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25 results for “stream water quality”
Water quality and watershed attributes of 41 Pampean streams in Argentina, 12 years later (2003-2015).
This database consists of water chemistry (pH, conductivity, dissolved oxygen, nutrients, and carbonates) and catchment attributes for 41 streams of Buenos Aires province, Argentina. Water quality was measured in 2003/4 and 12 years later (2015/16). Sampling were made in May (autumn), November (spring), and February (summer) at baseflow condition. Some physico-chemical parameters were measured in situ. Parameters determined at laboratory were nutrients and salts. And catchment attributes were determined (physiographic parameters, land use, soil type and geology).
Water quality in restored urban streams in Lexington, KY, USA
Stream-water grab samples were collected periodically from sampling locations upstream and downstream of restored stream reaches in Lexington, KY, USA, and analyzed for a suite of water quality characteristics including nitrate, cations, and pH. Study sites included streams receiving riparian reforestation or other conservation, as well as streams restored using a natural channel design approach. The goal of this sampling program was to evaluate to what extent stream restoration interventions can influence stream-water quality in an urban context.
Mohonk Preserve Stream Water Quality Invasive Species and Macroinvertebrate Sampling in from 2017-Present
The mission of the Mohonk Preserve is to protect the Shawangunk Mountains region and inspire people to care for, enjoy, and explore their natural world. Among these 8,000 acres are the vernal pools, permanent springs, tributaries, Humpo Marsh, and the Humpo Kill, and parts of the Kleine Kill and Coxing Kill watersheds within the Hudson River Drainage Basin. Not only are the areas around the Shawangunks established habitats for New York State (NYS) protected species, including an Audubon-designated Important Bird Area, but the watershed also encapsulates more than one agricultural land use area, as well as Rondout Creek, which is an important waterway for the New York City water supply. A conservation plan must be implemented in these areas in particular, keeping in line with the Mohonk Preserves goal to conserve the Shawangunk region for both humans and the greater ecosystem within it. Recognizing the immediate and long-term conservation needs of the streams in this region by employing volunteer data collection will be a catalyst to the Preserves understanding of which environmental threats of this area should be prioritized. The StreamWatch citizen science program will be the newest addition to an array of volunteer research areas, which include collection of weather data, phenology observations, monitoring of peregrine falcon breeding activities, and monitoring of fall hawk migration. Using concise stream monitoring protocol designed for volunteer safety and maximum data accuracy, StreamWatch will evaluate water quality using an array of parameters. Following thorough observation and assessment (which will include analyzing appearance and smell of the water, shape of the stream, canopy cover, nearby land uses, recent weather, and presence of riparian vegetation including invasive species) water quality will be evaluated by means of temperature, dissolved oxygen, pH, and turbidity measurements, in addition to a macroinvertebrate count. Width and depth will also be
ASW01 Stream water quality at the flumes on watersheds N04D and N02B and at the Shane Creek crossing on watershed SA at Konza Prairie
Turbidity, dissolved oxygen, conductivity, temperature, and pH are measured on streams draining catchments with 2-year (N02B), 4-year (N04D), and rotational (SA, SB, and SC) burns. Measurements are taken at 10 minute intervals upstream of the flume or crossing. Water quality parameters are measured using Yellow Springs Instruments (YSI) multiparameter water quality sondes model 6600 or 6920. Turbidity and dissolved oxygen are measured using YSI 6136 optical turbidity probe and YSI 6150 ROX optical dissolved oxygen probe. Conductivity, temperature, and pH are measured with the YSI 6560 temperature and conductivity sensor. The prairie streams are 3rd-order and are intermittent. The period of record started in October, 2008 and missing records are explained by the YSI sondes being removed for data download or the stream being dry or frozen.
Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.
Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).
High-frequency water quality data for three urban streams in Boston, MA (USA), 2021-2022
This dataset contains high-frequency water quality data for three urban stream locations in the great Boston, Massachusetts metropolitan area. Multiparameter sondes with sensors to measure temperature, pH, specific conductivity, optical dissolved oxygen (DO), turbidity, colored dissolved organic matter (CDOM), and optical brighteners (OB) were deployed from 23 November 2021 to 20 December 2022. Data were collected at 15-minute intervals.
Water quality dataset from stream water in the Khibiny massif, Kola Peninsula (Russia) in August 2017
<p>The dataset includes base chemistry, elemental concentrations, and sulfur isotope (δ34S_SO4) measurements from 11 stream water sampling locations.</p> <p>The measurements were taken in the Belaya and Vuonnemiok stream systems within the Khibiny massif (Kola Peninsula, Russia) during 25-30th of August 2017 and were analyzed at laboratories at Stockholm University, Sweden.</p> <p>The data was gathered to investigate potential pollution spreading in hydrological pathways from active apatite mining within the catchments, and it can be used for comparison with other mining-impacted sites in the Arctic.</p>
Figure 1 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 1. Satellite view of the area and water sampling points of Demétrio stream: point 1 source, point 2 and point 3, upstream near the area with the highest urban density and from the downstream near the meeting point of the Demétrio stream with the Gravataí River.
Figure 4 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 4. Integrated analysis of physicochemical and microbiological factors (PCA) of three water samples of Demétrio stream: point 1, point 2 and point 3. Component 1, with 78.9% affinity, separates component 2 with 21.1% affinity.
Figure 3 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 3. Al, Fe, Mn and Cu analysis of three water samples of Demétrio stream: point 1 (P1), point 2 (P2) and point 3 (P3).
High-frequency time series of stage height, stream discharge, and water quality (specific conductivity, dissolved oxygen, pH, temperature, turbidity) for Stroubles Creek in Blacksburg, Virginia, USA 2013-2018
A pressure transducer (CS450, Campbell Scientific Inc., Logan, UT, USA) and a multi-parameter water quality sonde (YSI Series 6:6920 v2, YSI Inc., Yellow Springs, OH, USA) were deployed by the Virginia Tech Biological Systems Engineering (BSE) Stream Research, Education, and Management (StREAM) Lab (vtstreamlab.weebly.com/) from January 2013 to December 2018 to assess stage and water quality, and calculate discharge in Stroubles Creek, Blacksburg, VA (4118326.215 N, 549236.02 W). Stroubles Creek is a 19 km tributary of the New River in Blacksburg, Virginia, USA. Stroubles Creek begins immediately below the Virginia Tech campus and flows north into the Kanawha River in the Valley and Ridge physiographic province. Stroubles Creek was a historic water supply for the town of Blacksburg and remains a highly visible waterway in the town, as it traverses both the downtown area and much of the Virginia Tech campus. In 2002, Stroubles Creek was placed on Virginia’s 303(d) list of impaired water bodies with a benthic impairment by the Virginia Department of Environmental Quality (VADEQ). A total maximum daily load (TMDL) plan was subsequently developed in 2003 for the stream in collaboration with Virginia Tech’s BSE department, the Virginia Department of Environmental Quality, and the Virginia Department of Conservation and Recreation. During 2008-2010, a 2.1 km reach of Stroubles Creek, mostly upstream of the StREAM Lab, was restored by means of cattle exclusion, natural revegetation, bank reshaping, and creating an inset floodplain. The water quality dataset provided here is associated with the upper Stroubles Creek watershed which drains approximately 14.5 km2. Currently, data from the StREAM Lab are used by 8-10 faculty and their graduate students in 7 departments across 5 colleges at Virginia Tech for research and at least 16 classes for field labs and/or assignments.
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>
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>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
<p>Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.</p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
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Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
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Water quality and spatial parameters from the main channel of a 6th order stream collected with an uncrewed surface vehicle
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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)
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DANDI Archive for NWB datasets
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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.