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103 results for “lake water quality”
Coupling nitrogen removal and watershed management to improve global lake water quality
<p>Updated dataset and code for the article "Coupling nitrogen removal and watershed management to improve global lake water quality" after removing deep lakes.</p>
Training course materials: Introduction to Remote Sensing of Water Quality in Lakes
<p>Dataset including ESA Sentinel-2 MSI and Sentinel-3 OLCI L1B scenes (L1B), atmospherically corrected reflectance products (C2RCC), image subsets, and intermediate water quality products from the Copernicus Land Service and ESA Lakes_cci processing chain <em>Calimnos. </em>Data are in support of a recorded course introducing the theoretical and practical basis for water quality observation of lakes through remote sensing. See https://monocle-h2020.eu/Resources/Training</p> <p>The data package contains the following:</p> <p><strong>input_data.zip </strong>- unprocessed (L1B / L1C) satellite scenes of MSI and OLCI instruments over the Razelm/Sinoe lagoon and Black Sea</p> <p><strong>Output_S3_OLCI.zip - </strong>all outputs derived from the Sentinel-3 OLCI scene, including C2RCC and <em>Calimnos </em>(including POLYMER atmospheric correction and biogeochemical estimates as in the Lakes_cci)</p> <p><strong>Output_S2_MSI_Full_Scene_C2RCC.zip</strong> - atmospherically corrected (C2RCC algorithm) full Sentinel-2 MSI scene</p> <p><strong>Output_S2_MSI_Subset_C2RCC.zip </strong>- subset of Sentinel-2 MSI scene and atmospherically corrected results (C2RCC algorithm) </p> <p><strong>Output_S2_MSI_Calimnos_PML.zip </strong>- subset of Sentinel-2 MSI scene atmospherically corrected using POLYMER and biogeochemical estimates, using <em>Calimnos </em>as in the Copernicus Land Service. </p>
Satellite-derived water quality data for Lake Harsha (USA) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Lake Harsha (USA) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).
Satellite-derived water quality data for Lake Hume (Australia) 2015-2019
<p>This dataset contains satellite-derived water quality (WQ) data of Lake Hume (Australia) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).</p>
Satellite-derived water quality data for Lake Mulargia (Sardinia, Italy) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Lake Mulargia (Sardinia, Italy) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).
Figure 2 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 2. Results of Cluster Analysis.
Figure 1 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 1. Rusałka Lake in Szczecin City, own elaboration, after Poleszczuk et al. (2012).
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
Stream water quality data in Tianmu Lake watershed
<p><span>Water samples were gathered from a total of 30 sites across the Tianmu Lake Watershed from July 2020 to September 2022. </span><span>Sampling was conducted approximately monthly throughout the study period. </span></p>
Tourists' perceptions of water quality in Finland, dataset, related to article "Browning of Boreal Lakes: Do Public Perceptions and Governance Meet the Biological Foundations?"
<p>Tourists' perceptions of water quality in Finland, dataset, related to article "Browning of Boreal Lakes: Do Public Perceptions and Governance Meet the Biological Foundations?" by Albrecht et al. (2022).<br> <br> <strong>Article abstract:</strong><br> "Surface water browning, also known as brownification, is an acute environmental issue concerning inland waters. We adopt an interdisciplinary perspective and combine limnology, ecology, environmental law, tourism, and economics to build a theoretical framework to understand the consequences and management needs concerning water browning, generate a research agenda, and pinpoint acute research needs. Using a systematic review approach, we identify gaps of knowledge and address the impacts of browning on lake ecosystem functioning. To support the biological information, we present primary survey data to define the recreational use and public perception of waters in Finland. We identified an urgent need for the development of indicators of brownification beyond EU’s Water Framework Directive (WFD) to be able to balance the costs of browning with terrestrial activities that increase browning. Despite a considerable body of research already exists, there is still a need for better understanding of the biogeochemical processes related to browning to progress towards ecosystem-based management of inland waters. Public perception of the quality of waterbodies in Finland was largely in agreement with the proportion of waterbodies classified to meet the good or excellent ecological status, but recreational fishers may value different aspects of water quality compared to the classification based on WFD. Consequently, to build an interdisciplinary understanding of how browning affects boreal lakes and their uses, we suggest improvements for environmental regulation concerning the impact assessment of terrestrial land use activities and ecosystem-based management of inland waters."</p>
Data for "Upward migration of calanoid copepods is driven by high food quality in surface waters in an alpine lake"
<p>In this study, we explored why zooplankton migrated to surface waters at night from the perspective of their physiological characteristics and adaptability to the environment. The calanoid Arctodiaptomus sp. accumulated large amounts of polyunsaturated fatty acids (PUFAs) and astaxanthin, which relieved oxidative stress to fatty acids. The concentrations of lutein, a precursor of astaxanthin synthesis, were highest in surface water, indicating the enhancement of ultraviolet radiation (UVR) to precursor synthesis, which was confirmed by our indoor experiment. The calanoids migrated to surface water at night to obtain high concentrations of lutein and PUFAs from their diets. Relevant data for this study include: the vertical distribution of <em>Arctodiaptomus</em> sp. during the day and at night; concentrations of total astaxanthin, free astaxanthin, astaxanthin esters in <em>Arctodiaptomus</em> sp. at night and during the day; fatty acid concentration and the ratio of SAFAs (saturated fatty acids), MUFAs (monounsaturated fatty acids), and PUFAs in<em> Arctodiaptomus</em> sp. during the day and at night; the carotenoid concentrations in seston at different depth of Lake Heihai during the day and at night; the lutein concentration in seston under UVR and dark treatment; main characteristics of Lake Heihai; fatty acid concentrations and the ratio of SAFAs , MUFAs, and PUFAs of seston in Lake Heihai; the relative abundance of Chlorophytes with the size of greater than 5 μm and 0.2-5 μm in different layers of Lake Heihai; fatty acid concentrations of the calanoids in Fuxian Lake.</p>
Data for: Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine lakes on the Tibetan Plateau
<p>The primary objectives of the study were to: (i) determine OM quantity and quality in the sediments of alpine lakes on the Tibetan Plateau, (ii) identify the primary environmental regulators of OM quantity and quality, and (iii) reveal OM transformations in the water column and sediments.</p> <p>Firstly, we collected sediments and lake water from 20 lakes with diverse morphology and sizes across the entire Tibetan Plateau.</p> <p>Secondly, We analyzed the bulk sedimentary OM and two leachable pools of the sedimentary OM, i.e., water-soluble OM and alkaline-extracted OM combining elemental and stable isotopic analysis, optical measurements (i.e., absorbance and fluorescence spectroscopy), and ultrahigh-resolution molecular techniques (i.e., electrospray ionization-assisted Fourier transform-ion cyclotron resonance mass spectrometry, ESI FT-ICR MS). We also measured the lake water physicochemical characteristics (i.e., depth, transparency, salinity, turbidity, DO, pH, TN, TP and Chl a), as well as the composition of dissolved OM in water column via optical measurements.</p> <p>Thirdly, we performed statistical analysis to determine the primary environmental control and predictors of the spatial variability in sedimentary OM on the Tibetan Plateau. The statistical analysis included non-parametric Kruskal-Wallis with Dunn post hoc test, redundancy analysis, and linear regression models.</p> <p>Main results of the study are that (i) sedimentary water-soluble OM and alkaline-extracted OM were both dominated by low-molecular-weight, low-aromaticity compounds with low contributions of terrestrial humic substances, suggesting that sedimentary leachable OM was primarily regulated by in-lake sources and processes; (ii) water depth, water transparency, and total phosphorus concentration in water column explained ~50% variance of sedimentary bulk and leachable OM, substantiating the importance of autochthonous sources and primary productivity in regulating the quantity and quality of sedimentary OM; (iii) in comparison to lake surface water DOM, water-soluble OM and alkaline-extracted OM from sediments had higher proportions of terrestrial humic-like substances, suggesting preferential preservation of allochthonous materials in the sediments.</p>
Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie
<p>The datasets here were used to examine relationships between the overall productivity of Lake Erie and the commercial harvest of lake whitefish (<em>Coregonus clupeaformis</em>), walleye (<em>Sander vitreus</em>), and yellow perch (<em>Perca flavescens</em>) during 1915–2011. Here, we provide the two datasets used in the paper by Sinclair et al. titled "Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Productivity") provides the annual values of the five metrics used to develop the index of overall Lake Erie productivity. The second dataset ("Commercial harvest") provides the total annual commercial harvest (kg) of the three fish species, which were obtained from the Great Lakes Fishery Commission (<a href="http://www.glfc.org/great-lakes-databases.php">http://www.glfc.org/great-lakes-databases.php</a>). A summary and explanation of each variable is provided in the "Info" tab. Further information on how values were calculated (and transformed if necessary) is provided in either the info tab or the methods and supporting information of the associated article.</p>
Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie
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Littoral macroinvertebrate and water quality data for 32 lakes across the boreal-tundra transition in the Northwest Territories
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Data for: Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine lakes on the Tibetan Plateau
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Zooplankton and water quality data for 37 lakes in the Gwich'in and Inuvialuit regions of the Northwest Territories
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Data from: Effects of Nile tilapia (Oreochromis niloticus) cage aquaculture on water quality in the world’s largest tropical lake
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Lake water quality, chemistry and zooplankton composition for 16 lakes surrounding the Green Lakes Valley, 2016
This dataset contains water chemistry measurements made from 16 different lakes north and south of LTER site in the green lakes valley. At each visit water samples were collected for analysis of chlorophyll a and nutrient analysis and field measurements were recorded for pH, temperature, specific conductivity, dissolved oxygen (DO), % saturation, secchi depth, PAR. Secchi depth was measured using a 30cm disk to the nearest 0.25 meter and here recorded at the 0m row in the data file however it is a measurement of depth and so the units are meters. Most samples were collected between 0900 and 1300 MST. The first sampling date occurred shortly after lake had become ice free and then if the lake was at least 3 meters deep it was re-surveyed at approximately 2 week intervals. All data are collected from an inflatable raft at the point of deepest depth. All water samples were taken at the surface (0m), and the hypolimnion (about 2m above the lake sediment) using a Van Dorne sampler. Chlorophyll-a was extracted from water sample filters and absorbance was measured before and after acidification to quantify chlorophyll a concentration. Field measurements were conducted using a YSI either DO or multiple probe meter (YSI MPS 556) and a Li-Cor meter with a quantum sensor. Zooplankton samples were collected opportunistically 3 times throughout the summer, once immediately after ice-out and two more times with at least 14 days bewteen each subsequent visit. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). The Wisconsin net is 0.9m long with an opening of an inner diameter of 0.2m and a mesh size of 80um. The vertical tow samples are taken from approximately 0.5 m off the lake bottom. After the net is raised and the sides rinsed, the sample is transferred to a 250 mL jar and preserved in a solution of 80% Ethanol. Jars are labeled with date (yyyy-mm-dd), time hh:
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 2 and 3
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the second and third project years (2018, 2019), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the first (2017) year of EOMORES have also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2019<br> - Finland: links to repositories of EO data<br> - Italy CNR: sample of EO data 2018 and 2019<br> - Italy Trasimeno: sample of WISPstation in situ spectral data and water quality parameters data<br> - Lithuania Curonian Lagoon: sample of WISPstation in situ spectral data and satellite based water quality maps<br> - Netherlands Lake Markermeer: sample of EO data 2018 and 2019 (Chl, Secchi, SPM)<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: links to relevant repositories of in situ data</p> <p>http://eomores-h2020.eu</p>
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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.