A multiscale climate emulator for long‐term morphodynamics (MUSCLE‐morpho)

Antolínez, José A. Á.; Méndez, Fernando J.; Camus, Paula; Vitousek, Sean; Gonzalez, E. M.; Ruggiero, Peter; Barnard, Patrick L.

doi:10.1002/2015jc011107

Cited by 48 publications

(47 citation statements)

References 42 publications

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“…Several extreme value models have been developed recently to deal with nonstationarity [ Katz et al ., ; Mendez et al ., ; Serafin and Ruggiero , ]. Long‐term projections of sea states are required for many offshore and coastal applications [ Solari and Losada , ] or probabilistic estimation of wave‐induced coastal erosion [ Walstra et al ., ; Callaghan et al ., ; Corbella and Stretch , 2012; Antolinez et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Rueda

Hegermiller

Antolínez

et al. 2017

J. Geophys. Res. Oceans

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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.

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Section: Introductionmentioning

confidence: 99%

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Rueda

Hegermiller

Antolínez

et al. 2017

J. Geophys. Res. Oceans

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“…Using an ensemble of computationally expensive models (e.g., 3‐D physics‐based models) becomes significantly less practical. Therefore, ensemble model predictions will likely use computationally efficient process‐based models and/or statistical downscaling [ Antolínez et al , ; Rueda et al , ] versus dynamical downscaling (i.e., nested models).…”

Section: Integrating Data and Modelsmentioning

confidence: 99%

Can beaches survive climate change?

2017

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Anthropogenic climate change is driving sea level rise, leading to numerous impacts on the coastal zone, such as increased coastal flooding, beach erosion, cliff failure, saltwater intrusion in aquifers, and groundwater inundation. Many beaches around the world are currently experiencing chronic erosion as a result of gradual, present‐day rates of sea level rise (about 3 mm/year) and human‐driven restrictions in sand supply (e.g., harbor dredging and river damming). Accelerated sea level rise threatens to worsen coastal erosion and challenge the very existence of natural beaches throughout the world. Understanding and predicting the rates of sea level rise and coastal erosion depends on integrating data on natural systems with computer simulations. Although many computer modeling approaches are available to simulate shoreline change, few are capable of making reliable long‐term predictions needed for full adaption or to enhance resilience. Recent advancements have allowed convincing decadal to centennial‐scale predictions of shoreline evolution. For example, along 500 km of the Southern California coast, a new model featuring data assimilation predicts that up to 67% of beaches may completely erode by 2100 without large‐scale human interventions. In spite of recent advancements, coastal evolution models must continue to improve in their theoretical framework, quantification of accuracy and uncertainty, computational efficiency, predictive capability, and integration with observed data, in order to meet the scientific and engineering challenges produced by a changing climate.

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“…This class of models is robust but usually requires high computational effort. Therefore, techniques to increase computational efficiency have been developed such as model reduction of complex systems focusing in the relevant processes (Cowell, Stive, Niedoroda, Vriend, et al, ; de Vriend et al, ), morphological acceleration and consequent hydrodynamic run time reduction (Roelvink, ), and input reduction techniques (Antolínez et al, ; Walstra et al, ). Nevertheless, physics‐driven models do not necessarily offer more accurate results than process‐driven models (French et al, ; Murray, ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Climate‐Driven Coastlines With a Simple and Efficient Multiscale Model

et al. 2019

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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.

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A multiscale climate emulator for long‐term morphodynamics (MUSCLE‐morpho)

Cited by 48 publications

References 42 publications

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Can beaches survive climate change?

Predicting Climate‐Driven Coastlines With a Simple and Efficient Multiscale Model

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