Browsing by Subject "Oil spill modeling"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Evaluating hydrodynamic uncertainty in oil spill modeling(2013-05) Hou, Xianlong; Hodges, Ben R.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.Item Integrating hydrodynamic and oil spill trajectory models for nowcasts/forecasts of Texas bays(2011-08) Rosenzweig, Itay; Hodges, Ben R.; Passalacqua, PaolaA 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.