulenta unidimensionais são comparados com um modelo espectral baseado na hipótese de Kolmogorov válida para uma turbulência desenvolvida. A boa concordância obtida a partir desta comparação permite derivar formulações para as variâncias de velocidade turbulenta, escala de tempo de decorrelação local e para os coeficientes de difusão. Além disso, o coeficiente de difusão vertical derivado a parir dos dados de vento norte apresenta um perfil semelhante àquele obtido dos conceitos da mecânica estatística não-extensiva. Finalmente, a validação da presente parametrização foi realizada utilizando-se um modelo de dispersão estocástico Lagrangeano. São simuladas as concentrações medidas ao nível do solo no experimento clássico de Prairie-Grass sob condições de vento forte. A análise desenvolvida no presente estudo mostra que a parametrização da turbulência, construída a partir de dados de casos de Vento Norte, é capaz de descrever a difusão em condições de vento forte, em uma CLP gerada pela turbulência mecânica.

Made available in DSpace on 2016-08-29T15:09:31Z (GMT). No. of bitstreams: 1 tese_2632_Dissertação_Santiago2007.pdf: 18381268 bytes, checksum: ea3070f9b201f73cb61b16da20bc45a9 (MD5) Previous issue date: 2007-02-26<br>Nesta pesquisa um Modelo Lagrangiano de Partículas de Deslocamento Aleatório (MLPDA) desenvolvido para a modelagem da dispersão em águas rasas é acoplado ao modelo hidrodinâmico DIVAST (Depth Integrated Velocity and Solute Transport) para estudar as características dispersivas na região do canal de acesso ao Porto de Vitória. Inicialmente o modelo DIVAST é utilizado na avaliação da hidrodinâmica induzida pela maré astronômica no canal de acesso ao Porto de Vitória. O DIVAST se fundamenta nas equações não-lineares de águas rasas e considera além do efeito de fricção da vegetação de mangue na hidrodinâmica, o alagamento e a secagem de planícies de marés cobertas com vegetação de mangue. A grade computacional elaborada representa adequadamente a geometria e as ilhas no interior da região de estudo. As condições de contorno fornecidas ao modelo numérico foram elevação no contorno leste e correntes no contorno oeste, que foram obtidas de um modelo global para o complexo estuarino da ilha de Vitória. A validação dos resultados do modelo DIVAST foi realizada pela comparação com dados experimentais de velocidade e com dados numéricos de elevação da superfície da água, mostrando uma boa concordância com os mesmos e indicando que o modelo representa satisfatoriamente a hidrodinâmica da região do canal de acesso ao Porto de Vitória. A observação dos campos de escoamento simulados pelo DIVAST possibilitou identificar e analisar diferentes padrões de escoamento associados a interação do escoamento com a geometria do canal. O MLPDA se fundamenta nas equações de deslocamento aleatório. O MLPDA foi validado a partir da simulação e comparação com experimentos numéricos sugeridos por Heemink (1995). Os resultados do MLPDA reproduziram bem os experimentos numéricos e demostraram que o modelo é uma ferramenta adequada para a simulação do transporte de solutos. O MLPDA acoplado ao DIVAST foi aplicado para a região do canal de acesso ao Porto de Vitória e demonstrou a capacidade de simular os processos dispersivos em pequena e grande escala. Identificaram-se diferentes zonas na região modelada, observando-se áreas mais dispersivas e áreas que favorecem o acúmulo de constituintes.