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Santa Cruz, CA, United States

Barnard P.L.,Pacific Coastal and Marine Science Center | van Ormondt M.,Deltares | Erikson L.H.,Pacific Coastal and Marine Science Center | Eshleman J.,National Park Service | And 4 more authors.
Natural Hazards | Year: 2014

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning. © 2014, Us Government.

Gallien T.W.,University of California at Irvine | Barnard P.L.,Pacific Coastal and Marine Science Center | Van Ormondt M.,Deltares | Foxgrover A.C.,Pacific Coastal and Marine Science Center | Sanders B.F.,University of California at Irvine
Journal of Coastal Research | Year: 2013

Coastal flood risk in California is concentrated around urbanized embayments that are protected by infrastructure, such as levees, pumps, and flood walls, which pose a challenge to accurate flood prediction. A capability to predict coastal urban flooding at the parcel-scale (individual home or street) from high ocean levels (extreme high tides) is shown here by coupling a regional ocean forecasting system to an embayment-scale hydrodynamic model that incorporates detailed information about flood defenses. A unique flooding data set affords the rare opportunity to validate model predictions and allows us to identify model data that are essential for accurate forecasting. In particular, results show that flood defense height data are critical, and here, that information is supplied by a Real Time Kinematic Global Positioning System (RTK-GPS) survey, which yields ca. 1-cm, vertical root mean-squared error accuracy. Bathymetry surveys and aerial Light Detection and Ranging (LIDAR) data characterizing the embayment also prove essential. Moreover, hydrodynamic modeling of flood inundation is shown to significantly improve on planar surface models, which overestimate inundation, particularly when manipulated to account for run-up in a simplistic way. This is attributed to the transient nature of overtopping flows and motivates the need for dynamic, spatially-distributed overtopping models that are tailored to the urban environment. © 2012, the Coastal Education & Research Foundation.

Rueda A.,University of Cantabria | Camus P.,University of Cantabria | Tomas A.,University of Cantabria | Vitousek S.,Pacific Coastal and Marine Science Center | Mendez F.J.,University of Cantabria
Ocean Modelling | Year: 2016

Coastal floods often coincide with large waves, storm surge and tides. Thus, joint probability methods are needed to properly characterize extreme sea levels. This work introduces a statistical downscaling framework for multivariate extremes that relates the non-stationary behavior of coastal flooding events to the occurrence probability of daily weather patterns. The proposed method is based on recently-developed weather-type methods to predict extreme events (e.g., significant wave height, mean wave period, surge level) from large-scale sea-level pressure fields. For each weather type, variables of interest are modeled using Generalized Extreme Value (GEV) distributions and a Gaussian copula for modelling the interdependence between variables. The statistical dependence between consecutive days is addressed by defining a climate-based extremal index for each weather type. This work allows attribution of extreme events to specific weather conditions, enhancing the knowledge of climate-driven coastal flooding. © 2016 Elsevier Ltd

Alvarez J.A.A.,University of Cantabria | Mendez F.J.,University of Cantabria | Camus P.,University of Cantabria | Vitousek S.,Pacific Coastal and Marine Science Center | And 3 more authors.
Journal of Geophysical Research: Oceans | Year: 2016

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. © 2015. American Geophysical Union.

Barnard P.L.,Pacific Coastal and Marine Science Center | Erikson L.H.,Pacific Coastal and Marine Science Center | Kvitek R.G.,California State University, Monterey Bay
Geo-Marine Letters | Year: 2011

New multibeam echosounder and processing technologies yield sub-meter-scale bathymetric resolution, revealing striking details of bedform morphology that are shaped by complex boundary-layer flow dynamics at a range of spatial and temporal scales. An inertially aided post processed kinematic (IAPPK) technique generates a smoothed best estimate trajectory (SBET) solution to tie the vessel motion-related effects of each sounding directly to the ellipsoid, significantly reducing artifacts commonly found in multibeam data, increasing point density, and sharpening seafloor features. The new technique was applied to a large bedform field in 20-30 m water depths in central San Francisco Bay, California (USA), revealing bedforms that suggest boundary-layer flow deflection by the crests where 12-m-wavelength, 0.2-m-amplitude bedforms are superimposed on 60-m-wavelength, 1-m-amplitude bedforms, with crests that often were strongly oblique (approaching 90°) to the larger features on the lee side, and near-parallel on the stoss side. During one survey in April 2008, superimposed bedform crests were continuous between the crests of the larger features, indicating that flow detachment in the lee of the larger bedforms is not always a dominant process. Assessment of bedform crest peakedness, asymmetry, and small-scale bedform evolution between surveys indicates the impact of different flow regimes on the entire bedform field. This paper presents unique fine-scale imagery of compound and superimposed bedforms, which is used to (1) assess the physical forcing and evolution of a bedform field in San Francisco Bay, and (2) in conjunction with numerical modeling, gain a better fundamental understanding of boundary-layer flow dynamics that result in the observed superimposed bedform orientation. © 2011 Springer-Verlag (outside the USA).

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