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71 results for “Coastal modelling”
data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers
<p>This is the modeling data for modeling scale of representation of heterogeneity on simulated salinity and saltwater circulation in coastal aquifers. We setup a series of SEAWAT models to see the scale-dependent heterogeneity in simulations of saltwater circulation and cautions</p>
Data files for the article "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region"
<p>This repository contains R data files required for reproducing the results from the article by Räty et al (2021) "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region", submitted to Nat. Hazards Earth Syst. Sci. See the README file for more details on the content of the files.</p>
An empirical evaluation of turbulence closure models in the coastal ocean
<p>Underlying data files and a Matlab script (generate_figure.m) used to generate all figures in the JGR-Oceans paper titled "An empirical evaluation of turbulence closure models in the coastal ocean".</p>
Dataset presented in the recently submitted AGU manuscript "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"
<p>Dataset presented in the recently submitted AGU paper "A multi-resolution finite-element approach for global electromagnetic induction modeling with application to southeast China coastal geomagnetic observatory studies"</p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<h3> FT-ICR-MS spectrometry data.</h3> <ul> <li>LD: LD consortium 1.zip, LD consortium 2.zip, LD consortium 3.zip;</li> <li>P: Pluralibacter gergoviae 1.zip, Pluralibacter gergoviae 2.zip, Pluralibacter gergoviae 3.zip;</li> <li>Blank: Blank 1.zip, Blank 2.zip, Blank 3.zip</li> </ul>
Data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change
<p>This folder will include data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change.</p>
Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"
<p><strong>Research Data related to the publication "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023) published in <em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023). The authors hope that the research data allows for a better understanding of the paleo-modeling workflow. Feedback on the model files or questions regarding the modeling approach etc. can be addressed to the authors of the article, see contact details below. The research data comprises the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "<em>parameter_estimation</em>")</li> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for each model variant. Note that model variants consist of several time slice models, indicated by the corresponding file names, e.g., '<em>Model_BC_slice_01.py'</em> etc. (see '<em>scripts.zip</em>' in the subfolders 'Model BC', 'Model CP', 'Model NE-ND-NP', 'Model NE-NP', 'Model NG', 'Model NP', 'Model R1', 'Model R2', 'Model R3', 'Model R4', 'Model R5', 'Model R6', 'Model SS')</li> <li>simulation output files, including concentration and head data for each model stress period (3-D), mean/max. concentration and head data for each model stress period (2-D), as well as depth [mbgs] of different salinity interfaces (2-D), i.e., marking the transitions from fresher to more saline groundwater using thresholds of 0.45 ('<em>depth_interface_mbgs</em>'), 1, 5, 10 and 20 g TDS L<sup>-1</sup>, respectively (see subfolders '<em>output/npy_arrays'</em> within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the '<em>output/npy_arrays'</em> subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., '<em>Model_BC_slice_01_final_concentrations.npz</em>' and '<em>Model_BC_slice_01_final_heads.npz</em>'; see '<em>output.zip'</em> in the model variant subfolders)</li> <li>iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see subfolder '<em>imod_input'</em>). However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consult the corresponding meta-data files or get in touch with one of the authors for further information</li> <li>bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder '<em>bash_scripts'</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder '<em>figures'</em>); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files</li> <li>videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder '<em>videos'</em>)</li> </ul> <p>Meta-data files are usually provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input data and .run-files were executed on the University Oldenburg High-Performance Cluster 'Carl', running simulations in parallel with 32 computational cores.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>Literature:</p> <p>Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394.</p> <p>Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274.</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p><strong>If you have further questions, please, contact one of the following authors</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Supporting data for manuscript: "Global-scale evaluation of coastal ocean alkalinity enhancement in a fully-coupled Earth system model"
<p>Supporting data for manuscript: "Global-scale evaluation of coastal<br> ocean alkalinity enhancement in a fully-coupled Earth system model"</p> <p>Authors: Julien Palmieri and Andrew Yool</p> <p>Institute: National Oceanography Centre, European Way, Southampton<br> SO14 3ZH, UK</p> <p>This repository consists of four main sets of files:</p> <p>1. Matlab scripts used for analysis, figure plotting and table<br> preparation</p> <p> Filenames of the format: Figure_??.m</p> <p>2. Raw netCDF output files from UKESM1 for six model experiments</p> <p> Filenames of the format: medusa_c*.nc</p> <p>3. BGCVal processed timeseries shelve files</p> <p> Filenames of the format: u-c*.shelve.txt</p> <p>4. CMM2 processed timeseries netCDF files</p> <p> Filenames of the format: c*_global.nc</p> <p>File sets 2-4 are read and processed by script files in file set 1<br> </p>
Dataset accompanying the publication: Reservoir mud releasing may suboptimize fluvial sand supply to coastal sediment budget: Modeling the impact of Shihmen Reservoir case on Tamsui River estuary
<p>Delft3D model input and output files for scenario simulations (Scenario 1, Scenario 2, and Scenario 3)</p>
Data from: Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins
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Data from: Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution
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Runoff modeling of a coastal basin to assess variations in response to shifting climate and land use: Implications for managed recharge
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Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - 1/2 degree WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a 1/2 degree structured grid.</p> <ul> <li>glo_30m.bot <ul> <li>Bottom depth file for a 1/2 degree structured grid</li> </ul> </li> <li>glo_30m.mask <ul> <li>Mask file for a 1/2 degree structured grid</li> </ul> </li> <li>obstructions_local.glo_30m.in <ul> <li>local obstructions file for use with UOST source term switch</li> </ul> </li> <li>obstructions_shadow.glo_30m.in <ul> <li>shadow obstructions file for use with UOST source term switch</li> </ul> </li> <li>ww3_grid.inp <ul> <li>Input file for the ww3_grid pre-processing program. This file specifies many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Coastal Wetland Crab Image Dataset and Image Processing Model Weights
<p>This data was collected from the distribution range of mangroves along the coast of China and has been randomly selected and manually corrected to be labeled as a crab image analysis dataset. The weights of deep learning models trained on YOLOv5/v8 and EfficientNet are also uploaded simultaneously. Additionally, it includes the necessary test datasets and some test results. Please cite when using this dataset, and contact the administrator if you need help. The specific directories are as follows:</p> <p>- R-crab: Contains code needed to test model performance, with the subfolder data containing the test dataset required. It includes (1) crab-man as human-marked references, crab-ref as model detection results. (2) luoyuan-burrow for the detection results of crab burrows in the Luoyuan area case study, and luoyuan-crab for crab detection results, including crab classification, localization, and carapace width information. (3) method-test for testing different methods, i.e., whether to use a two-stage detection model. (4) size-conf-test records the model detection results under different image input sizes and confidence threshold levels. (1), (3), and (4) are completed in Out-of-sample data, while (2) is completed in Luoyuan. fig_attr.csv records the test results of image attributes on detection accuracy. label_results.csv records the comparison results between traits measured manually using ImageJ software and our designed model for detecting crab carapace width. luoyuan_list.csv records the numbering information of Luoyuan sampling plots.<br>- Aiweights: Contains model weights trained based on Object detection data, with cpm-model under v8n-seg-crab.pt for YOLOv8 trained to extract crab carapace width. Sfc-model under adam20.pth is a two-stage detection model trained based on EfficientNet. Trained_weights under burrow-baseline is a crab burrow detection model trained based on our improved YOLOv5 (improvements stored at https://github.com/GuuX29/crab-yolo-add, same below); frame-s6-2560.pt is a plot frame detection model for obtaining standard 50*50cm plot images; s-simam-20.pt and x-simam-20.pt are both for crab detection and classification models, with x having higher accuracy.<br>- Luoyuan: crop stores standardized processed Luoyuan image data, numbered as above, test stores detection results, same as R-crab.<br>- Object detection: Contains cropped 640 pixels crab field sampling images, with images in the images folder and bounding box labels in the labels folder, divided into training and testing at a 9:1 ratio, train.txt and val.txt record the allocation information.<br>- Out-of-sample: Records 100 independent test images not used to train the model, stored in images, crab-man, and crab-ref are the same as in R-crab.<br>- Segmentation: Stores image data used to test model detection of crab carapace width, with results also stored in R-crab.</p> <p>We hope this data and method will benefit the progress of research in this field. For any suggestions for improvement and help with usage, please contact the administrator. guuxuan1994@gmail.com </p>
Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters
<p><span>Seagrass meadows are generally diverse in China and have the same essential ecosystem services as elsewhere. However, an evaluation of seagrass distribution across China is still lacking, and the magnitude and direction of changes in seagrass meadows remains unclear. Our primary objective was to provide a nationwide seagrass distribution map, and to explore the dynamic changes of seagrass population under global climate change. We use simulation studies within the modelling software MaxEnt with 58961 occurrence records and 27 marine environmental variables, to simulate the potential distribution of seagrasses and calculate the area. 7 environmental variables were deleted before the modelling processes based on a correlation analysis to ensure predicted suitability. The predicted area was 790.09 km<sup>2</sup>, which is much larger than the known seagrass distribution in China, and would be increased to 923.62 km<sup>2</sup> by the year 2100. However, the suitable habitat of almost all seagrass will shift northwest in the future. The sum of individual family will under-predict the national distribution of seagrass, showed a downward trend consistently in the future. Out of all environmental variables, the physical ones (e.g. depth, land distance and sea surface temperature) had the greatest contribution in predicting seagrass distributions, and nutrients (e.g. nitrate, phosphate) ranked among the key influential predictors for habitat suitability in our focal area. As this is a first effort to fill a gap in our understanding of the distribution of seagrass in China, further studies are necessary using both modeling and biological/ecological approaches. </span></p>
Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters
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Regional model (ACM) output used to elucidate coastal ocean carbon transport processes in the northwest North Atlantic (Rutherford and Fennel, 2022; GRL)
<p>Key variables from the regional Atlantic Canada model (ACM) used to elucidate coastal ocean carbon transport in the northwest North Atlantic. The dataset includes all model variables required to reproduce the results in Rutherford & Fennel (2022, <em>GRL</em>). See RutherfordandFennel_GRL_ACM_data_README.txt for more details.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.