Fernando J. Méndez

Hydrodynamics induced by wind waves in a vegetation field

The wave-induced kinematics and dynamics of a submerged or emerged vegetation field is analyzed. Using potential flow and an eigenfunction expansion, the problem is solved considering regular as well as irregular incident waves. The model takes into account the vegetation motion and solves for the complete wave system on the vegetation field and in its vicinity. The model is validated against experimental laboratory data obtained by other authors, showing a much better agreement than previous theoretical models. In this paper the model is used to evaluate wave height evolution (damping), vegetation and fluid motion, and forces and moments on the vegetation. Furthermore, the inclusion of irregular waves provides force and moment distributions on the vegetation field depending on the wave climate statistics.

Analyzing Monthly Extreme Sea Levels with a Time-Dependent GEV Model

A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales—such as seasonality, interdecadal variability, and secular trends—in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

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.

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.

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.

Directional Calibration of Wave Reanalysis Databases Using Instrumental Data

AbstractWave reanalysis databases (WRDBs) offer important advantages for the statistical characterization of wave climate (continuous time series, good spatial coverage, constant time span, homogeneous forcing, and more than a 40-yr-long time series) and for this reason, they have become a powerful tool for the design of offshore and coastal structures. However, WRDBs are not quantitatively perfect and corrections using instrumental observations must be addressed before they are used; this process is called calibration. The calibration is especially relevant near the coast and in areas where the orography is complex, since in these places the inaccuracy of WRDB is evident because of the bad description of the wind fields (i.e., insufficient forcing resolution). The quantitative differences between numerical and instrumental data suggest that different corrections should be applied depending on the mean direction of the sea state. This paper proposes a calibration method based on a nonlinear regression pro...

A weather‐type statistical downscaling framework for ocean wave climate

Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales.

ESTELA: a method for evaluating the source and travel time of the wave energy reaching a local area

The description of wave climate at a local scale is of paramount importance for offshore and coastal engineering applications. Conditions influencing wave characteristics at a specific location cannot, however, be fully understood by studying only local information. It is necessary to take into account the dynamics of the ocean surface over a large ‘upstream’ wave generation area. The goal of this work is to provide a methodology to easily characterize the area of influence of any particular ocean location worldwide. Moreover, the developed method is able to characterize the wave energy and travel time in that area. The method is based on a global scale analysis using both geographically and physically based criteria. The geographic criteria rely on the assumption that deep water waves travel along great circle paths. This limits the area of influence by neglecting energy that cannot reach a target point, as its path is blocked by land. The individual spectral partitions from a global wave reanalysis are used to reconstruct the spectral information and apply the physically based criteria. The criteria are based on the selection of the fraction of energy that travels towards the target point for each analysed grid point. The method has been tested on several locations worldwide. Results provide maps that inform about the relative importance of different oceanic areas to the local wave climate at any target point. This information cannot be inferred from local parameters and agrees with information from other approaches. The methodology may be useful in a number of applications, such as statistical downscaling, storm tracking and grid definition in numerical modelling.

Effects of climate change on exposure to coastal flooding in Latin America and the Caribbean

This study considers and compares several of the most important factors contributing to coastal flooding in Latin American and the Caribbean (LAC) while accounting for the variations of these factors with location and time. The study assesses the populations, the land areas and the built capital exposed at present and at the middle and end of the 21st century for a set of scenarios that include both climatic and non-climatic drivers. Climatic drivers include global mean sea level, natural modes of climate variability such as El Niño, natural subsidence, and extreme sea levels resulting from the combination of projected local sea-level rise, storm surges and wave setup. Population is the only human-related driver accounted for in the future. Without adaptation, more than 4 million inhabitants will be exposed to flooding from relative sea-level rise by the end of the century, assuming the 8.5 W m−2 trajectory of the Representative Concentration Pathways (RCPs), or RCP8.5. However, the contributions from El Niño events substantially raise the threat in several Pacific-coast countries of the region and sooner than previously anticipated. At the tropical Pacific coastlines, the exposure by the mid-century for an event similar to El Niño 1998 would be comparable to that of the RCP4.5 relative sea-level rise by the end of the century. Furthermore, more than 7.5 million inhabitants, 42,600 km2 and built capital valued at 334 billion USD are currently situated at elevations below the 100-year extreme sea level. With sea levels rising and the population increasing, it is estimated that more than 9 million inhabitants will be exposed by the end of the century for either of the RCPs considered. The spatial distribution of exposure and the comparison of scenarios and timeframes can serve as a guide in future adaptation and risk reduction policies in the region.

The effect of temporal dependence on the estimation of the frequency of extreme ocean climate events

The term ‘extreme ocean climate estimation’ refers to the assessment of the statistical distribution of extreme oceanographical geophysical variables. Components of the ocean climate are variables, such as the storm surge, wind velocity and significant wave height. Important characteristics of extreme ocean climate are the frequencies of the exceedances of ocean climate variables over selected thresholds. Assuming that exceedances are statistically independent of each other, their frequencies can be estimated using non-homogeneous Poisson processes. However, exceedances often exhibit temporal dependency because of the tendency of storms to gather in clusters. We assess the effect of these dependencies on the estimation of the rate of occurrence of extreme events. Using a database built under the HIPOCAS European project, which covers the Western Mediterranean Sea, we compare the performance of the non-homogeneous Poisson process approach versus a new model that allows for temporal dependency. We show that the latter outperforms the former in terms of the resulting goodness of fit and significance of the parameters involved.