A high resolution partitioned wave hindcast of New Zealand waters is presented together with validation results against 9 buoy deployments that are representative of the local wave climate. An analysis of integrated and partitioned mean wave parameters was conducted together with a study of the correlation between wave height anomalies and atmospheric indices. A directional spatial analysis was also performed to identify and quantify wave systems that are not detectable by in situ buoys or hindcast integrated parameters. The presented study outlines a framework of how partitions can be analyzed in order to improve our understanding of the local wave climate in terms of wind-sea and swell waves.

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

A large-scale field campaign was carried out on the ebb-tidal delta (ETD) of Ameland Inlet, a basin of the Wadden Sea in the Netherlands, as well as on three transects along the Dutch lower shoreface. The data have been obtained over the years 2017-2018. The most intensive campaign at the ETD of Ameland Inlet was in September 2017. With this campaign, as part of KustGenese2.0 (Coastal Genesis 2.0) and SEAWAD, we aim to gain new knowledge on the processes driving sediment transport and benthic species distribution in such a dynamic environment. These new insights will ultimately help the development of optimal strategies to nourish the Dutch coastal zone in order to prevent coastal erosion and keep up with sea level rise. The dataset obtained from the field campaign consists of (i) single-and multi-beam bathymetry; (ii) pressure, water velocity, wave statistics, turbidity, conductivity, temperature, and bedform morphology on the shoal; (iii) pressure and velocity at six back-barrier locations; (iv) bed composition and macrobenthic species from box cores and vibrocores; (v) discharge measurements through the inlet; (vi) depth and velocity from X-band radar; and (vii) meteorological data. The combination of all these measurements at the same time makes this dataset unique and enables us to investigate the interactions between sediment transport, hydrodynamics, morphology and the benthic ecosystem in more detail. The data provide opportunities to calibrate numerical models to a high level of detail. Furthermore, the open-source datasets can be used for system comparison studies. The data are publicly available at 4TU Centre for Research Data at https://doi.org/10.4121/collection:seawad (Delft University of Technology et al., 2019) and https://doi.org/10.4121/collection:kustgenese2 (Rijkswaterstaat and Deltares, 2019). The datasets are published in netCDF format and follow conventions for CF (Climate and Forecast) metadata. The http://data.4tu.nl (last access: 11 November 2020) site provides keyword searching options and maps with the geographical position of the data.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Understanding marine climate variability is important for coastal planning and marine operations. It is also particularly challenging for complicated settings (e.g., islands) and data-poor regions. The aim of this work is to establish a relationship between daily synoptic atmospheric patterns, and wave and storm surge conditions around New Zealand waters, based on instrumental and reanalysis data. The daily predictor we developed is able to represent sea and swell wave conditions as well as storm surge variability over different temporal scales. However, when climate variability is analysed on a longer temporal period, based on the 20th century reanalysis, large inhomogeneities are found. This highlights the dangers related to assessing climate variability, especially in data-poor regions (such as New Zealand), where inhomogeneities could be interpreted as actual changes.

Autoregressive logistic regression models have been demonstrated to be a powerful tool for statistical simulation of spatial patterns in climate and meteorology fields. In this paper we introduce a statistical framework for the simulation of ocean current patterns based on the autoregressive logistic regression models, and apply it to the Gulf of Mexico Loop Current. The statistical model is forced by three autoregressive terms, the wind stress curl in the Gulf of Mexico and in the Caribbean Sea, and the sea level pressure anomalies over the North Atlantic. It is used to replicate the bi-weekly historical sequence of 8 Loop Current patterns, obtained from a 24-year altimetry derived dataset. The model reproduces the inter-annual and intra-annual variability of the original time series, showing notable fitting capacity. A point-by-point comparison between the actual and simulated pattern series confirms the capability of the model in analysing the evolution of ocean current patterns. The predictive skill of the model is also explored, and the preliminary forecast (up to 3 months) results are encouraging. The presented statistical framework may find more practical applications in the future, such as the generation of statistically sound climate-based oceanographic scenarios for risk analyses, and the mid-term probabilistic prediction of ocean current patterns.

Ocean-basin-scale climate variability produces shifts in wave climates and water levels affecting the coastlines of the basin. Here we present a hybrid shoreline change—foredune erosion model (A COupled CrOss-shOre, loNg-shorE, and foreDune evolution model, COCOONED) intended to inform coastal planning and adaptation. COCOONED accounts for coupled longshore and cross-shore processes at different timescales, including sequencing and clustering of storm events, seasonal, interannual, and decadal oscillations by incorporating the effects of integrated varying wave action and water levels for coastal hazard assessment. COCOONED is able to adapt shoreline change rates in response to interactions between longshore transport, cross-shore transport, water level variations, and foredune erosion. COCOONED allows for the spatial and temporal extension of survey data using global data sets of waves and water levels for assessing the behavior of the shoreline at multiple time and spatial scales. As a case study, we train the model in the period 2004–2014 (11 years) with seasonal topographic beach profile surveys from the North Beach Sub-cell (NBSC) of the Columbia River Littoral Cell (Washington, USA). We explore the shoreline response and foredune erosion along 40 km of beach at several timescales during the period 1979–2014 (35 years), revealing an accretional trend producing reorientation of the beach, cross-shore accretional, and erosional periods through time (breathing) and alternating beach rotations that are correlated with climate indices.

This paper proposes a buoy independent directional calibration methodology that uses satellite altimeter data to correct the simultaneous wind-sea and all swell partitions available from wave hindcasts. The proposed technique was applied to a 20-year wave hindcast in the New Zealand region and showed promising results. The method could potentially be applied worldwide to fully assess systematic errors in wind-sea and swell partitions.

Shifts in the frequency of typical meteorological patterns in an ocean basin, over interannual to decadal time scales, cause shifts in the patterns of wave generation. Therefore, ocean basin-scale climate shifts produce shifts in the wave climates affecting the coastlines of the basin. We present a hybrid methodology for downscaling observed (or predicted) climate shifts into local nearshore wave climates and then into the associated coastline responses. A series of statistical analyses translate observed (or predicted) distributions of meteorological states into the deep water wave climate affecting a coastal region and dynamical modeling combined with statistical analyses transform the deep water wave climate into the nearshore wave climate affecting a particular coastline. Finally, dynamical modeling of coastline evolution hindcasts (or predicts) how coastline shapes respond to climate shifts. As a case study, we downscale from meteorological hindcast in the North Atlantic basin since 1870 to the responses of the shape of the coast of the Carolinas, USA. We test the hindcasts using shoreline change rates calculated from historical shorelines, because shifts in coastline shape equate to changes in the alongshore pattern of shoreline change rates from one historical period to another. Although limited by the availability of historical shorelines (and complicated by historical inlet openings), the observations are consistent with the predicted signal of ocean basin-scale climate change. The hybrid downscaling methodology, applied to the output of global climate models, can be used to help forecast future patterns of shoreline change related to future climate change scenarios.

A recent 35-year endpoint shoreline change analysis revealed significant counterclockwise rotations occurring in north-central Oregon, USA, littoral cells that extend 10s of kilometers in length. While the potential for severe El Niños to contribute to littoral cell rotations at seasonal to interannual scale was previously recognized, the dynamics resulting in persistent (multidecadal) rotation were unknown, largely due to a lack of historical wave conditions extending back multiple decades and the difficulty of separating the timescales of shoreline variability in a high energy region. This study addresses this question by (1) developing a statistical downscaling framework to characterize wave conditions relevant for longshore sediment transport during data-poor decades and (2) applying a one-line shoreline change model to quantitatively assess the potential for such large embayed beaches to rotate. A climate INdex was optimized to capture variability in longshore wave power as a proxy for potential LOngshore Sediment Transport (LOST_IN), and a procedure was developed to simulate many realizations of potential wave conditions from the index. Waves were transformed dynamically with Simulating Waves Nearshore to the nearshore as inputs to a one-line model that revealed shoreline rotations of embayed beaches at multiple time and spatial scales not previously discernible from infrequent observations. Model results indicate that littoral cells respond to both interannual and multidecadal oscillations, producing comparable shoreline excursions to extreme El Niño winters. The technique quantitatively relates morphodynamic forcing to specific climate patterns and has the potential to better identify and quantify coastal variability on timescales relevant to a changing climate.

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., 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 "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.

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.

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Interest in understanding long-term coastal morphodynamics has recently increased as climate change impacts become perceptible and accelerated. Multiscale, behavior-oriented and process-based models, or hybrids of the two, are typically applied with deterministic approaches which require considerable computational effort. In order to reduce the computational cost of modeling large spatial and temporal scales, input reduction and morphological acceleration techniques have been developed. Here we introduce a general framework for reducing dimensionality of wave-driver inputs to morphodynamic models. The proposed framework seeks to account for dependencies with global atmospheric circulation fields and deals simultaneously with seasonality, interannual variability, long-term trends, and autocorrelation of wave height, wave period, and wave direction. The model is also able to reproduce future wave climate time series accounting for possible changes in the global climate system. An application of long-term shoreline evolution is presented by comparing the performance of the real and the simulated wave climate using a one-line model.