Sonia Castanedo

The Prestige Oil Spill in Cantabria (Bay of Biscay). Part I: Operational Forecasting System for Quick Response, Risk Assessment, and Protection of Natural Resources

Abstract In this paper, we present the operational forecasting system developed to assist in the response to the 2002 Prestige oil spill in Cantabria. The objective of the system developed was to forecast the wave climate, tidal and wind currents, and oil spill trajectories to provide a technical assessment to decision makers for a response to the oil spill. The two main components of the system were data collection and processing and integration with numerical models for forecasting. The information from overflights received daily became essential in achieving a correct initial position of the oil slicks. Meteorological and oceanographic data were also received daily by means of an emergency protocol established between Puertos del Estado (Spain), the Naval Research Laboratory (USA), and the University of Cantabria (Spain). These data were used to run the trajectory model, the wave propagation model, and the shallow depth-integrated flow model. The information generated by the numerical simulations was presented to the decision makers every day in the form of maps that were easy and quick to interpretation as a tool to help in the response planning. In addition, to develop a defensive or protection strategy for sensitive areas like estuaries and marshes, a hydrodynamic study was carried out by the University of Cantabria in all the estuaries of the region. The result of this study consisted of a boom deployment plan for each.

Application of HF radar currents to oil spill modelling

In this work, the benefits of high-frequency (HF) radar currents for oil spill modeling and trajectory analysis of floating objects are analyzed. The HF radar performance is evaluated by means of comparison between a drifter buoy trajectory and the one simulated using a Lagrangian trajectory model. A methodology to optimize the transport model performance and to calculate the search area of the predicted positions is proposed. This method is applied to data collected during the Galicia HF Radar Experience. This experiment was carried out to explore the capabilities of this technology for operational monitoring along the Spanish coast. Two long-range HF radar stations were installed and operated between November 2005 and February 2006 on the Galician coast. In addition, a drifter buoy was released inside the coverage area of the radar. The HF radar currents, as well as numerical wind data were used to simulate the buoy trajectory using the TESEO oil spill transport model. In order to evaluate the contribution of HF radar currents to trajectory analysis, two simulation alternatives were carried out. In the first one, wind data were used to simulate the motion of the buoy. In the second alternative, surface currents from the HF radar were also taken into account. For each alternative, the model was calibrated by means of the global optimization algorithm SCEM-UA (Shuffled Complex Evolution Metropolis) in order to obtain the probability density function of the model parameters. The buoy trajectory was computed for 24h intervals using a Monte Carlo approach based on the results provided in the calibration process. A bivariate kernel estimator was applied to determine the 95% confidence areas. The analysis performed showed that simulated trajectories integrating HF radar currents are more accurate than those obtained considering only wind numerical data. After a 24h period, the error in the final simulated position improves using HF radar currents. Averaging the information from all the simulated daily periods, the mean search and rescue area calculated using HF radar currents, is reduced by approximately a 62% in comparison with the search area calculated without these data. These results show the positive contribution of HF radar currents for trajectory analysis, and demonstrate that these data combined with atmospheric forecast models, are of value for trajectory analysis of oil spills or floating objects.

Calibration of a Lagrangian Transport Model Using Drifting Buoys Deployed during the Prestige Oil Spill

Abstract The experience acquired in the Prestige crisis management has demonstrated the importance of forecasting oil slick trajectories to plan an effective oil spill response. To have a reliable prediction system, we need to perform a detailed calibration and validation of the oil spill transport model. In this work, the Lagrangian transport model, PICHI, developed by the University of Cantabria during the Prestige accident, is calibrated by means of an automatic calibration methodology. The shuffled complex evolution method, developed by the University of Arizona (SCE-UA), is applied to estimate the optimal coefficients of the model. The calibration of the model has been carried out using 13 buoys deployed in the Bay of Biscay during the Prestige accident as well as coetaneous meteorological and oceanographic data. Moreover, reanalysis data collected in the Spanish ESEOO project framework has also been used. Results suggest that buoys outside the continental slope were mainly driven by wind, whereas ocean currents played an important role in the motion of the buoys located over the continental slope and shelf. According to these findings, the final calibration of the coefficients is performed considering different buoy data. The methodology applied to this broad buoy database, has allowed us to calibrate the model, taking into account the relative importance of the forcings in buoy movement as well as the dynamics associated with each area.

Analysis of the reliability of a statistical oil spill response model

A statistical oil spill response model is developed and validated by means of actual oil slick observations reported during the Prestige accident and trajectories of drifter buoys. The model is based on the analysis of a database of hypothetical oil spill scenarios simulated by means of a Lagrangian transport model. To carry out the simulations, a re-analysis database consisting of 44-year hindcast dataset of wind and waves and climatologic daily mean surface currents is used. The number of scenarios required to obtain statistically reliable results is investigated, finding that 200 scenarios provide an optimal balance between the accuracy of the results and the computational effort. The reliability of the model was analyzed by comparing the actual data with the numerical results. The agreement found between actual and numerical data shows that the developed statistical oil spill model is a valuable tool to support spill response planning.

Oil spill vulnerability assessment integrating physical, biological and socio-economical aspects: Application to the Cantabrian coast (Bay of Biscay, Spain)

A methodology has been developed to carry out an integrated oil spill vulnerability index, V, for coastal environments. This index takes into account the main physical, biological and socio-economical characteristics by means of three intermediate indexes. Three different integration methods (worst-case, average and survey-based) along with ESI-based vulnerability scores, V(ESI), proposed for the Cantabrian coast during the Prestige oil spill, have been analyzed and compared in terms of agreement between the classifications obtained with each one for this coastal area. Results of this study indicate that the use of the worst-case index, V(R), leads to a conservative ranking, with a very poor discrimination which is not helpful in coastal oil spill risk management. Due to the homogeneity of this coastal stretch, the rest of the methods, V(I), V(M) and V(ESI), provide similar classifications. However, V(M) and V(I) give more flexibility allowing three indexes for each coastal segment and including socio-economic aspects. Finally, the V(I) procedure is proposed here as the more advisable as using this index promotes the public participation that is a key element in the implementation of Integrated Coastal Zone Management (IZCM).

The Prestige Oil Spill in Cantabria (Bay of Biscay). Part II. Environmental Assessment and Monitoring of Coastal Ecosystems

Abstract Assessment and monitoring activities constituted two main tasks of the emergency response system implemented by the regional government of Cantabria (north Spain) after the Prestige oil spill. The assessment covered four types of environmental units: estuaries, rocky shores, sand beaches, and subtidal areas, up to a 300-m depth. Monitoring procedures included the chemical quantification of total hydrocarbons and polycyclic aromatic hydrocarbons in water, sediments, and benthic organisms (clams and goose barnacles), and the analysis of structural changes in intertidal communities. Disturbance of oil cover was significantly more extensive in coastal areas, rather than in estuarine areas, where 50,000 m2 were directly covered by fuel. Otherwise, the presence of oil in subtidal areas was a rare event. Results from the bioeffects analyses were in agreement with the overall impact assessments, pointing to the coastal habitats as the areas where the bioavailability of toxic components from the Prestige spill presented a significant level of risk.

Stochastic Lagrangian trajectory model for drifting objects in the ocean

The prediction of drifting object trajectories in the ocean is a complex problem plagued with uncertainties. This problem is usually solved simulating the possible trajectories based on wind and advective numerical and/or instrumental data in real time, which are incorporated into Lagrangian trajectory models. However, both data and Lagrangian models are approximations of reality and when comparing trajectory data collected from drifter exercises with respect to Lagrangian models results, they differ considerably. This paper introduces a stochastic Lagrangian trajectory model that allows quantifying the uncertainties related to: (i) the wind and currents numerical and/or instrumental data, and (ii) the Lagrangian trajectory model. These uncertainties are accounted for within the model through random model parameters. The quantification of these uncertainties consists in an estimation problem, where the parameters of the probability distribution functions of the random variables are estimated based on drifter exercise data. Particularly, it is assumed that estimated parameters maximize the likelihood of our model to reproduce the trajectories from the exercise. Once the probability distribution parameters are estimated, they can be used to simulate different trajectories, obtaining location probability density functions at different times. The advantage of this method is that it allows: (i) site specific calibration, and (ii) comparing uncertainties related to different wind and currents predictive tools. The proposed method is applied to data collected during the DRIFTER Project (eranet AMPERA, VI Programa Marco), showing very good predictive skills.

Scaling properties of tidal networks

A new methodology is developed to extract tidal network from hydrodynamic conditions, and use data derived from numerical modeling or field observations to test the hypothesis that tidal networks are characterized by scale-invariant properties. Different tidal network configurations have been obtained from long-term numerical simulations in an idealized basin. These simulations show the influence of hydrodynamic conditions (tidal range, TR) and sediment (grain size sediment, D50) on the final configuration of the network. One of the signatures of scale-invariant behavior is related to the presence of a power law relationship in the probability distribution of geometrical characteristics. For each model configuration and field site, the probability distribution of drainage area and the drainage volume has been calculated, and in both cases tidal networks show scale-invariant characteristics. After assessing the sensitivity of the results, an energy expenditure analysis shows that tidal basins evolve toward a state with less morphodynamic activity, with a lower energy expenditure compare with the initial state.

Probabilistic relationships between wind and surface water circulation patterns in the SE Bay of Biscay

Non-linear K-means classification algorithm was used to obtain a comprehensive description of the winds and high-frequency radar-derived currents in the SE Bay of Biscay (study area), taking into account a wide range of scales, from several days to interannual variability. The results in the study area show that in summer, a stronger variability in winds and surface currents can be expected, while in winter, intense southwesterly winds and a cyclonic circulation prevail. In addition to the seasonal component of the currents, a significant spatial variability in terms of current patterns and a temporal variability at shorter and interannual scales were also identified, highlighting the complexity of the surface current dynamics. Moreover, the probabilistic relationships between wind and current patterns were explored, obtaining conditional probabilities. Most of the surface current patterns are clearly related to specific wind patterns that are recurrent in the study area. However, other common current patterns are not so clearly related to specific wind conditions. The presence of a seasonal slope current (Iberian Poleward Current, IPC) is one of the most relevant features of the local circulation. An IPC occurrence time series based on Sea Surface Temperature satellite imagery was used to obtain conditional probabilities with the high-frequency radar surface current patterns, showing a relation between the strongest IPC events and closed cyclonic currents, which are not linked to specific winds.

Development of a GIS-based oil spill risk assessment system

The Prestige crisis proved the importance of developing scientific- and application- oriented activities which allow us to improve the oil spill preparedness and response systems having efficient tools to minimize the spill impact in case of an emergency. In this work a methodology has been developed in which oil spill risk is calculated assuming its dependency on the hazard, H, and vulnerability, V, components. To estimate the probability of an oil spill reaching a specific target area, H, an approach based on numerically generated data has been used. Regarding the other risk component, the oil spill vulnerability V, a new approach is presented which focuses on the integration in one single index of physical, biological and socio-economic aspects of the coast. To illustrate the presented methodology it has been applied to the Cantabrian coast, Northern coast of Spain (Bay of Biscay) where a user-friendly application which incorporates a Geographic Information System has been built. This application integrates the two components of the oil spill risk, H and V, to support spill response planning along this coast.