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484 results for “water quality”
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 Western Water Treatment Plant (Melbourne, Australia) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Western Water Treatment Plant (Melbourne, 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), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2. 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).
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).
Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks
<pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>
Mitigating ecosystem service tradeoffs in rangelands by using grazing duration and timing to manage water quality
<p>1. Mitigating ecosystem service (ES) tradeoffs is a key management goal in locations where stakeholders value different and potentially conflicting ecosystem services (ESs). However, studies are not often designed to examine how local management actions address ES tradeoffs, and therefore do not provide options that can alleviate conflict.</p> <p>2. In semi-arid rangelands, we examined the potential for managers to mitigate tradeoffs between livestock production and water quality. To move away from solutions that offer cattle removal as a singular management strategy, we examined how cattle presence, plus two elements of rotational grazing - the length of time cattle spend on rangeland (i.e., duration), and the season grazed (i.e., timing), affected stream Escherichia coli (E. coli concentrations). We also modeled how grazing duration and timing affected the ability to meet regulatory benchmarks for water quality throughout a grazing season.</p> <p>3. Grazing duration controlled the length of time E. coli concentrations were high in streams. In short- and medium-duration systems, E. coli concentrations were high for shorter periods of time than in long-duration systems, resulting in fewer violations of national and state water quality standards.</p> <p>4. Stream E. coli concentrations showed a consistent seasonal pattern, starting low in spring, peaking in summer, and declining towards fall. Thus, grazing during spring or fall, rather than in summer, reduced the number of days that E. coli levels exceeded water quality standards.</p> <p>5. Our results suggest that reducing the grazing duration and shifting its timing are complementary strategies that can mitigate the tradeoffs between livestock grazing and water quality without fencing-off riparian areas or removing cattle from pastures with streams.</p> <p>6. Synthesis and applications. In this study, we found grazing duration and timing can be used as tools to mitigate ES tradeoffs between cattle production and water quality in rangeland streams. Shorter grazing durations reduced the number of days <i>E. coli</i> levels were above regulatory limits, as did grazing that occurred either early or late in the season. These results support the idea that rotational grazing can be an effective strategy to manage water quality in semi-arid rangelands. They also highlight the need for more grazing studies that incorporate gradients of duration and timing into study designs.</p>
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
Data: Water quality and the biodegradability of dissolved organic carbon in drained boreal peatland under different forest harvesting intensities
<p><span>This repository consists of three files, which contain ground water and ditch water quality data in drained forested peatlands and dissolved organic carbon biodegradation to carbon dioxide. Experiments and results are presented in:</span></p> <p><span>Palviainen M., Peltomaa E., Laurén A., Kinnunen N., Ojala A., Berninger F., Zhu X., Pumpanen J. 2022. Water quality and the biodegradability of dissolved organic carbon in drained boreal peatland under different forest harvesting intensities. Science of the Total Environment 806: 150919 <a href="https://doi.org/10.1016/j.scitotenv.2021.150919">https://doi.org/10.1016/j.scitotenv.2021.150919</a>.</span></p> <p><span> </span></p>
Bio-physicochemical Parameters of Water Quality Analysis in Dass LGA, Northeastern Nigeria
<p>The data consist of 20 groundwater samples collected randomly in Dass LGA for water quality assessment. The data has 14 biophysicochemical parameters including:<br>1.pH = The pH value of the samples</p> <p>2. Temp = Temperature of the samples</p> <p>3. TBD = Turbidity of the samples</p> <p>4. Colr = Colour of the water</p> <p>5. TDS = Total Dissolved Solids</p> <p>6. EC = Electrical Conductivity</p> <p>7. TH = Total Hardness</p> <p>8. Al- = Aluminum</p> <p>9. F- = Fluoride</p> <p>10. Fe- = Iron</p> <p>11. NO3 = Nitrate</p> <p>12. Mn = Manganese</p> <p>13. TCC = Total Coliform Count</p> <p>14. FCC = Fecal Coliform Count </p> <p>Other parametrs recorded include Location (Lat/Lon), Name of community, Sample Name, Altitude, Depth, Source of pollution, Distance to pollution source </p>
Fig 1 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia
Fig 1: Location of the experimental side, Jodamu, Cuttack, Odisha
Water Quality parameters in Odesa, Ukraine from February to May 2024
<p>The TOPCOAT Data.xlsx file contains parameters of seawater at the Odesa coast of the Black Sea as two sheets:</p> <p>A. WISP-3 - data measured by WISP-3 instrument</p> <ol> <li>Chl - Chlorophyll-a in μg/l</li> <li>TSS - Total Suspended Matter in mg/l</li> <li>Kd - Transparency in 1/m</li> <li>CPC - Phycocyanin in μg/l</li> </ol> <p>B. Values for the phytoplankton community</p> <ol> <li>N - Abundance in 10^6/m^3</li> <li>B - Wet Biomass in mg/m^3</li> <li>Bc - Carbon Biomass in mg/m^3</li> <li>S - Surface Area in µm^2/m^3</li> <li>S/W - Specific Surface in m^2/kg</li> <li>SI - Surface Index in 1/m</li> </ol> <p>S, S/W, and SI were calculated by (Minicheva H.G., Zotov A.B., Kosenko M.N., 2003. Methodological recommendations for determining the complex of morpho-functional indicators for unicellular and multicellular forms of aquatic vegetation. Odesa, 37 рр.)</p> <p>Both sheets also contain the name of the Station together with its latitude and longitude, and the date when samples were collected. The WISP-3 sheet additionally contains Time (UTC), Water Temperature (°C) and Salinity (‰).</p> <p> </p>
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.
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022. in Zooplankton as an indicator of water quality in the Varvara Reservoir
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022.
Urban stormwater pond water quality and vegetation cover data
<p>These data include measurements of water physical and chemical parameters and littoral vegetation measurements in urban stormwater ponds located in three different urban land use types in Orlando, Florida, USA.</p>
Figure 2 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 2. Classification of stations based on similarities of macroinvertebrate communities.
Figure 1 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 1. Study area and stations.
Figure 1 in Macroinvertebrate-based biotic indices for evaluating the water quality of Kargı Stream (Antalya, Turkey)
Figure 1. Study area and stations.
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>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.