Li H. Erikson

Doubling of coastal flooding frequency within decades due to sea-level rise

Global climate change drives sea-level rise, increasing the frequency of coastal flooding. In most coastal regions, the amount of sea-level rise occurring over years to decades is significantly smaller than normal ocean-level fluctuations caused by tides, waves, and storm surge. However, even gradual sea-level rise can rapidly increase the frequency and severity of coastal flooding. So far, global-scale estimates of increased coastal flooding due to sea-level rise have not considered elevated water levels due to waves, and thus underestimate the potential impact. Here we use extreme value theory to combine sea-level projections with wave, tide, and storm surge models to estimate increases in coastal flooding on a continuous global scale. We find that regions with limited water-level variability, i.e., short-tailed flood-level distributions, located mainly in the Tropics, will experience the largest increases in flooding frequency. The 10 to 20 cm of sea-level rise expected no later than 2050 will more than double the frequency of extreme water-level events in the Tropics, impairing the developing economies of equatorial coastal cities and the habitability of low-lying Pacific island nations.

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995–2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the models predictive capability when applied to the forecast period (2010–2100) driven by GCM-projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

Synthesis Study of an Erosion Hot Spot, Ocean Beach, California

Abstract Barnard, P.L.; Hansen, J.E., and Erikson, L.H., 2012. Synthesis study of an erosion hot spot, Ocean Beach, California (USA). A synthesis of multiple coastal morphodynamic research efforts is presented to identify the processes responsible for persistent erosion along a 1-km segment of 7-km-long Ocean Beach in San Francisco, California. The beach is situated adjacent to a major tidal inlet and in the shadow of the ebb-tidal delta at the mouth of San Francisco Bay. Ocean Beach is exposed to a high-energy wave climate and significant alongshore variability in forcing introduced by varying nearshore bathymetry, tidal forcing, and beach morphology (e.g., beach variably backed by seawall, dunes, and bluffs). In addition, significant regional anthropogenic factors have influenced sediment supply and tidal current strength. A variety of techniques were employed to investigate the erosion at Ocean Beach, including historical shoreline and bathymetric analysis, monthly beach topographic surveys, nearshore and regional bathymetric surveys, beach and nearshore grain size analysis, two surf-zone hydrodynamic experiments, four sets of nearshore wave and current experiments, and several numerical modeling approaches. Here, we synthesize the results of 7 years of data collection to lay out the causes of persistent erosion, demonstrating the effectiveness of integrating an array of data sets covering a huge range of spatial scales. The key findings are as follows: anthropogenic influences have reduced sediment supply from San Francisco Bay, leading to pervasive contraction (i.e., both volume and area loss) of the ebb-tidal delta, which in turn reduced the regional grain size and modified wave focusing patterns along Ocean Beach, altering nearshore circulation and sediment transport patterns. In addition, scour associated with an exposed sewage outfall pipe causes a local depression in wave heights, significantly modifying nearshore circulation patterns that have been shown through modeling to be key drivers of persistent erosion in that area.

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).

Seasonal Electrical Resistivity Surveys of a Coastal Bluff, Barter Island, North Slope Alaska

Select coastal regions of the North Slope of Alaska are experiencing high erosion rates that can be attributed in part to recent warming trends and associated increased storm intensity and frequency. The upper sediment column of the coastal North Slope of Alaska can be described as continuous permafrost underlying a thin (typically less than 1–2 m) active layer that responds variably to seasonal thaw cycles. Assessing the temporal and spatial variability of the active layer and underlying permafrost is essential to better constrain how heightened erosion may impact material fluxes to the atmosphere and the coastal ocean, and how enhanced thaw cycles may impact the stability of the coastal bluffs. In this study, multi-channel electrical resistivity tomography (ERT) was used to image shallow subsurface features of a coastal bluff west of Kaktovik, on Barter Island, northeast Alaska. A comparison of a suite of paired resistivity surveys conducted in early and late summer 2014 provided detailed information on how the active layer and permafrost are impacted during the short Arctic summer. Such results are useful in the development of coastal resilience models that tie together fluvial, terrestrial, climatic, geologic, and oceanographic forcings on shoreline stability.

Environmental controls on spatial patterns in the long‐term persistence of giant kelp in central California

As marine management measures increasingly protect static areas of the oceans, it is important to make sure protected areas capture and protect persistent populations. Rocky reefs in many temperate areas worldwide serve as habitat for canopy-forming macroalgae and these structure-forming species of kelps (order Laminariales) often serve as important habitat for a great diversity of species. Macrocystis pyrifera is the most common canopy-forming kelp species found along the coast of California, but the distribution and abundance of M. pyrifera varies in space and time. The purpose of this study is to determine what environmental parameters are correlated with and their relative contribution to the spatial and temporal persistence of M. pyrifera along the central coast of California and how well those environmental parameters can be used to predict areas where this species is more likely to persist. Nine environmental variables considered in this study included depth of the seafloor, structure of the rocky reef, proportion of rocky reef, size of kelp patch, biomass of kelp within a patch, distance from the edge of a kelp patch, sea surface temperature, wave orbital velocities, and population connectivity of individual kelp patches. Using a generalized linear mixed effects model (GLMM), the persistence of M. pyrifera was significantly associated with seven of the nine variables considered: depth, complexity of the rocky reef, proportion of rock, patch biomass, distance from the edge of a patch, population connectivity, and wave orbital velocities. These seven environmental variables were then used to predict the persistence of kelp across the central coast, and these predictions were compared to a reserved dataset of M. pyrifera persistence, which was not used in the creation of the GLMM. The environmental variables were shown to accurately predict the persistence of M. pyrifera within the central coast of California (r = 0.71, P < 0.001). Because persistence of giant kelp is important to the community structure of kelp forests, understanding those factors that support persistent populations of M. pyrifera will enable more effective management of these ecosystems.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically-downscaled GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily averaged near-surface winds at an appropriate spatial resolution for wave modeling within the orographically-complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short-duration high wind speeds, on the order of hours are usually excluded in statistically-downscaled climate models, and are of key importance in wave and subsequent coastal flood modeling. Here, we present a temporal downscaling approach, similar to constructed analogues, for near-surface winds suitable for use in local wave models, and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975-2004) recreate important extreme wind values within San Francisco Bay. A computationally-efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over-water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but twenty-first century projections show little change in the overall magnitude of extreme winds and locally-generated waves.

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.