Antonio Espejo

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.

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.

Spectral Ocean Wave Climate Variability Based on Atmospheric Circulation Patterns

AbstractTraditional approaches for assessing wave climate variability have been broadly focused on aggregated or statistical parameters such as significant wave height, wave energy flux, or mean wave direction. These studies, although revealing the major general modes of wave climate variability and trends, do not take into consideration the complexity of the wind-wave fields. Because ocean waves are the response to both local and remote winds, analyzing the directional full spectra can shed light on atmospheric circulation not only over the immediate ocean region, but also over a broad basin scale. In this work, the authors use a pattern classification approach to explore wave climate variability in the frequency–direction domain. This approach identifies atmospheric circulation patterns of the sea level pressure from the 31-yr long Climate Forecast System Reanalysis (CFSR) and wave spectral patterns of two selected buoys in the North Atlantic, finding one-to-one relations between each synoptic pattern (...

A global classification of coastal flood hazard climates associated with large-scale oceanographic forcing

Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.

Morphodynamic Evolution of Dredged Sandpits

Abstract Numerical modeling of dredged pits is conducted to investigate the hydrodynamic and morphodynamic interaction in offshore sand extractions. Based on an analytical formulation, a semianalytical numerical model (MEMPITS) has been developed to study the morphodynamic evolution of offshore (ho > 20 m) sand borrow areas. The numerical model has been applied to study the morphodynamic evolution of two offshore sand borrow areas in the Balearic Islands (Spain). Field data allowed a detailed characterization of the evolution of the sandpits. Time series of local hydrodynamics have been obtained using generation models (hindcast) combined with local wave and flow models. A verification of the simple model has been carried out using relatively slight adjustments to the calibration factors. The simple model provides good estimates of the infill rate and migration velocities of the offshore pits on the scale of years. This semianalytical tool allows a quick systematic investigation of the physical mechanisms as well as a detailed sensibility analysis regarding the pit design parameters. These parameters include location (water depth), pit length, width, depth, and orientation with respect to the mean flow. A nondimensional analysis based on the model is also carried out to explore the role of the different variables involved in the evolution of offshore sandpits. Based on the field data and the nondimensional analysis, some basic design recommendations for offshore sandpits are proposed.

Statistical wave climate projections for coastal impact assessments

The authors acknowledge the support of the Spanish Ministerio de Economia y Competitividad (MINECO) and European Regional Development Fund (FEDER) under Grant BIA2015-70644-R (MINECO/FEDER, UE). The authors are grateful to Nicolas Ripoll for his help in the performing the statistical simulations. The DAC data is produced by CLS Space Oceanography Division and distributed by Aviso, with support from Cnes (http://www.aviso.altimetry.fr/). The CMIP5 sea level pressure data are available at http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. Mean sea level projections are available at ftp://ftp.icdc.zmaw.de/ar5_sea_level_rise/Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

Introducing marine climate variability into life cycle management of coastal and offshore structures

Coastal and offshore structures are subject to a life cycle process including several different phases. From the planning and design phase to the re-use or demolition phase marine climate information is extremely important to achieve cost effective functionality and technical quality. The complete life cycle may span over several decades what requires site-specific marine climate information at different time scales and including its natural variability. We present an integrated methodology to generate marine climate information relevant for life cycle management of coastal and offshore structures including short-term, seasonal, long-terms and very longterm information. An application of the methodology to a harbour is presented.

Estimating Flooding Level Through the Brazilian Coast Using Reanalysis Data

ABSTRACT Gomes da Silva, P.; Dalinghaus, C.; González, M., Gutiérrez, O., Espejo, A., Abascal, A.J., and Klein, A.H.F., 2016. Estimating flooding level through the Brazilian coast using reanalysis data. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1092 - 1096. Coconut Creek (Florida), ISSN 0749-0208. This paper presents an estimative of coastal flooding level values distribution along the Brazilian coast by using reanalysis data and by simulating wave propagation with Snell approximation. The coast was divided into 24 zones and flooding levels series were calculated for each one. Mean and extreme distribution show higher flooding levels occurring in northern coast (3 m higher than beaches in central coast) followed by the southern coast (1 m higher than central coast). High values obtained on north region were associated with greater tidal elevations (maximum values > 3 m) while higher values in south were related to greater wave heights and higher storm surge elevations (∼2 m and >7 m respectively). A comparison between beach types were also conducted and demonstrate higher values of flooding levels during reflective conditions (0.3 m higher for a specific beach condition), as a result of the runup model applied. Although simplifications were used to calculate this large scale variation of the flooding level along the coast, the results partially presented here can be of large usefulness in pre-design phases of coastal projects and in preliminary risk analysis.