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17 pages, 15568 KiB  
Article
Pleistocene Glacial Transport of Nephrite Jade from British Columbia, Canada, to Coastal Washington State, USA
by George E. Mustoe
Geosciences 2024, 14(9), 242; https://doi.org/10.3390/geosciences14090242 - 9 Sep 2024
Cited by 1 | Viewed by 1505
Abstract
Since prehistoric times, indigenous residents of southwest British Columbia, Canada, collected water-worn nephrite specimens from the gravel bars along the Fraser River, using the stone for the manufacture of tools that were widely traded with other tribes. Allochthonous nephrite occurs in another geologic [...] Read more.
Since prehistoric times, indigenous residents of southwest British Columbia, Canada, collected water-worn nephrite specimens from the gravel bars along the Fraser River, using the stone for the manufacture of tools that were widely traded with other tribes. Allochthonous nephrite occurs in another geologic setting. Late Pleistocene continental glaciers transported nephrite and many other rock types from western Canada to northwest Washington State, producing extensive sediment deposits that border the Salish Sea coast in Whatcom and Island Counties, Washington. This material was little utilized by indigenous residents, but “black jade” specimens are prized by modern collectors. The depositional history and mineralogy of this material has received little attention. X-ray diffraction and SEM/EDS analyses indicate that the Salish Sea “black jade” is a form of impure nephrite that probably originated from metamorphism of a mafic igneous parent material (metabasite). The texture consists of prismatic amphibole crystals (ferro-actinolite) set in a matrix rich in plagioclase feldspar. Pyrite inclusions are locally present. A second material, sometimes erroneously labelled “muttonfat jade” by amateur collectors, consists of an intermixture of quartz and sillimanite. Full article
(This article belongs to the Section Cryosphere)
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Figure 1

Figure 1
<p>Tectonic setting of the western Washington and Canada. (<b>A</b>) Ophiolite complex geologic sequence (not to scale). (<b>B</b>). Cascadia Subduction Zone. Both images have been adapted from Creative Commons 3.0 licensed mages: (<b>A</b>) Wikipedia.org file: Ofioliti.org.svg). (<b>B</b>) U.S. Geological Survey graphics.</p>
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<p>Occurrences of ultramafic bedrock in western Washington, USA.</p>
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<p>Regional map of the Salish Sea, which includes the Straits of Georgia and Juan de Fuca and the complex channels and embayments of Puget Sound.</p>
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<p>Maps of Salish Sea coast showing cobblestone beaches where erosion of glacial sediment releases nephrite and related materials.</p>
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<p>Naturally polished pebble and cobble of Salish Sea “black jade”.</p>
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<p>The author with a “black jade” boulder exposed in the intertidal zone, Swantown beach, Whidbey Island, WA. This specimen was transported as a glacial erratic, eroded in modern time from late Pleistocene sediment that forms a cliff bordering the coastline (visible in background). 2024 photo by Wendy Walker, used with permission.</p>
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<p>Late Pleistocene advance of the continental glacier was dominantly from north to south. During interglacial intervals, sediment was transported southwest by the Thompson River and the Fraser River Red arrows show ice flow directions, as determined from glacial striations and geomorphic features. Map adapted from [<a href="#B11-geosciences-14-00242" class="html-bibr">11</a>,<a href="#B25-geosciences-14-00242" class="html-bibr">25</a>].</p>
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<p>Granitic glacial erratics at Point Whitehorn. A large angular erratic is weathering out of the coastal bluff, evidence that the sediment at this location arrived as ice-transported material, not from fluvial transport.</p>
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<p>“Black jade” (nephrite) occurs along the Salish Sea coast on cobbled beaches adjacent to high banks of Pleistocene sediment. (<b>A</b>) Point Whitehorn coast in northwestern Whatcom County, Washington. (<b>B</b>) Cobblestone beach at Libbey Beach County Park, Whidbey Island, Island County, Washington. (<b>C</b>) Nephrite pebble (red arrow) can be seen eroding from a stratum that contains water-worn clasts set in a matrix of outwash sand, Point Whitehorn. (<b>D</b>) Nephrite cobble partially encrusted with barnacles in the upper intertidal zone at Swantown beach, Whidbey Island. 2024 photos by the author.</p>
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<p>Typical XRD pattern for the Salish Sea “black jade”.</p>
Full article ">Figure 11
<p>Backscattered electron (BSE) images of the Salish Sea “black jade”. (<b>A</b>) Prismatic amphibole crystals in an albite-rich matrix. (<b>B</b>) Amphibole crystal cluster showing deformation curvature. Smaller angular crystals are albite. (<b>C</b>) Polished surface showing radiating amphibole crystals in an albite-rich matrix that contains small silicate mineral inclusions. A small pyrite inclusion is marked with an arrow. (<b>D</b>) High magnification view of albite crystals with scattered flakes of biotite. (<b>E</b>) Large pyrite inclusions bordered by quartz (dark gray). Medium gray zones are albite-rich matrix, with radiating needles of amphibole visible at right. (<b>F</b>) Close-up view of a pyrite inclusion showing linear morphology. Pyrite was identified based on EDS spectra that showed Fe and S as the components.</p>
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<p>Salish Sea specimen showing blocky morphology of amphibole. (<b>A</b>) Light areas are amphibole; dark areas are albite rich. (<b>B</b>) Magnetite inclusion in an amphibole crystal.</p>
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<p>SEM/EDS maps showing distribution of major elements. Amphibole crystal clusters contain abundant Fe and Mg. Elevated Na levels indicate that the plagioclase is albite. Small inclusions are presumed to be biotite because of their high K content and tabular shape.</p>
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<p>Mg/(Mg + Fe) atomic ratios for six Salish Sea amphibole crystals show the ferro-actinolite composition.</p>
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<p>Felted microtexture of Washington nephrite. (<b>A</b>) Backscattered electron image of polished nephrite, specimen WWU-DC-3. Felted microtexture of Washington nephrite. (<b>B</b>) SEM image, nephrite specimen WWU-DC-4. (<b>C</b>) Specimen DC-4, showing partial alignment of tremolite microcrystals. (<b>D</b>) Specimen WWU-DC-2 has randomly ordered bladed microcrystals.</p>
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<p>BSE images of black nephrite from Perth region of Western Australia. (<b>A</b>) Secondary image showing interlocking prismatic crystals of actinolite. (<b>B</b>) Secondary electron image of another area in the same small specimen.</p>
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<p>Sillimanite/quartz assemblage. (<b>A</b>) Sawn surface of a typical specimen. (<b>B</b>) Thin section photographs showing quartz (white) and sillimanite (yellowish brown). The parallel alignment of crystals is a result of metamorphic foliation. Specimen WWU-SILL-1.</p>
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15 pages, 2115 KiB  
Article
In-Water Photo Identification, Site Fidelity, and Seasonal Presence of Harbor Seals (Phoca vitulina richardii) in Burrows Pass, Fidalgo Island, Washington
by Ciera J. Edison, Cindy R. Elliser and Katrina H. White
Oceans 2024, 5(2), 368-382; https://doi.org/10.3390/oceans5020022 - 4 Jun 2024
Viewed by 2339
Abstract
Little is known about the in-water behavior and site fidelity of harbor seals (Phoca vitulina richardii), as most photo-identification (photo-ID) studies are typically conducted while they are hauled-out on land. We investigated in-water site fidelity rates and seasonal presence in Burrows [...] Read more.
Little is known about the in-water behavior and site fidelity of harbor seals (Phoca vitulina richardii), as most photo-identification (photo-ID) studies are typically conducted while they are hauled-out on land. We investigated in-water site fidelity rates and seasonal presence in Burrows Pass, Washington, using photographs collected during a long-term photo-ID and behavioral study from January 2015 through November 2019. There was a minimum of 161 individuals and a maximum of 286 individual harbor seals using Burrows Pass. Harbor seals were present in all seasons, with the lowest sighting rates during summer. Individuals were more likely to be sighted/re-sighted in fall and spring. There was large variations in the level and seasonality of site fidelity among individuals. The majority of seals (69.62%) were seen only once, but 22.69% showed low to moderate site fidelity (2–5 sightings) and 7.69% showed strong site fidelity (≥6 sightings) over seasons and across years. These seasonal variations were likely due to foraging, life history, and individual behavioral variabilities. Studies like this provide necessary information about harbor seal in-water site fidelity and behavior, which are less well known but vitally important in harbor seal management and conservation. Full article
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Figure 1

Figure 1
<p>Map of the land-based observation point and study area. Shaded areas indicate land, white indicates water. The black square on Fidalgo Island (north of Burrows Pass) represents the location of the land-based observation point. The entire passage between islands (located within the black semi-circle) represents the study area. Inset shows the study area (shaded) in relation to surrounding San Juan Islands, Washington.</p>
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<p>Examples of identified harbor seal photo-ID matches, where the photographs on the left represent a first identifiable photo of an individual and on the right are the matched photos taken 2 to 3 years later: (<b>a</b>) Seal#2, Left Head—1/28/15 and 1/15/18; (<b>b</b>) Seal#41, Right Head—9/30/15 and 12/4/17; (<b>c</b>) Seal#4, Left Head—2/20/15 and 3/7/18.</p>
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<p>Discovery curves calculated using (<b>a</b>) the maximum number of harbor seals identified (<span class="html-italic">n</span> = 286) and (<b>b</b>) the minimum number of harbor seals identified (<span class="html-italic">n</span> = 161) per season during the study period. New individuals identified are shown in white, and previously identified seals are shown in gray.</p>
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<p>Seasonal variations in habitat use by harbor seals identified in the study area, categorized by site fidelity levels (no sight fidelity—individuals seen only once, low—individuals identified twice, moderate—individuals identified 3–5 times, or high—individuals identified 6 or more times) and the respective number of sightings per season.</p>
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<p>The seasonal variation in re-sightings of the 22 identified seals with high site fidelity (seen ≥6 times) in Burrows Pass during the study period.</p>
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23 pages, 26754 KiB  
Article
Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA)
by Kees Nederhoff, Sean C. Crosby, Nate R. Van Arendonk, Eric E. Grossman, Babak Tehranirad, Tim Leijnse, Wouter Klessens and Patrick L. Barnard
Water 2024, 16(2), 346; https://doi.org/10.3390/w16020346 - 20 Jan 2024
Cited by 4 | Viewed by 2675
Abstract
The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s [...] Read more.
The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1
<p>Whatcom County is located in the Pacific Northwest of the United States of America (panel (<b>A</b>)). Panel (<b>B</b>) provides an overview of the area of interest in Whatcom County, Washington, and numbered SFINCS model domains. Panel (<b>C</b>) shows the validation site with an observed wrack line in Birch Bay for the December 2018 storm, and XBeach model domain. © Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.</p>
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<p>PS-CoSMoS workflow. Black boxes are data sources or outputs. Orange circles are pre- and-post-processing steps. Pink boxes are numerical models. Workflow in the green box is described in this paper.</p>
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<p>Photos of damage taken during (<b>left</b>) and after (<b>right</b>) a flood event at Birch Bay, Washington, in December 2018 storm. Pictures taken along Birch Bay Drive. See <a href="#water-16-00346-f001" class="html-fig">Figure 1</a> for specific location of both pictures.</p>
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<p>Modeled time series of still water level (panel (<b>A</b>); [<a href="#B19-water-16-00346" class="html-bibr">19</a>]), wave height (blue), period (red, panel (<b>B</b>), [<a href="#B20-water-16-00346" class="html-bibr">20</a>]), wind speed (red), and direction (red; panel (<b>C</b>); both based on HRDPS). Information extracted in the middle of Birch Bay. See <a href="#water-16-00346-f001" class="html-fig">Figure 1</a> for the location.</p>
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<p>Alongshore-varying wave height (H<sub>s</sub>; <b>top</b> panel) and maximum water level (z<sub>smax</sub>; <b>bottom</b> panel) as computed by XBeach-2D and SFINCS. The shading represents the 95% confidence interval (−2 and +2 standard deviations) based on the uncertainty of the boundary conditions. For a cross-shore interpretation, see <a href="#water-16-00346-f006" class="html-fig">Figure 6</a>.</p>
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<p>Cross-shore-varying wave height (H<sub>s</sub>; <b>top</b> panel) and maximum water level (z<sub>smax</sub>; <b>bottom</b> panel) as computed using XBeach-2D and SFINCS. The shading represents the 95% confidence interval (−2 and +2 standard deviations) based on the uncertainty of the boundary conditions. For an alongshore interpretation, see <a href="#water-16-00346-f005" class="html-fig">Figure 5</a>.</p>
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<p>Flood depth as computed using XB-2D (panel (<b>A</b>)) and SFINCS (panel (<b>B</b>)) compared to observed wrack line (red). © Microsoft Bing Maps.</p>
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<p>Runup extent as observed (purple), computed with XB-2D (red) and SFINCS (blue). Colors depict the bed level in meter relative to NAVD88. Figure uses a cross-shore- and alongshore-distance coordinate system.</p>
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<p>Total water level (TWL) for the no-storm condition (panel (<b>A</b>) and as function of return period for the current sea level (panel (<b>B</b>)). Red error bar in A represents the 95% CI (different values indicate spatial variability of the best-guess across Whatcom County, Washington). MHHW is estimated to be 2.4 m+NAVD88 based on the nearest station.</p>
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<p>Change in maximum total water level (∆TWL) as a function of sea-level rise. Different panels represent different return periods: panel (<b>A</b>) shows the no-storm conditions, panel (<b>B</b>) the annual recurrence, (<b>C</b>) the 10-year recurrence, and (<b>D</b>) the 100-year recurrence.</p>
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<p>Example output from PS-CoSMoS model for Birch Bay, Washington. Panel (<b>A</b>). Progressing storm extent for different storm frequencies for a sea-level-rise scenario of 50 cm. Panel (<b>B</b>). Progressing storm extent for different sea-level-rise scenarios for a storm frequency of 50 years. Panel (<b>C</b>). Water depth for a storm with a storm frequency of 50 years and 50 cm sea-level rise. Panel (<b>D</b>). Duration of the flooding for a storm with a storm frequency of 50 years and 50 cm sea-level rise. © Microsoft Bing Maps.</p>
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<p>Flood extent for Whatcom County as function of sea level (SLR) for different return periods. Shading represents the 95% CI interval of the flood simulation (+/− 50 cm offshore water level). Uncertainty is based on errors in the offshore water level, wave height, and digital elevation model (DEM).</p>
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<p>Flood damage as function of return period for 1 m sea-level rise. Colors depict different estimates (median, 68% CI, 95% CI). Left axis shows the damage in USD million (USD M) and right axis as damage relative to total value in percentages.</p>
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<p>Expected Annual Damage (EAD) as a function of time horizon (<span class="html-italic">x</span>-axis) and projection (colors). Shading represents uncertainty in the sea-level-rise projection (low and high estimates from [<a href="#B3-water-16-00346" class="html-bibr">3</a>]).</p>
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<p>Difference in flooding computed using FEMA and PS-CoSMoS. Panel (<b>A</b>). Birch Bay. Panel (<b>B</b>). Nooksack Delta. © Microsoft Bing Maps.</p>
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33 pages, 4701 KiB  
Article
Evaluating Metabarcoding Markers for Identifying Zooplankton and Ichthyoplankton Communities to Species in the Salish Sea: Morphological Comparisons and Rare, Threatened or Invasive Species
by Carol A. Stepien, Haila K. Schultz, Sean M. McAllister, Emily L. Norton and Julie E. Keister
DNA 2024, 4(1), 1-33; https://doi.org/10.3390/dna4010001 - 22 Dec 2023
Cited by 2 | Viewed by 2080
Abstract
Zooplankton and ichthyoplankton community assessments depend on species diagnostics, yet morphological identifications are time-consuming, require taxonomic expertise, and are hampered by a lack of diagnostic characters, particularly for larval stages. Metabarcoding can identify multiple species in communities from short DNA sequences in comparison [...] Read more.
Zooplankton and ichthyoplankton community assessments depend on species diagnostics, yet morphological identifications are time-consuming, require taxonomic expertise, and are hampered by a lack of diagnostic characters, particularly for larval stages. Metabarcoding can identify multiple species in communities from short DNA sequences in comparison to reference databases. To evaluate species resolution across phylogenetic groups and food webs of zooplankton and ichthyoplankton, we compare five metabarcode mitochondrial (mt)DNA markers from gene regions of (a) cytochrome c oxidase subunit I, (b) cytochrome b, (c) 16S ribosomal RNA, and (d) 12S ribosomal RNA for DNA extracted from net tows in the Northeastern Pacific Ocean’s Salish Sea across seven sites and two seasons. Species resolved by metabarcoding are compared to invertebrate morphological identifications and biomass estimates. Results indicate that species resolution for different zooplankton and ichthyoplankton taxa can markedly vary among gene regions and markers in comparison to morphological identifications. Thus, researchers seeking “universal” metabarcoding should take caution that several markers and gene regions likely will be needed; all will miss some taxa and yield incomplete overlap. Species resolution requires careful attention to taxon marker selection and coverage in reference sequence repositories. In summary, combined multi-marker metabarcoding and morphological approaches improve broadscale zooplankton diagnostics. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the southern Salish Sea (the Strait of Juan de Fuca and Puget Sound), showing the seven biological sampling site sites for Washington Ocean Acidification Center (WOAC) research cruises. Sites are lettered A–G (WOAC system designations in parentheses). A is the most oceanic-influenced (saline) year-round, and the Hood Canal sites are F and G.</p>
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<p>Taxa resolved to the phylogenetic level of class by morphological analysis and/or one or more metabarcoding marker(s). Filled circles denote identification; empty circles indicate that the taxon was not detected.</p>
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<p>Non-metric multi-dimensional scaling (NMDS) diagram illustrating relative abundance comparisons for unique taxa from morphology and metabarcoding. Morphology includes individual counts (density = #m<sup>−3</sup>) and carbon biomass, and metabarcoding shows relative sequence counts from metabarcoding markers (LrCOI, Cop16S, Mol16S—with and without the fish blocking primer, MiFish12S, FishCytb).</p>
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<p>Venn diagrams illustrate the number of species identified in common and those uniquely resolved by morphology or metabarcoding. (<b>A</b>). LrCOI and Mol16S (without the fish blocker primer) comparing equivalent samples (<b>B</b>). LrCOI, Cop16S, and Mol16S—with and without the fish blocker—for all available samples. Note that differences in the numbers of species resolved reflect (<b>A</b>) inclusion of all samples that were in common for morphology and for two of the zooplankton markers versus (<b>B</b>) inclusion of all species (regardless of sample) resolved by either morphology or by one or more of the three zooplankton markers. Eight species were resolved in common by all of the markers and morphology: <span class="html-italic">Acartia longiremis, Calanus marshallae, C. pacificus</span>, <span class="html-italic">Metridia pacifica, Glebocarcinus oregonensis, Metacarcinus gracilis</span>, <span class="html-italic">Lophopanopeus bellus</span>, and <span class="html-italic">Euphausia pacifica</span>; and for A, <span class="html-italic">Oregonia gracilis</span> and <span class="html-italic">Thysanoessa raschii</span> additionally were resolved by both LrCOI and Mol16S and morphology (yielding 10 species in common).</p>
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<p>Copepoda species identified using morphology and/or metabarcoding. Markers: LrCOI, Cop16S, and Mol16S. Filled circles denote presence, empty circles indicate that the taxon was not detected, and missing circles show overall lack of resolution for that method.</p>
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<p>Bar charts illustrating species/taxa identified with morphological analysis (carbon biomass) and/or metabarcoding markers (LrCOI, Cop16S, Mol16S, MiFish12S, FishCytb). % of total sequence reads for (<b>A</b>). (top) black = % identified taxa/species, grey = % unassigned identity, (<b>B</b>). Eukaryota, (<b>C</b>). Copepoda Crustacea, (<b>D</b>). Malacostraca Crustacea (i.e., Amphipoda, Decapoda, Euphausiacea, Mysida, Isopoda), (<b>E</b>). Actinopterygii fishes, (<b>F</b>). For Fishes: black = % identified non-fish taxa/species, red = % assigned to Actinopterygii fish species/taxon, grey = % unassigned identity. Sampling sites lettered (across bottom, (sites A–G)). Vertical lines separate spring (left) and autumn (right) samples. Oblique tows (at sites C and E) are underlined (remainder are vertical tows). Note that taxonomic composition, as represented by sequence reads, can be biased relative to the true proportion of organisms in the community.</p>
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<p>Proportion of copepod species carbon biomass versus proportion of copepod sequence reads (ASVs for merged per species) for metabarcoding markers (<b>A</b>). LrCOI and (<b>B</b>). Cop16S. Regression fit <span class="html-italic">R</span><sup>2</sup>, with adjusted <span class="html-italic">p</span>-values for multiple comparisons (per [<a href="#B93-dna-04-00001" class="html-bibr">93</a>]) and Pearson correlation (<span class="html-italic">r</span>) coefficients are indicated.</p>
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24 pages, 20847 KiB  
Article
Modeling Extreme Water Levels in the Salish Sea: The Importance of Including Remote Sea Level Anomalies for Application in Hydrodynamic Simulations
by Eric E. Grossman, Babak Tehranirad, Cornelis M. Nederhoff, Sean C. Crosby, Andrew W. Stevens, Nathan R. Van Arendonk, Daniel J. Nowacki, Li H. Erikson and Patrick L. Barnard
Water 2023, 15(23), 4167; https://doi.org/10.3390/w15234167 - 1 Dec 2023
Cited by 1 | Viewed by 2573
Abstract
Extreme water-level recurrence estimates for a complex estuary using a high-resolution 2D model and a new method for estimating remotely generated sea level anomalies (SLAs) at the model boundary have been developed. The hydrodynamic model accurately resolves the dominant physical processes contributing to [...] Read more.
Extreme water-level recurrence estimates for a complex estuary using a high-resolution 2D model and a new method for estimating remotely generated sea level anomalies (SLAs) at the model boundary have been developed. The hydrodynamic model accurately resolves the dominant physical processes contributing to extreme water levels across the Washington State waters of the Salish Sea, including the relative contribution of remote SLA and other non-tidal residual processes that drive extreme water levels above the predicted tide. The model’s predictions have errors of less than 15 cm (<5% of 3–4 m tidal range) at eight tide gauge locations across the model domain. The influence of remote SLAs at the seaward boundary of the model was implemented using a multivariate regression of readily available and locally relevant wind, sea surface temperature, and pressure anomaly data, combined with El Niño Index data (R2 = 0.76). The hydrodynamic model simulations using the remote SLA predictor compared well with simulations using the widely used data-assimilative global ocean model HYCOM SLA data (root mean square difference of 5.5 cm). Extreme water-level recurrence estimates with and without remote SLA show that remote forcing accounts for 50–60% of the total water level anomaly observed along Salish Sea shorelines. The resulting model simulations across decadal timescales provide estimates of extreme water level recurrence across the Salish Sea, capturing climate variability important to long-term coastal hazard planning. This approach has widespread applications for other complex estuarine systems. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1
<p>(<b>a</b>) Computational domain and bathymetry of the Salish Sea hydrodynamic model. (<b>b</b>) Locations of National Oceanic and Atmospheric Administration (NOAA, red triangles) and U.S. Geological Survey (USGS, green triangles) water level measurement stations.</p>
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<p>Timeline of available water level data at the U.S. Geological Survey (<b>a</b>) National Oceanic and Atmospheric Administration (<b>b</b>) water level stations. Locations of measurement sites shown in <a href="#water-15-04167-f001" class="html-fig">Figure 1</a>B.</p>
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<p>Modeled and measured water levels: quantile–quantile (Red) and scatter (Blue) plots for the Salish Sea hydrodynamic model validation period (2017–2020 water years) in meters relative to NAVD88. The root mean squared error (RMSE), mean absolute error (MAE), mean averaged error (bias), correlation of determination (R<sup>2</sup>), and scatter index (sci) values for each measurement station are listed.</p>
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<p>Maps of modeled M2 tidal amplitude in meters (<b>A</b>), M2 tidal phase in hours (<b>B</b>), and mean water level in meters (NAVD88) (<b>C</b>) during the 2017–2020 water years. Measured values are shown with circles. Plot of amplitude (<b>D</b>), tidal phase (<b>E</b>) of M2, K1, O1, S2, and N2 tidal constituents, and mean water level (black line in (<b>F</b>)) along the profile (white line shown in (<b>C</b>)) relative to the observed mean levels at tide gages Neah Bay (NB), Port Angeles (PA), Port Townsend (PT), Seattle (SE), and Tacoma (TA); crosses denote modeled mean water levels at gage sites as opposed to along the offshore transect; errors are similar to or smaller than the circles.</p>
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<p>Unbiased root mean squared error (uRMSE) and bias values for the modeled water levels (WL) (red circles) and water levels above the 50th (blue circles), 90th (green circles), 95th (orange circles), and 99th (yellow circles) measured percentiles during the 2017–2020 water years. Brown triangles and pink diamonds show error values for the tide and non-tidal residual (NTR). The polar distance from the center shows the RMSE values at each station (uRMSE<sup>2</sup> + Bias<sup>2</sup> = RMSE).</p>
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<p>(<b>A</b>) Scatter plot of the SLA predictor compared to the HYCOM SSH data at the offshore model boundary (warmer colors represent higher frequency of occurrence). (<b>B</b>) SLA predictor RMSE with respect to HYCOM SSH data for water years 1995–2010. (<b>C</b>) Time series of SLA predictor (blue) and HYCOM SSH (red) for the 2001 water year.</p>
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<p>Unbiased root mean squared error (uRMSE) and bias for the modeled total water level (circles), tide (squares), non-tidal residuals (NTR, diamonds), and water levels above 99th percentile (triangles) using HYCOM SSH (solid blue) and SLA predictor (hollow red) as offshore boundary conditions. The polar distance from the center shows the RMSE values at each station (uRMSE<sup>2</sup> + Bias<sup>2</sup> = RMSE).</p>
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<p>RMSE of modeled water levels above MSL for water years 1985–2015 at the NOAA measurement stations (<a href="#water-15-04167-f001" class="html-fig">Figure 1</a>). The color bar values are in cm, and empty squares indicate unavailable measured data.</p>
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<p>Extreme value analysis for Seattle (NOAA station ID, 9447130) based on the POT (blue) and AM/GEV method (red) for the modeled and measured water levels. Circles are point estimates used to fit the representative distribution, and the blue shading shows the 95 percent confidence interval for the POT method. The AM/GEV results of modeled water levels are shown with the green dotted line.</p>
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<p>Modeled 2-year (50%) and 50-year (2%) water levels across the Salish Sea in NAVD88. Circles depict estimates based on measured values.</p>
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<p>2-year (50%) and 50-year (2%) non-tidal residuals (NTR) in the Salish Sea based on the extreme value analysis (EVA) using the hindcast results for the 1985–2015 water years. Circles depict estimates based on measured values.</p>
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<p>Underestimation of 2-year (<b>A</b>,<b>C</b>,<b>E</b>) and 50-year (<b>B</b>,<b>D</b>,<b>F</b>) NTRs if sea level anomalies (<b>A</b>,<b>B</b>), atmospheric pressure (<b>C</b>,<b>D</b>), and wind (<b>E</b>,<b>F</b>) effects are neglected. Values are in cm.</p>
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<p>Correlation coefficient, rho<sup>2</sup>, in color for each stream site combination on the x- and y-axes.</p>
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<p>Predicted versus observed discharges for diverse streams in the region with point density in color (see colorbar) and coefficient of determination, R<sup>2</sup>, at the upper left.</p>
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<p>Percent differences between 12 km (GFDL) and 2.5 km (HRDPS) wind speeds for the mean (<b>A</b>), 75th (<b>B</b>), 90th (<b>C</b>), and 99th (<b>D</b>) percentiles (left to right). Green stars show similar percentiles estimated based on available meteorological observations.</p>
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17 pages, 2314 KiB  
Article
Harbor Porpoise Aggregations in the Salish Sea
by Dave Anderson, Laurie Shuster, Cindy R. Elliser, Katrina MacIver, Erin Johns Gless, Johannes Krieger and Anna Hall
Oceans 2023, 4(3), 269-285; https://doi.org/10.3390/oceans4030019 - 8 Aug 2023
Viewed by 4080
Abstract
Harbor porpoises are typically seen in small groups of 1–3 individuals, with aggregations of 20+ individuals treated as rare events. Since the 1990s, the harbor porpoise population in the Salish Sea has seen a significant recovery, and an increased number of observed aggregations [...] Read more.
Harbor porpoises are typically seen in small groups of 1–3 individuals, with aggregations of 20+ individuals treated as rare events. Since the 1990s, the harbor porpoise population in the Salish Sea has seen a significant recovery, and an increased number of observed aggregations that exceed the more usual small group sizes has been observed in recent years. By combining the observational data of United States and Canadian research organizations, community scientists, and whale watch captains or naturalists, we demonstrate that harbor porpoise aggregations appear to be more common than previously known, with 160 aggregations documented in 2022 alone. Behavioral data also indicate that foraging behaviors are common and social behaviors, like mating, are seen more often during these encounters compared to small groups. Other behaviors that are considered to be rare or unknown were also observed during these encounters, including cooperative foraging and vessel approach. These aggregations are likely important foraging and social gatherings for harbor porpoises. This holistic approach integrating data from two countries and multiple sources provides a population level assessment that more effectively reflects the behavior of harbor porpoises in this region, which do not recognize the socio-political boundaries imposed upon the natural world. Full article
(This article belongs to the Special Issue Marine Mammals in a Changing World, 2nd Edition)
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<p>Harbor porpoise group within an aggregation of an estimated 300+ individuals on 24 February 2021. Photo credit Trevor Derie, Pacific Mammal Research.</p>
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<p>Map of the Salish Sea, including all sighting reports.</p>
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<p>Behavior typical of a mating practice or attempt. Photographed during an aggregation of approximately 20 individuals on 31 August 2018. Photo credit: Laurie Shuster and David Anderson, Cascadia Research Collective, taken under National Marine Fisheries Service (NMFS) permit number 20605.</p>
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<p>Activity typical of a chase. Photographed during aggregation of over 100 individuals on 21 January 2019. Photo credit: Laurie Shuster and David Anderson, Cascadia Research Collective, taken under National Marine Fisheries Service (NMFS) permit number 20605.</p>
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<p>Splash during aggregation with four porpoises in foreground. Photographed during aggregation of approximately 200 individuals on 2 June 2022. Photo credit: Anna Hall, Sea View Marine Sciences.</p>
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18 pages, 1890 KiB  
Article
Surveillance for Antibiotic-Resistant E. coli in the Salish Sea Ecosystem
by Alexandria Vingino, Marilyn C. Roberts, Michelle Wainstein, James West, Stephanie A. Norman, Dyanna Lambourn, Jeffery Lahti, Ryan Ruiz, Marisa D’Angeli, Scott J. Weissman and Peter Rabinowitz
Antibiotics 2021, 10(10), 1201; https://doi.org/10.3390/antibiotics10101201 - 2 Oct 2021
Cited by 12 | Viewed by 3428
Abstract
E. coli was isolated from the Salish Sea (Puget Sound) ecosystem, including samples of marine and fresh water, and wildlife dependent on this environment. E. coli isolates were assessed for phenotypic and genotypic resistance to antibiotics. A total of 305 E. coli isolates [...] Read more.
E. coli was isolated from the Salish Sea (Puget Sound) ecosystem, including samples of marine and fresh water, and wildlife dependent on this environment. E. coli isolates were assessed for phenotypic and genotypic resistance to antibiotics. A total of 305 E. coli isolates was characterized from samples collected from: marine water obtained in four quadrants of the Salish Sea; select locations near beaches; fresh water from streams near marine beaches; and fecal samples from harbor porpoises (Phocoena phocoena), harbor seals (Phoca vitulina), river otters (Lontra canadensis), and English sole (Parophrys vetulus). Isolates were evaluated using antimicrobial susceptibility typing, whole-genome sequencing, fumC, and multilocus sequence typing. Resistance and virulence genes were identified from sequence data. Of the 305 isolates from Salish Sea samples, 20 (6.6%) of the E. coli were intermediate, and 31 (10.2%) were resistant to ≥1 class of antibiotics, with 26.9% of nonsusceptible (resistant and intermediate resistant) E. coli isolates from marine mammals and 70% from river otters. The proportion of nonsusceptible isolates from animals was significantly higher than samples taken from marine water (p < 0.0001). A total of 196 unique STs was identified including 37 extraintestinal pathogenic E. coli (ExPEC)-associated STs [ST10, ST38, ST58, ST69, ST73, ST117, ST131, and ST405]. The study suggests that animals may be potential sentinels for antibiotic-resistant and ExPEC E. coli in the Salish Sea ecosystem. Full article
(This article belongs to the Special Issue 10th Anniversary of Antibiotics—Feature Papers)
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<p>Maps of (<b>A</b>): all <span class="html-italic">E. coli</span> isolates by sample source; (<b>B</b>): resistant and intermediate <span class="html-italic">E. coli</span> isolates by sample source; (<b>C</b>): ExPEC STs of <span class="html-italic">E. coli</span> by location; (<b>D</b>): river otter <span class="html-italic">E. coli</span> isolate results.</p>
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<p>ST10 and ST73 SNP matrices and phylogenetic trees.</p>
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<p>ST10 and ST73 SNP matrices and phylogenetic trees.</p>
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19 pages, 1679 KiB  
Article
Antibiotic Resistance of Bacteria in Two Marine Mammal Species, Harbor Seals and Harbor Porpoises, Living in an Urban Marine Ecosystem, the Salish Sea, Washington State, USA
by Stephanie A. Norman, Dyanna M. Lambourn, Jessica L. Huggins, Joseph K. Gaydos, Sandra Dubpernell, Susan Berta, Jennifer K. Olson, Victoria Souze, Alysha Evans, Betsy Carlson, Mandi Johnson, Rachel Mayer, Cathy King and Alyssa Scott
Oceans 2021, 2(1), 86-104; https://doi.org/10.3390/oceans2010006 - 25 Jan 2021
Cited by 11 | Viewed by 7756
Abstract
The pervasive use of antibiotics in human medicine, veterinary medicine, and agriculture can result in a significant increase in the spread and environmental persistence of antibiotic resistance in marine ecosystems. This study describes the presence and distribution of antibiotic-resistant bacteria in Salish Sea [...] Read more.
The pervasive use of antibiotics in human medicine, veterinary medicine, and agriculture can result in a significant increase in the spread and environmental persistence of antibiotic resistance in marine ecosystems. This study describes the presence and distribution of antibiotic-resistant bacteria in Salish Sea harbor seals (Phoca vitulina) and harbor porpoises (Phocoena phocoena) and evaluates species, age class, and geographic differences in resistance patterns. Isolates from 95 dead-stranded animals (74 seals/21 porpoises) were tested for resistance to a suite of 15 antibiotics. Of the 95 sampled, 85 (89%) (67 seals/18 porpoises) successfully yielded 144 isolates, with 37% resistant to at least one antibiotic and 26% multi-drug resistant (24% and 39% of seal and porpoise isolates, respectively). Overall, and by study region, porpoises were significantly more likely to harbor resistant organisms compared to seals. Significant differences between age classes were noted for the antibiotics amoxicillin, cephalexin, and cefovecin. Overall isolate resistance was significantly greater in porpoises than seals for several individual antibiotics. Multiple antibiotic resistance (MAR) indices greater than 0.2 were observed in 55% of multi-drug resistant isolates, suggesting seal and porpoise exposure to anthropogenic pollution. The relatively high and disparate prevalence of antibiotic resistance in these common, but ecologically dissimilar, marine mammals reflects a potentially large environmental pool of antibiotic resistant organisms in the Salish Sea or inherently different resistance gene patterns between the two species. Full article
(This article belongs to the Special Issue Marine Mammals in a Changing World)
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<p>Distribution of stranded harbor seals (orange dots) and harbor porpoises (purple dots) sampled for antibiotic resistant organisms in the Salish Sea, Washington State, USA. Yellow horizontal line delineates northern and southern portions of the study area.</p>
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<p>Percentage of bacterial isolates from dead stranded harbor seals and porpoises with antibiotic resistance based on the number of antibiotics to which the isolate was resistant.</p>
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<p>Percentage of bacterial isolates from harbor seals (black) and harbor porpoises (white) displaying resistance to individual antibiotics, grouped by class.</p>
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<p>Proportion of bacterial isolates from harbor seals and porpoises with multipleantibiotic resistance originating from individual tissue sources. Proportions are categorized based on their Multiple Antibiotic Resistance Index (MAR): MAR = 0 (no resistance), MAR = 0 ≤ 0.2, or MAR &gt; 0.2. Numbers in parentheses represent number of bacterial isolates from each tissue source.</p>
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23 pages, 13514 KiB  
Article
Detailed Hydrodynamic Feasibility Assessment for Leque Island and Zis a Ba Restoration Projects
by Adi Nugraha and Tarang Khangaonkar
J. Mar. Sci. Eng. 2018, 6(4), 140; https://doi.org/10.3390/jmse6040140 - 16 Nov 2018
Cited by 1 | Viewed by 3087
Abstract
Numerous restoration projects are underway in Puget Sound, Washington, USA with the goal of re-establishing intertidal wetlands that were historically lost due to dike construction for flood protection and agricultural development. One such effort is the restoration effort within the Stillaguamish Delta, benefitting [...] Read more.
Numerous restoration projects are underway in Puget Sound, Washington, USA with the goal of re-establishing intertidal wetlands that were historically lost due to dike construction for flood protection and agricultural development. One such effort is the restoration effort within the Stillaguamish Delta, benefitting from the cumulative effects from the Leque Island and zis a ba restoration projects. The preferred restoration design calls for the removal of perimeter dikes at the two sites and the creation of tidal channels to facilitate the drainage of tidal flows. A 3-D high-resolution unstructured-grid coastal ocean model based on FVCOM was developed to evaluate the hydrodynamic response of the estuary to restoration alternatives. A series of hydrodynamic modeling simulations were then performed to quantify the hydrodynamic response of the nearshore restoration project, such as periodic inundation, suitable currents, and desired habitat/salinity levels. Sediment impacts were also examined, including the potential for excessive erosion or sedimentation requiring maintenance. Simulation results indicate that the preferred alternative scenario provides the desired estuarine response, which is consistent with the planned design. A decrease in velocities and bed shear in the main river channels was noted for the restored condition associated with the increased inundation of tidal flat area and reduced tidal flows through the main channels. High bed shear near the restored tidal channel entrances indicates that the inlets may evolve in size until equilibrium is established. Full article
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<p>(<b>a</b>) Location of the Leque Island and zis a ba sites in Whidbey Basin. (<b>b</b>) Schematic representation of (<b>top</b>) the baseline scenario and (<b>bottom</b>) the preferred restoration alternative scenario.</p>
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<p>(<b>a</b>) Baseline model grid of the Skagit and Stillaguamish River estuaries including Skagit Bay, Port Susan Bay, and the Leque Island and zis a ba. The inset shows detail the Leque Island and zis a ba project sites. (<b>b</b>) Oceanographic observation stations at Port Susan Bay, October 2005.</p>
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<p>Comparison of predicted and measured (<b>a</b>) water surface elevations and (<b>b</b>) salinity at the Kayak Point, Hatt Slough, and South Pass stations, respectively.</p>
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<p>Comparison of predicted and measured velocity components at the Hatt Slough (<b>a</b>) and South Pass (<b>b</b>) stations, respectively. Data is from mid-depth of the water column.</p>
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<p>Preferred alternative grid for the Leque Island site.</p>
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<p>Preferred alternative design (<b>a</b>) and grid (<b>b</b>) for the zis a ba site.</p>
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<p>Eleven station locations on the Leque Island and the zis a ba sites.</p>
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<p>(<b>a</b>) Baseline scenario grid overlaying the bed elevation map. (<b>b</b>) Preferred restoration alternative scenario grid overlaying the bed elevation map.</p>
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<p>Horizontal distribution of salinity at low tide (07:00: <b>a</b>) and high tide (13:00: <b>b</b>) for the baseline scenario on 24 October 2005.</p>
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<p>Horizontal distribution of maximum (<b>a</b>) and mean bed shear stress in Pa (<b>b</b>) for the baseline scenario for the high-flow (bank-full) condition at 750.62 m<sup>3</sup>/s.</p>
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<p>Preferred vs. baseline scenarios: time series of Salinity at Station P1, P7, and P10 for typical estuarine conditions (October 2005).</p>
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<p>Horizontal distribution of salinity at low tide (07:00: <b>a</b>) and high tide (13:00: <b>b</b>) for the preferred restoration alternative scenario on 24 October 2005.</p>
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<p>Salinity difference contours of the preferred restoration alternative scenario relative to the baseline at low tide (07:00: <b>a</b>) and high tide (13:00: <b>b</b>) on 24 October 2005.</p>
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<p>Preferred vs. baseline scenarios: time series of bed shear stress at stations P1, P7, and P10 for typical estuarine conditions (October 2005). Critical bed shear for erosion of sand (0.11 Pa) and gravel (1.26 Pa) are marked as green and pink lines.</p>
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<p>Preferred vs. baseline scenarios: time series of velocity magnitude at Stations P1, P7, and P10 for bank-full conditions at 750.62 m<sup>3</sup>/s river flow (tides and wind corresponding to October 2005).</p>
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<p>Preferred vs. baseline scenarios: time series of bed shear stress at Stations P1, P7, and P10 for bank-full conditions at 750.62 m<sup>3</sup>/s river flow (tides and wind corresponding to October 2005). Critical bed shear for the erosion of sand (0.11 Pa) and gravel (1.26 Pa) is marked as green and pink lines.</p>
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<p>Horizontal distribution of maximum (<b>a</b>) and mean bed shear stress (<b>b</b>) for the preferred restoration alternative scenario for high-flow (bank-full) conditions at 750.62 m<sup>3</sup>/s.</p>
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<p>Mean difference bed shear stress contours of the preferred restoration alternative scenario relative to the baseline scenario at the high-flow (bank-full) condition at 750.62 m<sup>3</sup>/s.</p>
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22 pages, 10552 KiB  
Article
Hydrodynamic Zone of Influence Due to a Floating Structure in a Fjordal Estuary—Hood Canal Bridge Impact Assessment
by Tarang Khangaonkar, Adi Nugraha and Taiping Wang
J. Mar. Sci. Eng. 2018, 6(4), 119; https://doi.org/10.3390/jmse6040119 - 15 Oct 2018
Cited by 3 | Viewed by 4110
Abstract
Floating structures such as barges and ships affect near-field hydrodynamics and create a zone of influence (ZOI). Extent of the ZOI is of particular interest due to potential obstruction to and impact on out-migrating juvenile fish. Here, we present an assessment of ZOI [...] Read more.
Floating structures such as barges and ships affect near-field hydrodynamics and create a zone of influence (ZOI). Extent of the ZOI is of particular interest due to potential obstruction to and impact on out-migrating juvenile fish. Here, we present an assessment of ZOI from Hood Canal (Floating) Bridge, located within the 110-km-long fjord-like Hood Canal sub-basin in the Salish Sea, Washington. A field data collection program allowed near-field validation of a three-dimensional hydrodynamic model of Hood Canal with the floating bridge section embedded. The results confirm that Hood Canal Bridge, with a draft of 4.6 m covering ~85% of the width of Hood Canal, obstructs the brackish outflow surface layer. This induces increased local mixing near the bridge, causes pooling of water (up-current) during ebb and flood, and results in shadow/sheltering of water (down-current). The change in ambient currents, salinity, and temperature is highest at the bridge location and reduces to background levels with distance from the bridge. The ZOI extends ~20 m below the surface and varies from 2–3 km for currents, from 2–4 km for salinity, and from 2–5 km for temperature before the deviations with the bridge drop to <10% relative to simulated background conditions without the bridge present. Full article
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<p>(<b>a</b>) Oceanographic regions of Salish Sea including Northwest Straits, Puget Sound, and the inner sub-basins—Hood Canal, Whidbey Basin, Central Basin, and South Sound; (<b>b</b>) bathymetric profile of Hood Canal study area.</p>
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<p>Hood Canal Bridge (HCB) study area showing the floating bridge and the 2017 oceanographic data collection program station locations relative to the bridge.</p>
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<p>(<b>a</b>) Salish Sea model domain and finite-volume grid; (<b>b</b>) monthly water-quality monitoring stations from Washington State Department of Ecology.</p>
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<p>HCB layout with pontoon cross-section and east span details. Detail A is a sectional view across the width of the bridge.</p>
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<p>Salish Sea model grid with refinement in the region to facilitate incorporation of the bridge block elements. The grid size is refined such that cell centers are separated by the bridge width distance of 18.3 m. The bridge draft is represented by two layers whose combined thickness is equal to 4.57 m.</p>
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<p>Time series of model result, NOAA harmonic (Xtide) prediction, and observed data for sea-water elevation. Time is shown as Julian days in 2017.</p>
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<p>Time series of model results and observed data for salinity (left panel) and temperature (right panel) from 31 May and 1 June 2017. Plots (<b>a</b>,<b>d</b>) are a comparison for the North acoustic Doppler current profiler (ADCP) station; plots (<b>b</b>,<b>e</b>) are for the Bridge ADCP station; and plots (<b>c</b>,<b>f</b>) are for the South ADCP station.</p>
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<p>Comparisons of predicted and observed salinities at peak ebb and peak flood north of HCB on 31 May 2017.</p>
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<p>Comparisons of predicted and observed salinities at peak ebb and peak flood south of HCB on 31 May 2017.</p>
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<p>Comparisons of predicted (blue) and observed (red) velocities for different depths at the Bridge ADCP station (example).</p>
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<p>Comparisons of predicted and observed velocities just below HCB.</p>
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<p>(<b>a</b>) Time series of depth-averaged velocity at bridge; (<b>b</b>–<b>g</b>) comparisons of predicted and observed average velocity profiles at the South, Bridge, and North ADCP stations during maximum flood and ebb tide periods. Gray thin lines represent daily velocity profiles during peak ebb and flood periods.</p>
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<p>Predicted horizontal velocity contour and vector plots for the baseline scenario with HCB present for (<b>a</b>) ebb and (<b>b</b>) flood currents in the surface layer. (Typical spring tide on 27 April 2017).</p>
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<p>Predicted horizontal velocity contour and vector plots for test 1 scenario without HCB present for (<b>a</b>) ebb and (<b>b</b>) flood currents in the surface layer. (Typical spring tide on 27 April 2017).</p>
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<p>Predicted horizontal velocity contour and vector plots for test 2 scenario with HCB present and middle span open for (<b>a</b>) ebb and (<b>b</b>) flood currents in the surface layer. (Typical spring tide on 27 April 2017).</p>
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<p>(<b>a</b>,<b>b</b>) Velocity difference of baseline scenario with HCB relative to test 1 without HCB during peak ebb and flood; (<b>c</b>,<b>d</b>) velocity difference of baseline scenario with HCB relative to test 2 with draw span open during ebb and flood. (Typical spring tide on 27 April 2017).</p>
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<p>Transects for assessment of zone of influence (ZOI) in a vertical plane: (<b>a</b>) short transect for near-field effects; (<b>b</b>) longer transect to examine the effects over full tidal excursion.</p>
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<p>(<b>a</b>,<b>b</b>) Velocity difference of baseline scenario with HCB relative to test 1 without HCB during peak ebb and flood; (<b>c</b>,<b>d</b>) velocity difference of baseline scenario with HCB relative to test 2 with draw span open during ebb and flood. (Typical spring tide on 27 April 2017).</p>
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<p>(<b>a</b>,<b>b</b>) Salinity difference of baseline scenario with HCB relative to test 1 without HCB during peak ebb and flood; (<b>c</b>,<b>d</b>) velocity difference of baseline scenario with HCB relative to test 2 with draw span open during ebb and flood. (Typical spring tide on 27 April 2017).</p>
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<p>(<b>a</b>,<b>b</b>) Temperature difference of baseline scenario with HCB relative to test 1 without HCB during peak ebb and flood; (<b>c</b>,<b>d</b>) velocity difference of baseline scenario with HCB relative to test 2 with draw span open during ebb and flood. (Typical spring tide on 27 April 2017).</p>
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<p>Difference in current magnitudes due to HCB relative to test 1, plotted along Transect-b for all model layers. The (<b>top</b>) panel presents peak ebb and the (<b>bottom</b>) panel presents peak flood results averaged over the calibration period of 25 April–11 June 2017. (The distance between −10 km and 0 km represents the region south of the bridge, while that between 0 km and 10 km represents the region north of the bridge).</p>
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<p>Difference in salinity due to HCB relative to test 1, plotted along Transect-b for all model layers. The plot shows average salinity difference for all hourly time steps over the calibration period of 25 April–11 June 2017. (The distance between −10 km and 0 km represents the region south of the bridge, while that between 0 km and 10 km represents the region north of the bridge).</p>
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<p>Difference in temperature due to HCB relative to test 1, plotted along Transect-b for all model layers. The plot shows average temperature difference for all hourly time steps over the calibration period of 25 April–11 June 2017. (The distance between −10 km and 0 km represents the region south of the bridge, while that between 0 km and 10 km represents the region north of the bridge).</p>
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39 pages, 25607 KiB  
Article
Simulation of the 2003 Foss Barge - Point Wells Oil Spill: A Comparison between BLOSOM and GNOME Oil Spill Models
by Rodrigo Duran, Lucy Romeo, Jonathan Whiting, Jason Vielma, Kelly Rose, Amoret Bunn and Jennifer Bauer
J. Mar. Sci. Eng. 2018, 6(3), 104; https://doi.org/10.3390/jmse6030104 - 11 Sep 2018
Cited by 26 | Viewed by 8504
Abstract
The Department of Energy’s (DOE’s) National Energy Technology Laboratory’s (NETL’s) Blowout and Spill Occurrence Model (BLOSOM), and the National Oceanic and Atmospheric Administration’s (NOAA’s) General NOAA Operational Modeling Environment (GNOME) are compared. Increasingly complex simulations are used to assess similarities and differences between [...] Read more.
The Department of Energy’s (DOE’s) National Energy Technology Laboratory’s (NETL’s) Blowout and Spill Occurrence Model (BLOSOM), and the National Oceanic and Atmospheric Administration’s (NOAA’s) General NOAA Operational Modeling Environment (GNOME) are compared. Increasingly complex simulations are used to assess similarities and differences between the two models’ components. The simulations presented here are forced by ocean currents from a Finite Volume Community Ocean Model (FVCOM) implementation that has excellent skill in representing tidal motion, and with observed wind data that compensates for a coarse vertical ocean model resolution. The comprehensive comparison between GNOME and BLOSOM presented here, should aid modelers in interpreting their results. Beyond many similarities, aspects where both models are distinct are highlighted. Some suggestions for improvement are included, e.g., the inclusion of temporal interpolation of the forcing fields (BLOSOM) or the inclusion of a deflection angle option when parameterizing wind-driven processes (GNOME). Overall, GNOME and BLOSOM perform similarly, and are found to be complementary oil spill models. This paper also sheds light on what drove the historical Point Wells spill, and serves the additional purpose of being a learning resource for those interested in oil spill modeling. The increasingly complex approach used for the comparison is also used, in parallel, to illustrate the approach an oil spill modeler would typically follow when trying to hindcast or forecast an oil spill, including detailed technical information on basic aspects, like choosing a computational time step. We discuss our successful hindcast of the 2003 Point Wells oil spill that, to our knowledge, had remained unexplained. The oil spill models’ solutions are compared to the historical Point Wells’ oil trajectory, in time and space, as determined from overflight information. Our hindcast broadly replicates the correct locations at the correct times, using accurate tide and wind forcing. While the choice of wind coefficient we use is unconventional, a simplified analytic model supported by observations, suggests that it is justified under this study’s circumstances. We highlight some of the key oceanographic findings as they may relate to other oil spills, and to the regional oceanography of the Salish Sea, including recommendations for future studies. Full article
(This article belongs to the Special Issue Marine Oil Spills 2018)
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<p>Approximate timeline of events, as recorded in ENTRIX 2005.</p>
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<p>Maps showing the study area in the Salish Sea and surrounding areas.</p>
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<p>Project area map showing the approximate location and path of the oil surface slick, based on data recorded by ENTRIX, Inc., [<a href="#B10-jmse-06-00104" class="html-bibr">10</a>]; see area of interest in <a href="#jmse-06-00104-f002" class="html-fig">Figure 2</a>.</p>
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<p>A schematic of the observed oil path (top left), and a time series of digitized maps from National Oceanic and Atmospheric Administration’s (NOAA’s) overflight observation records.</p>
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<p>Extent of coverage for the Salish Sea Model.</p>
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<p>Comparison of sea-surface height between the model and a local XTide Station.</p>
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<p>Hourly wind data from NOAA’s WPOW1 station starting midnight 30 December 2003. Wind direction follows the oceanographic convention, i.e., the direction is towards where the wind blows.</p>
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<p>Comparison of wind data from NOAA (NDBD-WPOW1, black vectors) and Kingston (blue vectors) wind stations. The locations of these two stations can be seen in <a href="#jmse-06-00104-f009" class="html-fig">Figure 9</a>.</p>
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<p>Trajectory (orange, red circles mark locations at hourly marks) initiated at the same time and location as the oil spill, resulting from forcing exclusively with 6% of the wind from the NOAA wind station. Also shown are the locations of NOAA (NDBC-WPOW1) and Kingston wind stations (white circles with black cross) and the approximate locations of oil at different times, as observed from overflights (white squares; see <a href="#jmse-06-00104-f004" class="html-fig">Figure 4</a>).</p>
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<p>Comparison of advection algorithms using constant ocean currents; test 1.</p>
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<p>Comparison using constant wind, trajectories diverge with the separation between them is plotted in the bottom left inset; test 2.</p>
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<p>GNOME and BLOSOM trajectories diverge due to including, or not, the effect of earth’s rotation on wind forcing; test 3, part 1.</p>
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<p>The same two plots shown in <a href="#jmse-06-00104-f012" class="html-fig">Figure 12</a> are shown along with an additional trajectory by GNOME, that now includes deflection due to earth’s rotation; rotation was included directly to the wind data, forcing GNOME to replicate the deflection computed internally by BLOSOM. GNOME’s trajectory with deflection agrees well with BLOSOM’s trajectory, however, some difference remains; test 3 part 2.</p>
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<p>The trajectories for GNOME and BLOSOM, both with deflection included, as seen in <a href="#jmse-06-00104-f013" class="html-fig">Figure 13</a>, are compared to the same BLOSOM trajectory, but now including temporal interpolation; test 4. The trajectory from GNOME with added deflection, and the trajectory from BLOSOM with added interpolation, now resemble each other closely.</p>
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<p>(<b>a</b>) Trajectories showing beaching differences, parameters for these simulations can be found in <a href="#jmse-06-00104-t008" class="html-table">Table 8</a>; test 5. (<b>b</b>) Distance between GNOME’s and BLOSOM’s trajectories as a function of time during the first two hours of the simulation for test 5, trajectories are plotted in (<b>a</b>). (<b>c</b>) GNOME’s trajectory after refloating at 18:53 is shown; test 5.</p>
Full article ">Figure 15 Cont.
<p>(<b>a</b>) Trajectories showing beaching differences, parameters for these simulations can be found in <a href="#jmse-06-00104-t008" class="html-table">Table 8</a>; test 5. (<b>b</b>) Distance between GNOME’s and BLOSOM’s trajectories as a function of time during the first two hours of the simulation for test 5, trajectories are plotted in (<b>a</b>). (<b>c</b>) GNOME’s trajectory after refloating at 18:53 is shown; test 5.</p>
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<p>(<b>A</b>) Trajectories cross the channel without wind, as they are entrained by eddy-induced cross-channel transport when initiated offshore from the location of the oil spill; test 6. (<b>B</b>) Zoomed in view of the simulations that were initiated at the correct oil spill location.</p>
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<p>Simulation including diffusion with thirty particles; test 7.</p>
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<p>Simulation including diffusion with a thousand particles; test 7.</p>
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<p>Thirty-particle simulation with diffusion, released over a 15 min period; test 8.</p>
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<p>Comparison of diffusion coefficients; test 9.</p>
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<p>Diagram summarizing steps, as used in this study, for hindcasting an oil spill.</p>
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<p>GNOME and BLOSOM simulations using a 1 min time step. This simulation includes 6% wind, ocean currents and some diffusion.</p>
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<p>Same as plot A1 but both models using a 6 min time step.</p>
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<p>Same as <a href="#jmse-06-00104-f0A1" class="html-fig">Figure A1</a>, with both models using a computational time step of 18 min.</p>
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<p>Same as <a href="#jmse-06-00104-f0A1" class="html-fig">Figure A1</a> but with a 36 min computational time step for BLOSOM.</p>
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<p>(<b>Left Panel</b>) The wind-driven velocity computed from a slip-velocity approach as specified in Csanady [<a href="#B42-jmse-06-00104" class="html-bibr">42</a>] (pp. 22–24) (blue arrows) as a function of depth (meters, vertical axis) when forced with a constant wind (u, v) = (5, 5) meters/second. Assuming a 6m deep surface cell, an approximation to the FVCOM’s sea-surface velocity (in red) is computed by averaging the velocity vectors in blue. (<b>Right Panel</b>) Hodograph for the ocean current velocity solution (meters/second) that is plotted in the left panel. Results remain qualitatively similar when using other wind speeds that are likewise comparable to the wind speeds observed during the Point Wells 2003 spill.</p>
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21 pages, 8258 KiB  
Article
Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region
by Andrea Hilborn and Maycira Costa
Remote Sens. 2018, 10(9), 1449; https://doi.org/10.3390/rs10091449 - 11 Sep 2018
Cited by 51 | Viewed by 7910
Abstract
A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, [...] Read more.
A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, the spatial and temporal coverage of the input image dataset can heavily impact the outcomes of using this method and, thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, we used a three-year time series of MODIS-Aqua chlorophyll-a to evaluate the DINEOF reconstruction output accuracy corresponding to variation in the form of the input data used (i.e., daily or week composite scenes) and time series length (annual or three consecutive years) for a dynamic region, the Salish Sea, Canada. The accuracy of the output data was assessed considering the original chla pixels. Daily input time series produced higher accuracy reconstructing chla (95.08–97.08% explained variance, RMSExval 1.49–1.65 mg m−3) than did all week composite counterparts (68.99–76.88% explained variance, RMSExval 1.87–2.07 mg m−3), with longer time series producing better relationships to original chla pixel concentrations. Daily images were assessed relative to extracted in situ chla measurements, with original satellite chla achieving a better relationship to in situ matchups than DINEOF gap-filled chla, and with annual DINEOF-processed data performing better than the multiyear. These results contribute to the ongoing body of work encouraging production of ocean color datasets with consistent processing for global purposes such as climate change studies. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Graphical abstract

Graphical abstract
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<p>The Salish Sea, oceanic and geographic features, and population centers. The region includes the Juan de Fuca Strait (JFS), Strait of Georgia (SoG), Puget Sound (PS), and Queen Charlotte Strait (QCS). QCS is included in this study considering its use in salmon migration research [<a href="#B26-remotesensing-10-01449" class="html-bibr">26</a>]. Locations of in situ <span class="html-italic">chla</span> matchups (<a href="#sec2dot4dot2-remotesensing-10-01449" class="html-sec">Section 2.4.2</a>) are indicated by blue (DINEOF-reconstructed <span class="html-italic">chla</span>) and blue-ringed circles (satellite and DINEOF-reconstructed <span class="html-italic">chla</span>).</p>
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<p>Temporal coverage displayed as (<b>a</b>) number of images per month and (<b>b</b>) percent spatial coverage of the study region per month. Presence of a given pixel is shown for (<b>c</b>) the daily time series and (<b>d</b>) week composite.</p>
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<p>D1 (<b>a</b>), W1 (<b>b</b>), D3 (<b>c</b>), and W3 (<b>d</b>) linear correlation results. The 40.00 mg m<sup>−3</sup> threshold (<a href="#sec2dot2dot1-remotesensing-10-01449" class="html-sec">Section 2.2.1</a>) is evident as a cutoff feature in all plots.</p>
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<p>Per-pixel R<sup>2</sup> of DINEOF results for D1 (<b>a</b>), W1 (<b>b</b>), D3 (<b>c</b>), and W3 (<b>d</b>).</p>
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<p>Daily reconstruction of February 28, 2014, shown as the original <span class="html-italic">chla<sub>sat</sub></span> (<b>a</b>), D1 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>b</b>), and D3 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>c</b>); similarly, the week composite <span class="html-italic">chla<sub>sat</sub></span> (<b>d</b>), W1 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>e</b>), and W3 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>f</b>). Salish Sea thalweg is shown in (<b>a</b>), with a gap excluding the region of no data in Johnstone Strait.</p>
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<p>Daily image time series shown as Hovmöller plot along Salish Sea thalweg (<span class="html-italic">y</span> axis, shown in <a href="#remotesensing-10-01449-f005" class="html-fig">Figure 5</a>a), contrasting <span class="html-italic">chla<sub>sat</sub></span> (<b>a</b>), D1 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>b</b>), and D3 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>c</b>) for 2014–2016. The dashed line represents a spatial gap in Johnstone Strait due to the inability of MODISA to resolve data in the narrow passages.</p>
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<p>Week composite time series extracted along the Salish Sea thalweg (<span class="html-italic">y</span> axis, <a href="#remotesensing-10-01449-f005" class="html-fig">Figure 5</a>a) for <span class="html-italic">chla<sub>sat</sub></span> (<b>a</b>)<span class="html-italic">,</span> W1 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>b</b>), and W3 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>c</b>) for 2014–2016.</p>
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<p>Statistical results for <span class="html-italic">chla<sub>insitu</sub></span> between <span class="html-italic">chla<sub>sat</sub></span> (<b>a</b>), D1 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>b</b>), and D3 <span class="html-italic">chla<sub>sat+rec</sub></span> (<b>c</b>). All <span class="html-italic">p</span>-values are &lt;0.05.</p>
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<p>Relationship between original and reconstructed pixel time series for a D3 example pixel located in the Fraser River plume (<b>a</b>) and in central JFS (<b>b</b>), and for the W3 reconstruction in (<b>c</b>,<b>d</b>), respectively.</p>
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<p>Spatial median and shaded ±1 standard deviation for <span class="html-italic">chla<sub>sat+rec</sub></span> of D1/D3 (<b>a</b>), divided by year for legibility, and 2014–2016 for W1/W3 (<b>b</b>). Corresponding per-scene median <span class="html-italic">chla<sub>sat</sub></span> shown as black dots with ±1 standard deviation.</p>
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21726 KiB  
Article
Characteristics and Dynamics of a Large Sub-Tidal Sand Wave Field—Habitat for Pacific Sand Lance (Ammodytes personatus), Salish Sea, Washington, USA
by H. Gary Greene, David A. Cacchione and Monty A. Hampton
Geosciences 2017, 7(4), 107; https://doi.org/10.3390/geosciences7040107 - 23 Oct 2017
Cited by 13 | Viewed by 22119
Abstract
Deep-water sand wave fields in the San Juan Archipelago of the Salish Sea and Pacific Northwest Washington, USA, have been found to harbor Pacific sand lance (PSL, Ammodytes personatus), a critical forage fish of the region. Little is known of the dynamics [...] Read more.
Deep-water sand wave fields in the San Juan Archipelago of the Salish Sea and Pacific Northwest Washington, USA, have been found to harbor Pacific sand lance (PSL, Ammodytes personatus), a critical forage fish of the region. Little is known of the dynamics of these sand waves and the stability of the PSL sub-tidal habitats. Therefore, we have undertaken an initial investigation to determine the dynamic conditions of a well-known PSL habitat in the San Juan Channel within the Archipelago using bottom sediment sampling, an acoustical doppler current profiling (ADCP) system, and multi-beam echo sounder (MBES) bathymetry. Our study indicates that the San Juan Channel sand wave field maintained its shape and bedforms geometry throughout the years it has been studied. Based on bed phase diagrams for channelized bedforms, the sand waves appear to be in a dynamic equilibrium condition. Sea level rise may change the current regime within the Archipelago and may alter some of the deep-water or sub-tidal PSL habitats mapped there. Our findings have global significance in that these dynamic bedforms that harbor PSL and sand-eels elsewhere along the west coast of North America and in the North Sea may also be in a marginally dynamic equilibrium condition and may be prone to alteration by sea level rise, indicating an urgency in locating and investigating these habitats in order to sustain the forage fish. Full article
(This article belongs to the Special Issue Marine Geomorphometry)
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Figure 1

Figure 1
<p>Multibeam echosounder bathymetric map showing the location of the San Juan Channel sediment wave field within the San Juan Archipelago of the Pacific NW, Washington State, USA. The bedform investigated for this study is prominently displayed near the top of the bathymetric image.</p>
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<p>Expanded view of multi-beam echo sounder (MBES) bathymetric image of the San Juan Channel sediment wave field exhibited in <a href="#geosciences-07-00107-f001" class="html-fig">Figure 1</a> with depth illustrated in color. Small dots within circles represent sediment sample locations (diameter of dot equals ~23 m), station numbers are located next to open circles.</p>
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<p>Locations of eyeball photo shots (purple dots) taken along transects of the San Juan Channel sand wave field using the USGS eyeball camera and sediment sample stations showing mean grain sizes (phi scale) in various colors. The objective of this figure is to show the density of sampling points within the sand wave field compared to samples taken outside of the field.</p>
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<p>Examples of images collected using the USGS eyeball camera: (<b>a</b>) MBES bathymetric image with locations of samples collected in the sand wave field; (<b>b</b>) photo of sediment taken in trough; (<b>c</b>) photo of sediment taken near crest of a sand wave.</p>
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<p>Tidal curves showing tidal heights in meters above Mean Low Low Water (MLLW) within the San Juan Channel for the time of the ADCP survey on 17 July 2012. Data from NOAA tide gauge at Friday Harbor, WA (NOAA, 6–7 October 2010).</p>
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<p>Surface, bottom, and depth-averaged currents observed during ADCP transect B aboard RV <span class="html-italic">Centennial</span> at flood tide, 17 July 2012.</p>
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<p>MBES image of the San Juan Channel sand wave field that shows two different sizes of sediment waves from long high amplitude to short low-amplitude waves. Red solid arrows show where ripples and small amplitude sediment waves exist along the edges of the field, dashed red lines show orientation of large sand waves, yellow line with arrows shows video camera transect made to validate sediment type and morphology, and green lines are scale marks used to show wave lengths.</p>
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<p>Sediment samples [<a href="#B22-geosciences-07-00107" class="html-bibr">22</a>] collected and analyzed for this study; (on left, MBES image) location of samples; color dots represent sample sites, and (right) graph of grain sizes from sieve analyses showing grain size distribution.</p>
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<p>Numbers of fish captured in sediment samples from the San Juan Channel sand wave field. The highest numbers of fish (from 30 to 62 per sample) were collected in sediment samples with mean grain sizes of approximately 0.35 to 0.5 mm.</p>
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<p>Graph showing mass transport rate of bottom sediment for the San Juan Channel sand wave field based on methods of Van Rijin [<a href="#B34-geosciences-07-00107" class="html-bibr">34</a>] for grain sizes of 0.5 mm (red line) and 2.0 mm (green line).</p>
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<p>MBES bathymetric image of San Juan Channel sand wave field collected in 2007 with comparisons of changes between years 2004 and 2006 and between 2006 and 2007. Note how the general wave morphology is consistent from one year to the next but that slight sediment erosion (warm colors) and accumulation (green colors), probably the result of shifting back and forth of the sand waves, have occurred.</p>
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<p>MBES bathymetric imagery and cross-section (A-A’) of the San Juan Channel sand wave field showing two different sections (1–2 and 3–4) of sediment waves of differing sizes.</p>
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<p>Bed-phase diagram for San Francisco Bay developed by Rubin and McCulloch [<a href="#B27-geosciences-07-00107" class="html-bibr">27</a>] whose observations were combined with those of Southard [<a href="#B25-geosciences-07-00107" class="html-bibr">25</a>], Boothroyd and Hubbard [<a href="#B37-geosciences-07-00107" class="html-bibr">37</a>], and Dalrymple and others [<a href="#B26-geosciences-07-00107" class="html-bibr">26</a>]. Black boxes are for data collected by this study in the San Juan Channel.</p>
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<p>Number of fish in grab sample versus amount of fine sediment (grain size finer than 0.25 mm).</p>
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9451 KiB  
Article
Sediment Transport into the Swinomish Navigation Channel, Puget Sound—Habitat Restoration versus Navigation Maintenance Needs
by Tarang Khangaonkar, Adi Nugraha, Steve Hinton, David Michalsen and Scott Brown
J. Mar. Sci. Eng. 2017, 5(2), 19; https://doi.org/10.3390/jmse5020019 - 21 Apr 2017
Cited by 6 | Viewed by 6847
Abstract
The 11 mile (1.6 km) Swinomish Federal Navigation Channel provides a safe and short passage to fishing and recreational craft in and out of Northern Puget Sound by connecting Skagit and Padilla Bays, US State abbrev., USA. A network of dikes and jetties [...] Read more.
The 11 mile (1.6 km) Swinomish Federal Navigation Channel provides a safe and short passage to fishing and recreational craft in and out of Northern Puget Sound by connecting Skagit and Padilla Bays, US State abbrev., USA. A network of dikes and jetties were constructed through the Swinomish corridor between 1893 and 1936 to improve navigation functionality. Over the years, these river training dikes and jetties designed to minimize sedimentation in the channel have deteriorated, resulting in reduced protection of the channel. The need to repair or modify dikes/jetties for channel maintenance, however, may conflict with salmon habitat restoration goals aimed at improving access, connectivity and brackish water habitat. Several restoration projects have been proposed in the Skagit delta involving breaching, lowering, or removal of dikes. To assess relative merits of the available alternatives, a hydrodynamic model of the Skagit River estuary was developed using the Finite Volume Community Ocean Model (FVCOM). In this paper, we present the refinement and calibration of the model using oceanographic data collected from the years 2006 and 2009 with a focus on the sediment and brackish water transport from the river and Skagit Bay tide flats to the Swinomish Channel. The model was applied to assess the feasibility of achieving the desired dual outcome of (a) reducing sedimentation and shoaling in the Swinomish Channel and (b) providing a direct migration pathway and improved conveyance of freshwater into the Swinomish Channel. The potential reduction in shoaling through site-specific structure repairs is evaluated. Similarly, the potential to significantly improve of brackish water habitat through dike breach restoration actions using the McGlinn Causeway project example, along with its impacts on sediment deposition in the Swinomish Navigation Channel, is examined. Full article
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Figure 1

Figure 1
<p>Study area showing, (<b>a</b>) Northern Puget Sound area and (<b>b</b>) Swinomish Channel near the mouth of Skagit River estuary, located in Whidbey Basin, Puget Sound, Washington. (A) North Dike; (B) McGlinn Island to Goat Island Jetty; (C) South Jetty; (D) McGlinn Island to Mainland causeway, (E) McGlinn Island.</p>
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<p>(<b>a</b>) The Skagit River estuary model grid with expanded domain covering Padilla Bay to the north and Saratoga Passage to the South with the Swinomish Channel connecting the two basins; (<b>b</b>) the locations of May 2006 (blue circles), WHOI study mooring stations during June of 2009 [<a href="#B13-jmse-05-00019" class="html-bibr">13</a>] (green diamonds), and for scenario evaluation (red circles).</p>
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<p>Comparison of observed tide, salinity, and currents in the Swinomish Channel (<a href="#jmse-05-00019-f002" class="html-fig">Figure 2</a>b) shown as example at stations S1, S2, and S4 during May of 2006 as part of hydrodynamic model setup and calibration.</p>
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<p>Skagit River daily (<b>a</b>) average flow and (<b>b</b>) estimated TSS at Mt. Vernon, Washington.</p>
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<p>Comparison of bottom currents in Skagit tidal flats shown at stations 3c, 2c, 2f and 1c during June of 2009 as part of sediment model setup and calibration. (<span class="html-italic">u</span><sup>obs</sup> = observed east velocity component, <span class="html-italic">v</span><sup>obs</sup> = observed north velocity component, <span class="html-italic">u</span><sup>mod</sup> = modeled east velocity component and <span class="html-italic">v</span><sup>mod</sup> = modeled north velocity component).</p>
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<p>Comparison of observed bottom stress in Skagit tidal flats shown at stations 3c, 2c and 2f during June of 2009 as part of sediment model setup and calibration. (τ<sup>obs</sup> = observed bottom shear stress and τ<sup>mod</sup> = modeled bottom shear stress).</p>
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<p>Comparison of observed salinity (<span class="html-italic">s</span><sup>obs</sup>-bot = observed bottom salinity, <span class="html-italic">s</span><sup>obs</sup>-suf = observed bottom salinity, <span class="html-italic">s</span><sup>m</sup><sup>od</sup>-bot = modeled bottom salinity and <span class="html-italic">s</span><sup>m</sup><sup>od</sup>-suf = modeled surface salinity) and SSC (<span class="html-italic">ssc</span><sup>obs</sup>-abs = observed acoustic backscatter sensor suspended sediment, <span class="html-italic">ssc</span><sup>obs</sup>-adv = observed acoustic Doppler velocitymeter suspended sediment and <span class="html-italic">ssc</span><sup>m</sup><sup>od</sup> = modeled suspended sediment) in Skagit tidal flats at stations 3c and 1c.</p>
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<p>Close-up of the model grid and bathymetry used in baseline (or existing conditions) simulation. Note—color contours indicate depths (negative elevations) relative to NAVD88 datum.</p>
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<p>Modified model configuration used for (<b>a</b>) SCN-1 South Jetty Repair and (<b>b</b>) SCN-2, McGlinn Causeway Restoration. Note—color contours indicate depths (negative elevations) relative to NAVD88 datum.</p>
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<p>Time averaged salinity distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) Jetty Repair Scenario, SCN-1.</p>
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<p>Time averaged bottom shear stress distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) Jetty Repair Scenario, SCN-1.</p>
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<p>Time averaged total suspended sediment (TSS) distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) Jetty Repair Scenario, SCN-1.</p>
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<p>Time averaged salinity distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) McGlinn Causeway restoration, SCN-2.</p>
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<p>Time averaged bottom shear stress distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) McGlinn Causeway restoration, SCN-2.</p>
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<p>Time averaged total suspended sediment (TSS) distribution (20 days) for (<b>a</b>) baseline conditions and (<b>b</b>) McGlinn Causeway restoration, SCN-2.</p>
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<p>Time averaged horizontal distribution difference between baseline conditions and SCN-1 for (<b>a</b>) salinity (<b>c</b>) shear stress and (<b>e</b>) TSS; time averaged horizontal distribution difference between baseline conditions and SCN-2 for (<b>b</b>) salinity (<b>d</b>) shear stress and (<b>f</b>) TSS. The positive sign of the color bar means that the baseline concentrations are higher than the selected scenario.</p>
Full article ">Figure 16 Cont.
<p>Time averaged horizontal distribution difference between baseline conditions and SCN-1 for (<b>a</b>) salinity (<b>c</b>) shear stress and (<b>e</b>) TSS; time averaged horizontal distribution difference between baseline conditions and SCN-2 for (<b>b</b>) salinity (<b>d</b>) shear stress and (<b>f</b>) TSS. The positive sign of the color bar means that the baseline concentrations are higher than the selected scenario.</p>
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<p>Comparison of time series of salinity, bed shear stress and TSS at (<b>a</b>) station A and (<b>b</b>) station B (<a href="#jmse-05-00019-f002" class="html-fig">Figure 2</a>b) for baseline, Jetty Repair Scenario, SCN-1, and McGlinn Causeway restoration, SCN-2.</p>
Full article ">
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