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118 results for “nearshore”
Geoclaw inputs and processed outputs used in building tsunami ML surrogates for nearshore and onshore approximation
<p>This dataset contains some input files needed for GeoClaw tsunami simulation and post-processed outputs used for training the nearshore and onshore surrogates discussed in the article - Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates available as preprint (https://doi.org/10.5194/nhess-2024-72) and project repo - https://github.com/naveenragur/tsunami-surrogates</p> <p>The GeoClaw simulation requires the following files: </p> <p><strong><a href="../api/records/10817116/draft/files/geoclaw_dtopo_files.tar.gz/content" target="_blank" rel="noopener noreferrer">geoclaw_dtopo_files.tar.gz</a></strong><strong>: </strong>input bathymetry and topography elevation in asc format from multiple sources of datasets.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_his.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_his.tar.gz</a></strong>: tsunami displacement(dtopo) input files in tt3 format for historic earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeA.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeA.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeB.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeB.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p>The machine learning training and testing requires:</p> <p><strong><a href="../api/records/10817116/draft/files/procesed_tsunami_data.tar/content" target="_blank" rel="noopener noreferrer">procesed_tsunami_data.tar</a>: </strong>the post-processed outputs for the three test location i.e. (1) the waveforms( time series recorded for the water level at the offshore and the nearshore gauge locations and (2) the maximum inundation depth recorded at the fixed grids onshore.</p> <p><a href="https://zenodo.org/uploads/14902165"><strong>tsunami-surrogates-nhess-2024-72.tar.gz</strong></a><a href="https://zenodo.org/uploads/14902165"> </a>: Model code, scripts and notebooks used in the study</p>
Nearshore marine biodiversity of Osa Peninsula, Costa Rica
<p>Osa Peninsula in remote southwest Costa Rica harbors 2.5% of global terrestrial biodiversity in only 1,200 km2 and has the largest remaining tract of Pacific lowland wet forest in Mesoamerica. However, little is known about the marine ecosystems of this diverse region. Much of the coastline consists of soft sediment exposed to strong wave action. Three major hard bottom habitat types define this region, including: 1) coral reefs around Isla del Caño Biological Reserve, a no-take marine protected area (MPA) of 52 km<sup>2</sup>, 2) coastal rocky reefs and islets along the peninsula, including Corcovado National Park, and 3) submerged pinnacles just outside the Isla del Caño MPA. Average coral cover at Isla del Caño was 21%, composed primarily of<em> Porites lobata</em> and <em>Pocillopora elegans</em>. In contrast, coastal rocky reefs were dominated by turf algae (39.8%) and macroalgae (20.7%) with low coral cover (1.1%). Submerged pinnacles were dominated by crustose coralline algae (33.3%) and erect coralline algae (25.7%). Fish assemblage characteristics (species richness, abundance, biomass) were significantly higher at the pinnacles compared to the other habitats and was dominated by schooling species such as Haemulon steindachneri, and the herbivores <em>Kyphosus ocyurus</em>, and <em>Acanthurus xanthopterus.</em> Top predators, primarily <em>Triaenodon obesus</em>, <em>Caranx sexfasciatus</em>, and <em>Lutjanus argentimaculatus</em>, were also most abundant at these pinnacles and accounted for the largest differences in fish trophic structure among habitats. Despite Isla del Caño being fully protected from fishing, biomass was similar to fished areas along the coast and lower than the adjacent submerged pinnacles outside the reserve. Similarly, Corcovado National Park includes 20.3 km<sup>2</sup> of no-take MPAs; however, there is limited enforcement, and we noted several instances of fishing within the park. The unique configuration of healthy offshore coral reefs and pinnacles connected to coastal habitats provides corridors for many species including large predators such as sharks and other marine megafauna, which warrants additional protection.</p>
Fig. 4 in Ostracodes (Crustacea, Ostracoda) In The Rocky Nearshore Water Area Of Zmiiniy Island (Black Sea)
Fig. 4. The average means of the ostracodes biomass on different substrates in Zmiiniy Island.
Fig. 1 in Ostracodes (Crustacea, Ostracoda) In The Rocky Nearshore Water Area Of Zmiiniy Island (Black Sea)
Fig. 1. Location of Zmiiniy Island study area in north-western Black Sea, Ukraine.
Central California Shallow Nearshore Temperature for Marine Heatwave/Cold Spell Analysis
<p>These are daily-averaged temperature data for use in marine heatwave and cold spell analyses. The data were originally used and described in Dalsin et al. (2023): <a href="https://www.nature.com/articles/s41598-023-39193-4">https://www.nature.com/articles/s41598-023-39193-4</a></p> <p>Relevant details from Dalsin et al. (2023) copied below:</p> <p>Temperature data from a long-term, subtidal site located along the central California coast (USA). Data were collected in a shallow (~ 3 m nominal depth) nearshore site adjacent to the Diablo Canyon nuclear power plant (35.2055°N, 120.8500°W). Originally established as a control site outside the influence of the power plant’s thermal outfall, these data span from 1978–2020 and make up the longest known in-situ temperature dataset in this region. Temperature was measured using thermistors sampling at 20-min intervals [see Table S1 in Dalsin et al. (2023)].</p> <p>We acknowledge John Steinbeck from Tenera and Pacific Gas & Electric for assistance acquiring the raw temperature data.</p> <div> </div>
Data from : Measuring nearshore waves at break point in 4D with Stereo-GoPro photogrammetry
<p>This dataset was collected with a stereophotogrammetric method, using a cost-effective stereo system composed of two GoProTM (Hero 7) video cameras.</p> <p>The result is a geotiff DEM time series, with a resolution of 0.2 m, focusing on close-range measurements of nearshore waves at break point.</p> <p>Data collected in the framework of the WEST project (Natural Breaking WavEs and Sediment Transport during beach recovery - ANR-20-CE01-009) granted by the Agence Nationale pour la Recherche (ANR).</p>
Figure 2 in The first record of the eggs of black wing flyingfish Hirundichthys rondeletii (Valenciennes, 1846) in the Turkish nearshores of the Aegean Sea (Gümüldür)
Figure 2. External and internal appearance of Hirundichthys rondeletii eggs (drawings by O Uygun).
Figure 1 in The first record of the eggs of black wing flyingfish Hirundichthys rondeletii (Valenciennes, 1846) in the Turkish nearshores of the Aegean Sea (Gümüldür)
Figure 1. The map of the region where Hirundichthys rondeletii eggs were found.
A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area
<p>This dataset (in ArcGIS project) contains the results of geomorphological spatial analysis of water-related features at the Zhurong landing area in Southern Utopia on Mars. It includes vector data of the Zhurong landing site, crater maps, ghost craters, AMA craters, various geomorphological features (cones, etched flows, troughs, pancake craters, rampart craters), and newly defined geological units. Other supporting data and basemaps are also included.</p> <p>This dataset is related to the following paper currently under review in Scientific Reports:</p> <p>Wu B., Dong J., Wang Y., Rao W., Sun Z., Krasilnikov S., Li Z., Tan Z., Chen Z., Wang C., Ivanov M., Zhu J., Liu W.C., Chen L., Li H. 2024. A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area. </p>
Data/Code for: Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation
<p>This archive contains data and postprocessed results used in the article "Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation" by G. Wilson, P. Dickhudt & J. Aldrich. All data and code in this archive is copyright of the authors. Please contact the authors prior to publishing new results or derivative works based on data/code from this archive.</p>
Fig. 3 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 3. Species richness of fish samples in each habitat during the study period.
Sea turtle relative abundance in nearshore waters adjacent to the Mississippi River delta, Gulf of Mexico, United States
<p><span>We measured the relative abundance of sea turtles using standardized transect surveys conducted during the summer and fall of 2013 in neritic waters surrounding the Mississippi River delta in Louisiana, USA. Data comprise sea turtle locations, observation circumstances, and environmental covariates recorded at the beginning of each transect and at the time of each turtle observation. Turtles were recorded by species and size class, as well as location in the water column and the distance the turtle was from the transect line. Transects were performed on an 8.2-meter vessel with two observers atop a 4.5-meter elevated platform, with vessel speed standardized at ~15 km/hr. These data are the first to describe relative abundance of sea turtles observed from small vessels in this region. Detection of turtles <45 cm SSCL and data detail are greater than aerial surveys. The data serve to inform resource managers and researchers regarding these protected marine species. </span></p>
Shellfish aquaculture farms as foraging habitat for nearshore fishes and crabs
<p>This is the dataset used for the analysis in our paper. The file contains species counts and behavior classifications of nearshore fish and crabs taken from time-lapse underwater video. Also included are habitat transect data from each site. Presence/absence of zostera marina and zostera japonica eelgrass, algae, as well as eelgrass metrics were quantified in the transects.</p> <p>The metadata spreadsheet contains descriptions of each column in the datasheet.</p> <p>This data was collected at shellfish aquaculture farm sites across Puget Sound, Washington, USA in 2017, 2018, and 2019 using underwater timelapse video cameras and habitat transects.</p> <p>Transects were done with 1/4 meter^2 quadrat divided into 25 10x10cm^2 squares, each quadrat was measured 5 times along a 30 meter line parallel to shore.</p> <p>Video was recorded in 2 minute lengths every 10 minutes starting 1 hour before high tide, going to 1 hour after high tide, using cameras positioned approximately 18 inches off the substrate. PVC stakes were used to mark a 1 meter square of camera visibility and organisms observed within that 1 meter square were recorded. </p> <p>For more information, see my chapter 1 thesis.</p>
Data for effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems
<p><strong><em>Okuhata, B.K., Delevaux, J.M.S., Richards Donà, A., Smith, C.M., Gibson, V.L., Dulai, H., El-Kadi, A.I., Stamoulis, K., Burnett, K.M., Wada, C.A., Bremer, L.L., Effects of multiple drivers of environmental change on native and invasive macroalgae in nearshore groundwater dependent ecosystems</em></strong></p> <p>Environmental change scenarios, with a spatial extent of the Keauhou basal aquifer (Hawai‘i), were produced using a recharge coverage from Engott (2011), land use coverages from the State of Hawai‘i (2022) and National Oceanic and Atmospheric Administration (2006); climate change calculations based on Elison Timm et al. (2015), and native forest conversion calculations from Bremer et al. (2021). Scenarios were developed based on the following assumptions:</p> <p>Scenario 0 (Baseline) assumes current land use, groundwater recharge, and groundwater withdrawal rates (National Oceanic and Atmospheric Administration, 2006; State of Hawai‘i, 2022; Engott, 2011; Commission on Water Resource Management, unpublished data, 2018). Please see Okuhata et al. (2021) for more details regarding the scenario assumptions for the baseline groundwater model.</p> <p>Scenario 1 (Climate Change) assumes current land use, but with Representative Concentration Pathway (RCP) 8.5 mid-century rainfall projections (Elison Timm et al., 2015), where rainfall and recharge calculations were based on estimates from Giambelluca et al. (2013) and Engott (2011). </p> <p>Scenario 2 (Urban Development) assumes RCP 8.5 mid-century rainfall conditions along with future permitted development, which includes an increase in water demand (Fukunaga & Associates, Inc., 2017). </p> <p>Scenario 3 (Native Forest Conversion + Urban Development) assumes RCP 8.5 mid-century rainfall conditions and future permitted development, along with the assumption that native forests are not protected and converted to non-native forests (Bremer et al., 2021), therefore altering recharge estimates (Wada et al., 2017; Engott, 2011).</p> <p>Please note that scenario numbers listed in the groundwater model and marine water quality model shapefiles may differ from the manuscript scenario numbers. The following table assigns the scenario numbers to their respective scenarios in the manuscript, groundwater model, and marine water quality model.</p> <table> <tbody> <tr> <td> <p><strong>Scenario Name</strong></p> </td> <td> <p><strong>Manuscript #</strong></p> </td> <td> <p><strong>Groundwater Model #</strong></p> </td> <td> <p><strong>Marine Water Quality Model #</strong></p> </td> </tr> <tr> <td> <p>Baseline</p> </td> <td> <p>Scenario 0</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 0</p> </td> </tr> <tr> <td> <p>Climate Change</p> </td> <td> <p>Scenario 1</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 1</p> </td> </tr> <tr> <td> <p>Urban Development</p> </td> <td> <p>Scenario 2</p> </td> <td> <p>Scenario 7</p> </td> <td> <p>Scenario 6</p> </td> </tr> <tr> <td> <p>Native Forest Conversion + Urban Development</p> </td> <td> <p>Scenario 3</p> </td> <td> <p>Scenario 5</p> </td> <td> <p>Scenario 4</p> </td> </tr> </tbody> </table> <p>The groundwater model results are in shapefile format and were produced using the program SEAWAT (Langevin et al., 2008). The spatial extent is the Keauhou basal aquifer, and the projection is NAD 1983 UTM Zone 4N. The two polygon shapefiles represent the first and second layers of the groundwater model, and include groundwater level (meters relative to mean sea level), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (milligrams per liter), and phosphorus (milligrams per liter) results under the assumptions of each scenario. The point shapefile represents the simulated discharge at SGD plumes under the assumptions of each scenario.</p> <p>The marine water quality model results are in floating point TIFF format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the groundwater discharge (cubic meters per month), salinity (parts per thousand), temperature (degrees Celsius), nitrogen (kilograms per month), and phosphorus (kilograms per month) results under the assumptions of each scenario.</p> <p>The limu model results are in shapefile format and were produced using the program R software. The spatial extent is the coastal area of the Keauhou aquifer system, and the geographic coordinate system is WGS 1984. The files include the increase and decrease in area (hectares) for <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>under the assumptions of each scenario.</p> <p>The limu experiment results are derived from a csv file, which reports the <em>Ulva lactuca</em> and <em>Hypnea musciformis </em>measured weights (initial and final) for each growth run. These were used to calculate the weight difference. Included also in the dataset are the fixed and random effects used in the R script to run the model.</p> <p>Contact Leah Bremer (<a href="mailto:lbremer@hawaii.edu">lbremer@hawaii.edu</a>) or Brytne Okuhata (bokuhata@hawaii.edu) of the University of Hawaiʻi for more information on these files.</p>
Phytoplankton metabolism in a stratified nearshore ecosystem with recurrent Harmful Algal Blooms (HABs)
<p>Datasets from HAB monitoring program in Mallorca</p>
Data and code from: Spatially-explicit foraging by an apex predator linked to nearshore prey and their accessibility in lakes
Open the record for dataset details and reuse information.
Sea turtle relative abundance in nearshore waters adjacent to the Mississippi River delta, Gulf of Mexico, United States
Open the record for dataset details and reuse information.
Nearshore marine biodiversity of Osa Peninsula, Costa Rica
Open the record for dataset details and reuse information.
California Current Ecosystem site, station Ohman Region: subset of CalCOFI stations inshore and nearshore in the Southern California Bight region; CalCOFI lines 80-93, stations from shore offshore to station 70, study of biomass of zooplankton in units of Log10(millilitersPerSquareMeter) on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains biomass of zooplankton measurements in Log10(millilitersPerSquareMeter) units and were aggregated to a yearly timescale.
California Current Ecosystem site, station Ohman Region: subset of CalCOFI stations inshore and nearshore in the Southern California Bight region; CalCOFI lines 80-93, stations from shore offshore to station 70, study of chlorophyll a in units of microgramsPerLiter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains chlorophyll a measurements in microgramsPerLiter units and were aggregated to a yearly timescale.
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