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107 results for “turbidity”
Year 2001, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge
Year 2001, Continuous, 30 minute time interval, water quality measurements of water column temperature, salinity, conductivity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge, Newbury, MA.
Year 2002, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge
Year 2002, Continuous, 30 minute time interval, water quality measurements of water column temperature, salinity, conductivity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge, Newbury, MA.
Data used in 'Slumping regime in lock-release turbidity currents'
<p>This repository contains the data used in the paper:</p><blockquote><p><strong>Gadal, C., Mercier, M., Rastello, M., & Lacaze, L. (2023). Slumping regime in lock-release turbidity currents. </strong><i><strong>Journal of Fluid Mechanics,</strong></i><strong> </strong><i><strong>974</strong></i><strong>, A4. doi:10.1017/jfm.2023.762</strong></p></blockquote><p><br>where the slumping regime of turbidity currents is studied with respect to the initial volume fraction, the bottom slope and the particle settling velocity. The folder 'runs' contains 169 netcdf4 files corresponding to each experimental run used in the paper. For each run, the structure of the NetCDF file is the following:</p><ul><li>attributes:<ul><li>particle_type: particle type used (silica sand, glass beads or saline water)</li><li>run_number: NetCDF file name</li><li>expe_type: always lock-release here</li><li>surface_type: can be 'open surface' or 'rigid lid'</li><li>set_up: can be 'set-up 1' or 'set-up 2'</li><li>run_oldID: run name corresponding to the experimental notebook</li></ul></li><li>groups:<ul><li>initial_parameters:<ul><li>dimensions(sizes):</li><li>variables(dimensions):<ul><li>Bottom slope(): bottom slope</li><li>Current density(): initial average (fluid + particle) lock density</li><li>Grain density(): particle density (not measured, estimated)</li><li>Grain diameter(): particle diameter</li><li>Initial Reynolds number(): initial Reynolds number, [rho_0 * u_0 * h_0 / mu]</li><li>Initial Rouse number(): initial Rouse number, [v_s / u_0]</li><li>Initial volume fraction(): initial lock particle volume fraction</li><li>Reduced gravity(): reduced gravity, [g*(rho_0 - rho_f)/rho_f]</li><li>Settling velocity(): particle settling velocity</li><li>Temperature(): water temperature (not measured)</li><li>V0 (lock volume)(): lock suspension volume</li><li>Water density(): water density</li><li>Water dynamic viscosity(): water dynamic viscosity (not measured)</li><li>h0 (lock height)(): suspension height inside lock</li><li>u0 (velocity scale)(): velocity scale, [sqrt(g'*h_0)]</li><li>w0 (tank width)(): lock crosstream width</li><li>x0 (lock length)(): lock streamwise length</li></ul></li></ul></li><li>scalar_variables:<ul><li>dimensions(sizes): tuples(2), x(1181)</li><li>variables(dimensions):<ul><li>Av. shape('x',): current average shape</li><li>Av. shape head volume(): Volume per unit of width of the head part of current average shape</li><li>Av. shape tail volume(): Volume per unit of width of the tail part of current average shape</li><li>Av. shape volume(): Volume per unit of width of current average shape</li><li>Bulk entrainment coefficient(): Bulk entrainment coefficient during slumping</li><li>Current Froude number(): Current Froude number, [u_c/sqrt(g' * h_b)]</li><li>Current Reynolds number(): Current Reynolds number, [rho_0 * u_c * h_b / mu]</li><li>Current Rouse number(): Current Rouse number, [v_s / u_c]</li><li>Current head height (log fit)(): current height h_h coming from log fit</li><li>Current height (benjamin fit)(): current height h_b coming from fit of Benjamin's shape</li><li>Current nose height (benjamin fit)(): current nose height h_n coming from fit of Benjamin's shape</li><li>Current nose height (log fit)(): current nose h_n coming from log fit</li><li>Geometrical Froude number(): Current Geometrical Froude number, [u_c/sqrt(g' * h0)]</li><li>Geometrical Reynolds number(): Current Geometrical Reynolds number [rho_0 * u_c * h0 / mu]</li><li>Times lock opening (tstart, tend)('tuples',): Start and end times of lock opening</li><li>Times slumping regime (tstart, tend)('tuples',): Start and end times of constant velocity regime</li><li>Velocity (slumping regime)(): Current velocity during slumping</li><li>time_series:</li><li>dimensions(sizes): time(3071)</li><li>variables(dimensions):</li><li>Volume('time',): current volume per unit of width</li><li>contour time series (x)('time',): x coordinate time series of the current contours</li><li>contour time series (y)('time',): y coordinate time series of the current contours</li><li>position('time',): front position</li><li>time('time',): time vector</li><li>velocity('time',): front velocity</li></ul></li></ul></li></ul></li></ul><p><br>Most variables possess the following attributes:</p><ul><li>unit: corresponding unit</li><li>std: error(s) on the given quantity, calculated by error propagation from measurement uncertainties using the `uncertainties` module (https://pythonhosted.org/uncertainties/) in Python.</li><li>comments: comments on the given quantity (definition, formulas, etc ..)</li></ul><p>Note that all variables related to the current shape are not available for experimental runs carried out in set-up 2.<br>The script ReadPlotData.py shows how to display the structure of a NetCDF file, and gives examples of how to load some variables and plot them by reproducing some of the paper's figures.<br>The CSV file 'dataset_summary.csv' offers a summary of all runs and corresponding experimental parameters, allowing for easier access for testing purposes. *Note that errors are not given in this file.*</p><p>OpenData License: licence-ouverte-v2.0</p><p>If you use this open data in your work (research or other), please cite in your bibliography the following reference doi:https://doi.org/10.1017/jfm.2023.762</p>
Data and R script for: Shoaling behaviour in response to turbidity in three-spined sticklebacks
<p class="MsoNormal"><span>Many fresh and coastal waters are becoming increasingly turbid because of human activities, which may disrupt the visually-mediated behaviours of aquatic organisms. Shoaling fish typically depend on vision to maintain collective behaviour, which has a range of benefits including protection from predators, enhanced foraging efficiency, and access to mates. Previous studies of the effects of turbidity on shoaling behaviour have focussed on changes to nearest neighbour distance and average group-level behaviours. Here, we investigated whether and how experimental shoals of three-spined sticklebacks (<em><span>Gasterosteus aculeatus</span></em>) in clear (<10 <span>Nephelometric Turbidity Units (NTU))</span> and turbid (~35 NTU<span>) </span>conditions differed in five local-level behaviours of individuals (nearest and furthest neighbour distance, heading difference with nearest neighbour, bearing angle to nearest neighbour, and swimming speed). These variables are important for the emergent group-level properties of shoaling behaviour. We found an indirect effect of turbidity on nearest-neighbour distances driven by a reduction in swimming speed, and a direct effect of turbidity which increased variability in furthest neighbour distances. In contrast, the alignment and relative position of individuals was not significantly altered in turbid compared to clear conditions. Overall, our results suggest that the shoals were usually robust to adverse effects of turbidity on collective behaviour, but group cohesion was occasionally lost during periods of instability.</span></p>
Data from: Turbidity drives plasticity in the eyes and brains of an African cichlid
<p>Natural variation in environmental turbidity correlates with variation in the visual sensory system of many fishes, suggesting that turbidity may act as a strong selective agent on visual systems. Since many aquatic systems experience increased turbidity due to anthropogenic perturbations, it is important to understand the degree to which fish can respond to rapid shifts in their visual environment, and whether such responses can occur within the lifetime of an individual. We examined if developmental exposure to turbidity (Clear <5 NTU, Turbid ~9 NTU) influenced the size of morphological structures associated with vision in the African cichlid <em>Pseudocrenilabrus multicolor</em>. Parental fish were collected from two sites (clear swamp, turbid river) in western Uganda. F1 broods from each population were split and reared under clear and turbid rearing treatments until maturity. We measured morphological traits associated with the visual sensory system (eye diameter, pupil diameter, axial length, brain mass, optic tectum volume) over the course of development. Age was significant in explaining variation in visual traits even when standardized for body size, suggesting an ontogenetic shift in the relative size of eyes and brains. When age groups were analyzed separately, young fish reared in turbid water grew larger eyes than fish reared in clear conditions. Population was important in the older age category, with swamp-origin fish having relatively larger eyes and optic lobes relative to river-origin fish. Plastic responses during development of fish may be important in responding to a more variable visual environment associated with anthropogenically induced turbidity.</p>
Dataset of the "Unwrapping Non-Locality in the Image Transmission Through Turbid Media"
<p><strong>This post provides the dataset associated with the study titled "Unwrapping Non-Locality in Image Transmission Through Turbid Media."</strong></p> <p>The "X_random.npy" file contains speckle image data with dimensions (150,000, 128, 128). The first axis spans the 150,000 images used in this study. The first 120,000 images are speckles corresponding to randomly generated images. The next 10,000 images (from 120,000 to 130,000) correspond to the first 10,000 images of the CelebA dataset. The remaining 20,000 (from 130,000 to 150,000) images consist of the first 10,000 images of the CIFAR and MNIST datasets, respectively.</p> <p>The ground truth images (SLM commanded images) are provided in "gt_random.npy", "gt_cifar.npy", and "gt_mnist.npy" for randomly generated images, CIFAR, and MNIST datasets, respectively. Each image in these datasets is scaled to a resolution of 128 by 128 pixels. The faces dataset are the 128 by 128 resized images of the CelebA dataset.</p> <p>A code example of the presented neural network model and its architecture implementation is given in "<strong>GAM on ImageNet data.py</strong>". This code reads the training dataset (speckle images) from the Ref [1] study ("x_train.npy") and rescales them to 128 by 128 pixels. The rescaled data, along with their corresponding resized SLM commanded images ("y_train.npy"), are used for training the GAM model (the model presented in the manuscript). This code then saves three files: "Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy," which contain the trained model weights and biases and the sampling index of the model nodes, as described in the manuscript and commented on in the "GAM on ImageNet data.py" code.</p> <p>Code for testing this model is also provided in "test GAM.py." This file reads the testing dataset from the [1] study, specifically "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" speckle images. It applies the trained model ("Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy") to them. The code then calculates the structural similarity index (SSIM) to the ground truth files, namely "y_test_cat.npy," "y_test_horse.npy," "y_test_punch.npy," and "y_test_parrot.npy" for the "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" data, respectively.</p> <h3>Reference</h3> <p>[1] Caramazza, P., Moran, O., Murray-Smith, R., & Faccio, D. (2019). Transmission of natural scene images through a multimode fibre. Nature Communications, 10(1), 2029.</p>
Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas
<p>Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas at multiple stations in 2018, 2019 and 2020.</p>
Turbidity shapes shallow Southwestern Atlantic benthic reef communities
<p>Southwestern Atlantic reefs (Brazilian Province) occur along a broad latitudinal range (~5°N-27°S) and under varied environmental conditions. We provide here the database (reef benthic coverage) used by Santana et al. "Turbidity shapes shallow Southwestern Atlantic benthic reef communities" (Marine Environmental Research, in press). The data encompasses the four Brazilian oceanic islands and the coast (139 sites distributed between 0°55’N - 27°00’S) and is composed by a combination of location and depth.</p>
Supplementary Material - Marine animal forests in turbid environments are overlooked seascapes in urban areas
<p>Figures S1, S2, and S3 of the manuscript "Marine animal forests in turbid environments are overlooked seascapes in urban areas" accepted in the open-access journal Ocean and Coastal Research (Soares et al. 2023)</p>
Increased water temperature and turbidity act independently to alter social behaviour in guppies (Poecilia reticulata)
<p>Changes in environmental conditions can shift the costs and benefits of aggregation or interfere with the sensory perception of near neighbours. This affects group cohesion with potential impacts on the benefits of collective behaviour such as reduced predation risk. Organisms are rarely exposed to one stressor in isolation, yet there are only a few studies exploring the interactions between multiple stressors and their effects on social behaviour. Here we tested the effects of increased water temperature and turbidity on refuge use and three measures of aggregation in guppies (<em>Poecilia</em> <em>reticulata</em>), increasing temperature and turbidity in isolation or in combination. When stressors were elevated in isolation, the distribution of fish within the arena as measured by the index of dispersion became more aggregated at higher temperatures but less aggregated when turbidity was increased. Another measure of cohesion at the global scale, the mean inter-individual distance, also indicated that fish were less aggregated in turbid water. This is likely due to turbidity acting as a visual constraint, as there was no evidence of a change in risk perception as refuge use was not affected by turbidity. Fish decreased refuge use and were closer to their nearest neighbour at higher temperatures. However, nearest-neighbour distance was not affected by turbidity, suggesting that local-scale interactions can be robust to the moderate increase in turbidity used here (5 NTU) compared to other studies which show a decline in shoal cohesion at higher turbidity (>100 NTU). We did not observe any significant interaction terms between the two stressors, indicating no synergistic or antagonistic effects. Our study suggests that the effects of environmental stressors on social behaviour may be unpredictable and dependent on the metric used to measure cohesion, highlighting the need for mechanistic studies to link behaviour to the physiology and sensory effects of environmental stressors.</p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
Temperature and turbidity interact synergistically to alter anti-predator behaviour in the Trinidadian guppy
<p>Due to climate change, freshwater habitats are facing increasing temperatures and more extreme weather that disrupts water flow. Together with eutrophication and sedimentation from farming, quarrying and urbanisation, freshwaters are becoming more turbid as well as warmer. Predators and prey need to be able to respond to one another adaptively, yet how changes in temperature and turbidity interact to affect predator-prey behaviour remains unexplored. Using a fully factorial design, we tested the combined effects of increased temperature and turbidity on the behaviour of guppy shoals (<em>Poecilia</em> <em>reticulata</em>) in the presence of one of their natural cichlid predators, the blue acara (<em>Andinoacara</em> <em>pulcher</em>). Our results demonstrate that the prey and predator were in closest proximity in warmer, turbid water, with an interaction between these stressors showing a greater than additive effect. There was also an interaction between the stressors in the inter-individual distances between the prey, where shoal cohesion increased with temperature in clear water, but decreased when temperature increased in turbid water. The closer proximity to predators and reduction in shoaling in turbid, warmer water may increase the risk of predation for the guppy, suggesting that the combined effects of elevated temperature and turbidity may favour predators rather than prey.</p>
Data, Scripts underlying the publication: Identifying the critical turbidity threshold to maintain estuarine tidal flats worldwide
<p>Data & Scripts featured in the currently unpublished manuscript (as of 21-07-2023) Grandjean et al.: 'Identifying the critical turbidity threshold to maintain estuarine tidal flats worldwide'. This study explores the relationship between tidal range and turbidity concentrations on the morphological trajectory of unvegetated tidal flats at a global scale. These files include the scripts used to produce digital elevation models per estuary.</p> <p> </p>
Temperature and turbidity interact synergistically to alter anti-predator behaviour in the Trinidadian guppy
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Data from: Turbidity drives plasticity in the eyes and brains of an African cichlid
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Data and R script for: Shoaling behaviour in response to turbidity in three-spined sticklebacks
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Increased water temperature and turbidity act independently to alter social behaviour in guppies (Poecilia reticulata)
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Data from: Enhanced conspicuousness of prey in warmer water mitigates the constraint of turbidity for predators
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Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities
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Sapelo Island Marsh pCO2, turbidity, and salinity, Summer 2021
This data package includes biogeochemical data from Sapelo Island marsh, Georgia, USA, collected during the summer of 2021. Specifically, this data includes partial pressure of CO2 in water and air measured by the CO2-LAMP monitoring platform, as well as salinity and turbidity measured by the Smart Rock. Units of measurement are parts per million for pCO2, uS/cm for salinity, and Volts for relative turbidity. This data package has been completed.
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