A new method for automatically integrating the results of hydrodynamic models of currents in Texas bays with the National Oceanic and Atmospheric Administration’s (NOAA) in house oil spill trajectory model, the General NOAA Operational Modeling Environment (GNOME), is presented. Oil spill trajectories are predicted by inputting wind and water current forces on an initial spill in a dedicated spill trajectory model. These currents can be field measured, but in most real and meaningful cases, the current field is too spatially complex to measure with any accuracy. Instead, current fields are simulated by hydrodynamic models, whose results must then be coupled with a dedicated spill trajectory model. The newly developed automated approach based on Python scripting eliminates the present labor-intensive practice of manually coupling outputs and inputs of the separate models, which requires expert interpretation and modification of data formats and setup conditions for different models. The integrated system is demonstrated by coupling GNOME independently with TXBLEND – a 2D depth-averaged model which is currently used by the Texas Water Development Board, and SELFE – a newer 3D hydrodynamic model with turbulent wind mixing. A hypothetical spill in Galveston Bay is simulated under different conditions using both models, and a brief qualitative comparison of the results is used to raise questions that may be addressed in future work using the automated coupling system to determine the minimum modeling requirements for an advanced oil spill nowcast/forecast platform in Texas bays. / text

A new method is presented to provide automatic sequencing of multiple hydrodynamic models and automated analysis of model forecast uncertainty. A Hydrodynamic and oil spill model Python (HyosPy) wrapper was developed to run the hydrodynamic model, link with the oil spill, and visualize results. The HyosPy wrapper completes the following steps automatically: (1) downloads wind and tide data (nowcast, forecast and historical); (2) converts data to hydrodynamic model input; (3) initializes a sequence of hydrodynamic models starting at pre-defined intervals on a multi-processor workstation. Each model starts from the latest observed data, so that the multiple models provide a range of forecast hydrodynamics with different initial and boundary conditions reflecting different forecast horizons. As a simple testbed for integration strategies and visualization on Google Earth, a Runge-Kutta 4th order (RK4) particle transport tracer routine is developed for oil spill transport. The model forecast uncertainty is estimated by the difference between forecasts in the sequenced model runs and quantified by using statistics measurements. The HyosPy integrated system with wind and tide force is demonstrated by introducing an imaginary oil spill in Corpus Christi Bay. The results show that challenges in operational oil spill modeling can be met by leveraging existing models and web-visualization methods to provide tools for emergency managers. / text

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).