Ana Rueda

Integrated treatment of landfill leachates including electrooxidation at pilot plant scale.

This paper reports the integration of advanced and conventional technologies to deal with the treatment of landfill leachates. The raw leachate, with average values of COD=4430 mg/L and N-NH(4)(+)=1225 mg/L, was first treated on site by an activated sludge large-scale process reducing the former parameters to 1750 mg/L (av.) of COD and 750 mg/L (av.) of N-NH(4)(+). Next, 50 L/h of the effluent were pumped to a pilot plant that included Fenton oxidation followed by an electrooxidation unit, provided with boron doped diamond anodes (anode area=1.05 m(2)); almost complete removal of the organic matter and ammonium nitrogen was achieved. Comparison of the results with those obtained in the laboratory (70 cm(2) of anode area) was performed observing a similar performance in the kinetics of COD removal, while differences were found in the ammonium removal rates. The specific energy consumption necessary to electro-oxidize the organic load below the disposal limit (COD<160 mg/L) at pilot plant scale was 35 kWh/m(3).

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.

A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

AbstractCharacterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “w...

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.

Multiscale climate emulator of multimodal wave spectra: MUSCLE‐spectra

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications. This article is protected by copyright. All rights reserved.We thank Jorge Perez for the ESTELA code. A.R., J.A.A.A., and F.J.M. acknowledge the support of the Spanish ‘‘Ministerio de Economia y Competitividad’’ under grant BIA2014-59643-R. P.C. acknowledges the support of the Spanish ‘‘Ministerio de Economia y Competitividad’’ under grant BIA2015-70644-R. J.A.A.A. is indebted to the MEC (Ministerio de Educacion, Cultura y Deporte, Spain) for the funding provided in the FPU (Formacion del ProfesoradoUniversitario) studentship (BOE-A-2013-12235). This material is based upon work supported by the U.S. Geological Survey under grant/cooperative agreement G15AC00426. P.R. acknowledges the support of the National Oceanic and Atmospheric Administration Climate Program Office via award NA15OAR4310145. Support was provided from the US DOD Strategic Environmental Research and Development Program (SERDP Project RC-2644) through the NOAA National Centers for Environmental Information (NCEI). Atmospheric data from CFSR are available online at https://climatedataguide.ucar.edu/climatedata/climate-forecast-system-reanalysis-cfsr. Marine data from global reanalysis are lodge with the IHData center from IHCantabria and are available for research purposes upon request (contact: ihdata@ihcantabria.com).

An atmospheric-to-marine synoptic classification for statistical downscaling marine climate

A regression-guided classification is implemented in statistical downscaling models based on weather types for downscaling multivariate wave climate and modelling extreme events. The semi-supervised method classifies the atmospheric circulation conditions (predictor) and the estimations from a regression model linking the circulation with local marine climate (filtered predictand). A weighted factor controls the influence of the predictor and predictand in the weather patterns to improve the performance of the classification to reflect local marine climate characteristics. In addition to the analysis of the variance explained by the predictor and the predictand, the selection of the optimal value of the weighted factor is based on the predictand response in order to avoid subjectivity in the solution. The statistical models using the guided classification are applied in the North Atlantic. The new technique reduces the dispersion of the multivariate predictand within weather types and improves the model skill to downscale waves and to reproduce extremes.

An extreme value model for maximum wave heights based on weather types

Extreme wave heights are climate-related events. Therefore, special attention should be given to the large-scale weather patterns responsible for wave generation in order to properly understand wave climate variability. We propose a classification of weather patterns to statistically downscale daily significant wave height maxima to a local area of interest. The time-dependent statistical model obtained here is based on the convolution of the stationary extreme value model associated to each weather type. The interdaily dependence is treated by a climate-related extremal index. The models ability to reproduce different time scales (daily, seasonal, and interannual) is presented by means of its application to three locations in the North Atlantic: Mayo (Ireland), La Palma Island, and Coruna (Spain).

Controls of Multimodal Wave Conditions in a Complex Coastal Setting

Coastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low-frequency atmosphere-ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave-generating atmospheric patterns with coastal conditions. We present a hybrid statistical-dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large-scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.

A Meta-Modelling Approach for Estimating Long-Term Wave Run-Up and Total Water Level on Beaches

ABSTRACT Gainza, J.; Rueda, A.; Camus, P.; Tomás, A.; Méndez, F.J.; Sano, M., and Tomlinson, R., 2018. A meta-modelling approach for estimating long-term wave run-up and total water level on beaches. Wave run-up is defined as the maximum vertical extent of wave up-rush on a beach or structure above the sea water level from wave breaking. Wave run-up is responsible for beach and dune erosion and can be an important component of coastal flooding. Run-up can be estimated using either empirical formulations or sophisticated wave-breaking models with high computational demand. On the other hand, meta-models are efficient approximations of physical-process models that enable researchers to obtain long-term time series of wave dynamics. These hybrid models are developed by combining statistical techniques and numerical models. In this study, a methodology to transform offshore sea conditions to long-term time series of wave run-up is described. The methodology combined the construction of two meta-models of offshore wave propagation to coastal areas and of nearshore wave transformation to run-up. Clustering techniques were then implemented to select a subset of spectral patterns of the offshore conditions for nearshore transfer and a subset of sea states for reconstructing the run-up. Multivariate, radial-basis functions were then fitted to the outputs of the wave propagation and wave run-up simulations to reconstruct the time series of sea-state parameters in shallow water and the time series of run-up. This methodology was applied to Palm Beach on the Gold Coast (QLD, Australia). The nearshore wave climate was validated quantitatively, whereas the reconstructed wave run-up and total water-level time series was validated with a qualitative approximation, confirming that this methodology is capable of accurately transforming the offshore wave conditions into run-up time series. The total water levels were also reconstructed to show the applicability of the results to probabilistic flood-risk analyses.