Hindcast of oil-spill pollution during the Lebanon crisis in the Eastern Mediterranean, July-August 2006

MOON (Mediterranean Operational Oceanography Network http://www.moon-oceanforecasting.eu) provides near-real-time information on oil-spill detection (ocean color and SAR) and predictions [ocean forecasts (MFS and CYCOFOS) and oil-spill predictions (MEDSLIK)]. We employ this system to study the Lebanese oil-pollution crisis in summer 2006 and thus to assist regional and local decision makers in Europe, regionally and locally. The MEDSLIK oil-spill predictions obtained using CYCOFOS high-resolution ocean fields are compared with those obtained using lower-resolution MFS hydrodynamics, and both are validated against satellite observations. The predicted beached oil distributions along the Lebanese and Syrian coasts are compared with in situ observations.The oil-spill predictions are able to simulate the northward movement of the oil spill, with the CYCOFOS predictions being in better agreement with satellite observations. Among the free MEDSLIK parameters tested in the sensitivity experiments, the drift factor appears to be the most relevant to improve the quality of the results.     

Scale- and orientation-invariant scene similarity metrics for image queries

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumptions that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework.     

Revisiting the 1669 Etnean eruptive crisis using a cellular automata model and implications for volcanic hazard in the Catania area

Cellular Automata provide an alternative approach to standard numerical methods for modelling some complex natural systems, the behaviour of which can be described in terms of local interactions of their constituent parts. SCIARA is a 2-D Cellular Automata model which simulates lava flows. It was tested on, validated by, and improved on several Etnean lava events such as the 1986–1987 eruption and the first and last phase of the 1991–1993 event. With respect to forecasting the surface covered by the lava flows, the best results were acceptable. The model has been used to determine hazard zones in the inhabited areas of Nicolosi, Pedara, S. Alfio and Zafferana (Sicily, Italy). The main goal of the current work in the Etnean area from Nicolosi to Catania has been the verification of the volcanic hazard effects of an eruptive crisis similar to the event that occurred in 1669. The simulation uses the volcanic data of the 1669 eruption with present-day morphology. Catania has been affected by some historical Etnean events, the most famous one being the 1669 eruption, involving 1 km³ of lava erupted over the course of 120 days. The simulation of ephemeral vents and the use of different histories within the experiments have been crucial in the determination of a new hazard area for Catania. In fact, during the simulation the city was never affected without the introduction of ephemeral vents, proving the fact that lava tubes played a fundamental role in the 1669 Catania lava crisis.     

“Grey swan” storm surges pose a greater coastal flood hazard than climate change

Ocean Dynamics - In this paper, we show that over the next few decades, the natural variability of mid-latitude storm systems is likely to be a more important driver of coastal extreme sea levels...     

Personalizing map content to improve task completion efficiency

Significant interaction challenges arise in both developing and using interactive map applications. Users encounter problems of information overload in using interactive maps to complete tasks. This is further exacerbated by device limitations and interaction constraints in increasingly popular mobile platforms. Application developers must then address restrictions related to screen size and limited bandwidth in order to effectively display maps on mobile devices. In order to address issues of user information overload and application efficiency in interactive map applications, we have developed a novel approach for delivering personalized vector maps. Ongoing task interactions between users and maps are monitored and captured implicitly in order to infer individual and group preferences related to specific map feature content. Personalized interactive maps that contain spatial feature content tailored specifically to users' individual preferences are then generated. Our approach addresses spatial information overload by providing only the map information necessary and sufficient to suit user interaction preferences, thus simplifying the completion of tasks performed with interactive maps. In turn, tailoring map content to specific user preferences considerably reduces the size of vector data sets necessary to transmit and render maps on mobile devices. We have developed a geographic information system prototype, MAPPER (MAP PERsonalization), that implements our approach. Experimental evaluations show that the use of personalized maps helps users complete their tasks more efficiently and can reduce information overload.     

Assessment of uncertainty associated with the estimation of well catchments by moment equations

Non-local stochastic moment equations are used successfully to analyze groundwater flow in randomly heterogeneous media. Here we present a moment equations-based approach to quantify the uncertainty associated with the estimation of well catchments. Our approach is based on the development of a complete second order formalism which allows obtaining the first statistical moments of the trajectories of conservative solute particles advected in a generally non-uniform groundwater flow. Approximate equations of moments of particles’ trajectories are then derived on the basis of a second order expansion in terms of the standard deviation of the aquifer log hydraulic conductivity. Analytical expressions are then obtained for the predictors of locations of mean stagnation points, together with their associated uncertainties. We implement our approach on heterogeneous media in bounded two-dimensional domains, with and without including the effect of conditioning on hydraulic conductivity information. The impact of domain size, boundary conditions, heterogeneity and non-stationarity of hydraulic conductivity on the prediction of a well catchment is explored. The results are compared against Monte Carlo simulations and semi-analytical solutions available in the literature. The methodology is applicable to both infinite and bounded domains and is free of distributional assumptions (and so applies to both Gaussian and non-Gaussian log hydraulic conductivity fields) and formally includes the effect of conditioning on available information.     

Eddy diffusivity derived from drifter data for dispersion model applications

Ocean transport and dispersion processes are at the present time simulated using Lagrangian stochastic models coupled with Eulerian circulation models that are supplying analyses and forecasts of the ocean currents at unprecedented time and space resolution. Using the Lagrangian approach, each particle displacement is described by an average motion and a fluctuating part. The first one represents the advection associated with the Eulerian current field of the circulation models while the second one describes the sub-grid scale diffusion. The focus of this study is to quantify the sub-grid scale diffusion of the Lagrangian models written in terms of a horizontal eddy diffusivity. Using a large database of drifters released in different regions of the Mediterranean Sea, the Lagrangian sub-grid scale diffusion has been computed, by considering different regimes when averaging statistical quantities. In addition, the real drifters have been simulated using a trajectory model forced by OGCM currents, focusing on how the Lagrangian properties are reproduced by the simulated trajectories.     

An ordered probit model for seismic intensity data

Seismic intensity, measured through the Mercalli–Cancani–Sieberg (MCS) scale, provides an assessment of ground shaking level deduced from building damages, any natural environment changes and from any observed effects or feelings. Generally, moving away from the earthquake epicentre, the effects are lower but intensities may vary in space, as there could be areas that amplify or reduce the shaking depending on the earthquake source geometry, geological features and local factors. Currently, the Istituto Nazionale di Geofisica e Vulcanologia analyzes, for each seismic event, intensity data collected through the online macroseismic questionnaire available at the web-page www.haisentitoilterremoto.it. Questionnaire responses are aggregated at the municipality level and analyzed to obtain an intensity defined on an ordinal categorical scale. The main aim of this work is to model macroseismic attenuation and obtain an intensity prediction equation which describes the decay of macroseismic intensity as a function of the magnitude and distance from the hypocentre. To do this we employ an ordered probit model, assuming that the intensity response variable is related through the link probit function to some predictors. Differently from what it is commonly done in the macroseismic literature, this approach takes properly into account the qualitative and ordinal nature of the macroseismic intensity as defined on the MCS scale. Using Markov chain Monte Carlo methods, we estimate the posterior probability of the intensity at each site. Moreover, by comparing observed and estimated intensities we are able to detect anomalous areas in terms of residuals. This kind of information can be useful for a better assessment of seismic risk and for promoting effective policies to reduce major damages.