Advances in statistical methodologies for mid-long term simulation of oil spills in the sea

RESUMEN: Con esta tesis se pretenden aportar nuevas metodologías para mejorar las herramientas disponibles actualmente para la lucha contra la contaminación marina por derrames de hidrocarburos. Por un lado, se propone una metodología para predecir de forma probabilística derrames en el medio-largo plazo. La parte más innovadora de esta metodología consiste en la simulación estadística, mediante modelos de regresión logística, de aquellas variables ambientales que afectan la evolución de un derrame en el mar. El modelado basado en regresión logística es aplicado en el golfo de México y en el Golfo de Vizcaya. En este segundo caso, los patrones ambientales obtenidos son empleados, posteriormente, para la predicción en el medio-largo plazo de derrames. Los resultados obtenidos en cada caso, demuestran el potencial de las técnicas propuestas. Por otro lado, se propone otra metodología enfocada al análisis de peligrosidad asociada a derrames profundos, basada en la extracción de patrones ambientales espacio-temporales. Esta metodología ha sido aplicada en el Mar del Norte y los resultados obtenidos comparados con metodologías tradicionales de estudio de peligrosidad, evidenciando las capacidades de la metodología propuesta, habiendo reducido enormemente los costes computacionales respecto a las técnicas tradicionales. Se ha demostrado como las metodologías propuestas en esta tesis pueden mejorar y ampliar los beneficios de las herramientas existentes para la lucha contra la contaminación marina.ABSTRACT: The aim of this thesis is the improvement of existing tools for the fight against oil spill marine pollution. On the one side, we developed a methodology for the probabilistic forecast of oil spills at the mid-long term. The core of the methodology is the statistical simulation of oil spill met-ocean forcings, using a logistic regression model. Logistic regression modeling of met-ocean patterns is applied in the Gulf of Mexico and in the Bay of Biscay. In the second case, mid-long term prediction of oil spill is achieved considering the statistically simulated met-ocean conditions. On the other hand, we proposed a methodology for deep oil spill hazard assessment, based on the selection of spatio-temporal met-ocean patterns. This methodology was applied in the North Sea, and the obtained results were compared with the ones achieved with a traditional hazard estimation technique, highlighting the benefit of the proposed method. The methodologies presented in this thesis have shown their ability and the benefits they could bring to the tools for the fight against marine pollution

Predicción probabilística de trayectorias de derrames de hidrocarburos a medio-largo plazo en el Golfo de Vizcaya

El presente trabajo ha sido parcialmente financiado por el Ministerio de Ciencia, Innovación y Universidades en el marco del proyecto OILHAZARD3D (TRA2017-89164-R

Estudio de las zonas de acumulación de basuras marinas en el estuario del Pas (Cantabria)

Este trabajo ha sido financiado por la Fundación Biodiversidad del Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente del Gobierno de España, en el marco del proyecto “Clean LICs

Statistical Simulation of Ocean Current Patterns Using Autoregressive Logistic Regression Models: a Case Study in the Gulf of Mexico

Autoregressive logistic regression models have been demonstrated to be a powerful tool for statistical simulation of spatial patterns in climate and meteorology fields. In this paper we introduce a statistical framework for the simulation of ocean current patterns based on the autoregressive logistic regression models, and apply it to the Gulf of Mexico Loop Current. The statistical model is forced by three autoregressive terms, the wind stress curl in the Gulf of Mexico and in the Caribbean Sea, and the sea level pressure anomalies over the North Atlantic. It is used to replicate the bi-weekly historical sequence of 8 Loop Current patterns, obtained from a 24-year altimetry derived dataset. The model reproduces the inter-annual and intra-annual variability of the original time series, showing notable fitting capacity. A point-by-point comparison between the actual and simulated pattern series confirms the capability of the model in analysing the evolution of ocean current patterns. The predictive skill of the model is also explored, and the preliminary forecast (up to 3 months) results are encouraging. The presented statistical framework may find more practical applications in the future, such as the generation of statistically sound climate-based oceanographic scenarios for risk analyses, and the mid-term probabilistic prediction of ocean current patterns

Operational oil spill trajectory modelling using HF radar currents:A northwest European continental shelf case study

This paper presents a novel operational oil spill modelling system based on HF radar currents, implemented in a northwest European shelf sea. The system integrates Open Modal Analysis (OMA), Short Term Prediction algorithms (STPS) and an oil spill model to simulate oil spill trajectories. A set of 18 buoys was used to assess the accuracy of the system for trajectory forecast and to evaluate the benefits of HF radar data compared to the use of currents from a hydrodynamic model (HDM). The results showed that simulated trajectories using OMA currents were more accurate than those obtained using a HDM. After 48 h the mean error was reduced by 40%. The forecast skill of the STPS method was valid up to 6 h ahead. The analysis performed shows the benefits of HF radar data for operational oil spill modelling, which could be easily implemented in other regions with HF radar coverage.</p

Análisis meteoceánico en BIMEP para el diseño de convertidores de energía marinos

El trabajo presentado se ha desarrollado en el marco del proyecto TRL +. TRL+ es un proyecto de investigación financiado por el Ministerio de Economía y Competitividad (MINECO) mediante el programa RETOS (RTC-2015-3836-3