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484 results for “water quality”
The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2018 water quality data from two vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2018, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.
The Jefferson Project 2019 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
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).
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
High-Frequency and Water Quality Monitoring Data of Long Pond at Grafton Lakes State Park, New York, United States, 2024
This collection of datasets contains high frequency data captured through Hobo, Minidot, and water level sensors, as well as data collected from manual sampling days. Long Pond is located in Grafton New York, USA named for its long shape and shallower depth (max depth is around 8 meters). Using a buoy, sensors were attached to a rope at the deepest point discoverable and deployed. Data covers all information recorded from 2024-04-30 to 2024-10-09. Times are recorded in Eastern Standard Time. Temperature Readings were taken every 10 minutes at 1 meter intervals by both Minidots and hobo sensors (1.52-7.52 m). Dissolved Oxygen was similarly collected every 10 minutes by the Minidots at depths 1.52, 6.52, and 7.52 meters. Manual measurements (YSI and Secchi disk) were recorded on sampling days, as well as water samples that were assessed for water quality parameters from the top and bottom of the lake. Water level data was also collected in 12 hour intervals.
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.
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
Longitudinal water quality sampling of King's Creek (KS) and Caribou Creek (AL), 2022 and 2023
We sampled Caribou Creek in July 2022 and King's Creek in August 2022 and May 2023 longitudinally for dissolved carbon dioxide using a headspace equilibrium method as well as other water quality parameters. The data was used to inform a stream network model to model carbon dioxide across the stream networks. The data package is complete.
Indicators of Contaminant Sources, PFAS, and Water Quality in Ellerbe Creek and New Hope Creek, NC (2019-2022)
Thousands of chemical contaminants are found in urban stream globally. This is a dataset of water quality measures of (1) compounds that are indicative of specific contaminant sources, (2) common water quality measures [trace metals, major ions, nutrients], and (3) PFAS. Sampling was conducted in Ellerbe Creek and New Hope Creek in the Durham and Orange counties of North Carolina. Biweekly and synoptic sampling was undertaken to explore spatial and temporal variation in water concentrations.
Clear Lake water quality monitoring data from 2019 to 2023 by the University of California, Davis
A major barrier to effective water quality restoration at Clear Lake is the absence of quantitative data on the anticipated response to restoration projects. In-lake monitoring (in-situ measurements) is needed to understand better the processes contributing to poor water quality. This data package contains the in-situ measurements collected by the University of California, Davis at Clear Lake between 2019 and 2023, which include: continous stream properties at three locations (Middle, Scott, and Kelsey Creeks); continuous meteorological variables at seven locations around the perimeter of the lake; continuous lake temperature and dissolved oxygen at multiple depths and locations across the lake (six permanent water quality stations); continuous lake surface temperature in the shoreline; and discreate samples to measure nutrient concentrations and phytoplankton biovolumes and species identification throughout the water column and across all three lake basins every 6-8 weeks.
Middle Rio Grande New Mexico, Bernalillo 550 Bridge, Water Quality Daily Means 2010-2018
This data package includes daily mean data for streamflow, turbidity, light, and gross primary production for a site on the Middle Rio Grande located at the Bernalillo 550 Bridge in Bernalillo New Mexico, USA, from 2010-01-01 to 2018-12-31.
Ultra-high frequency water quality sonde data from Lake George and Chautauqua Lake, NY, 2021
Forecasting rapid ecological change represents a major challenge in environmental science. In aquatic ecosystems, the shift from clearwater conditions to an algal bloom-dominated state represents an important transition resulting in harmful algal blooms (HABs). Methods for forecasting HABs using sensors are problematic, as lower frequency sampling can miss early warning indicators. Here, using sensor data from two lakes we show that antecedent information essential to forecasting HABs was best characterized using ultra-high frequencies (UHF; sampling ≤ 1 second). This publication is comprised of four individual datasets, each containing water quality measurements sampled at 4 Hz using an EXO2 sonde from two lakes in New York State, USA (Lake George and Chautauqua Lake). An accompanying R script is provided to read in each dataset, format it, and undertake a variety of analyses to demonstrate the utility of UHF data in identifying subtle environmental changes linked to HABs. The first dataset, "CHQ_StaticEXO.csv," includes UHF data from a stationary EXO2 sonde deployed in the South Basin of Chautauqua Lake, NY. This dataset spans a 24-hour period and is utilized to examine the behavior of different water quality sensor technologies during the rapid onset of a HAB. The second and third datasets, "LG_HarrisBayVP.csv" and "LG_HarrisBayVP_Cal.csv" were collected using a vertical profiler in Lake George, NY. These datasets provide UHF phycocyanin fluorescence data from a profiling EXO2 sonde over a two-month period, and are used to demonstrate how antecedent information critical for forecasting HABs is best characterized using UHF frequencies. Lastly, the dataset "GloeotrichiaExp.csv" was gathered as part of a laboratory experiment measuring cultured Gloeotrichia sp. colonies. As with the Lake George datasets, this dataset includes phycocyanin fluorescence data from an EXO2 sonde collected at 4 Hz, and is used to help explain some of the trends observed in the previous th
Sacramento-San Joaquin Bay-Delta Continuous (15 minute) Water Quality Monitoring: South Delta Region, collected by the North Central Region Office, DWR, 1999 – ongoing
The Department of Water Resources (DWR) Water Quality Evaluation Section (WQES) provides technical expertise and program support for regulatory compliance, water operations, emergency response, and environmental restoration. Wireless telemetry is used to transmit real-time provisional data to the California Data Exchange Center (CDEC), making the data publicly available. The published dataset is quality controlled and quality assured providing detailed information at 15-minute intervals from 18 monitoring stations, using Xylem’s YSI EXO2 and YSI 6600 multiparameter sondes to document individual water quality measurements of multiple water quality parameters. The dataset informs operations for the California State Water Project and supports water quality monitoring required by Water Right Decision D-1641, the Delta Compliance Program, the South Delta Temporary Barriers and the South Delta Improvement Program. It is important to note that the start dates and subsequent equipment upgrades vary between stations and equipment leading to discrepancies in the dataset’s date ranges.
Interagency Ecological Program: Fish catch and water quality data from the Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2024.
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. The YBFMP operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. Only juvenile and adult fish catch with associated water quality are presented in this dataset. The rotary screw trap sampling objectives are to: (1) examine species abundance and life stage of juvenile outmigrants and resident small-bodied fishes, (2) identify temporal and spatial patterns in fish abundance and species composition, and (3) examine the effect of physical and environmental conditions on these patterns. The fyke trap sampling objectives are to: (1) examine abundance of migrating and resident adult fishes, (2) identify temporal and spatial patterns in fish abundance and species composition, especially with regard to anadromous species, (3) examine the effect of physical and environmental conditions on these patterns, and (4) provide data on the timing and duration of species captured in the Yolo Bypass for comparison to those captured in other Sacramento Valley tributaries. The beach seine surveys are conducted in the Yolo Bypass’s perennial channel (Toe Drain), inundated floodplain, disconnected inundated ponds, and perennial ponds. The objectives o
Interagency Ecological Program: Zooplankton catch and water quality data from the Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2018
Largely supported by the Interagency Ecological Program (IEP), the California Department of Water Resources (DWR) has operated a fisheries and invertebrate monitoring program in the Yolo Bypass since 1998. The main objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to collect baseline data on lower trophic levels (phytoplankton, zooplankton and insect drift), juvenile and adult fish, hydrology, and water quality parameters. As the Yolo Bypass has been identified as a high restoration priority by numerous regulatory agencies, these baseline data are critical for evaluating success of future restoration projects. In addition, the data have already served to increase our understanding of the role of the Yolo Bypass in the life history of native fishes, and its ecological function in the San Francisco Estuary. Zooplankton are an important component in the diet of larval, juvenile, and small adult fishes within the San Francisco Estuary, including Delta Smelt, juvenile Chinook Salmon, Striped Bass, and Sacramento Splittail. The YBFMP collects zooplankton year-round from two sites. Since 2011, samples have been collected biweekly (every other week) to weekly (during floodplain inundation) using 150- and 50- micrometer mesh plankton nets. Zooplankton are identified and enumerated by contractors (currently BSA Environmental Services). The goals of the zooplankton monitoring program are to compare the seasonal variation in species densities and trends between (1) the Sacramento River channel, and (2) the Yolo Bypass, the river’s seasonal floodplain. Data on zooplankton catch and associated water quality parameters are presented in this dataset.
Interagency Ecological Program: Water quality, fish, and zooplankton monitoring and modeling to support the 2018 Suisun Marsh Salinity Control Gates Summer Action
In summer 2018 we used a unique water control structure in the San Francisco Estuary (SFE) to direct a managed flow pulse into Suisun Marsh, one of the largest contiguous tidal marshes on the west coast of the United States. The action was designed to increase habitat suitability for the endangered Delta Smelt Hypomesus transpacificus, a small osmerid fish endemic to the upper SFE. The approach was to operate the Suisun Marsh Salinity Control Gates (SMSCG) in conjunction with increased Sacramento River tributary inflow to direct an estimated 160 x 10^6 m3 pulse of low salinity water into Suisun Marsh during August, a critical time period for juvenile Delta Smelt rearing. This dataset includes physical and biological monitoring data collected for the action. Datasets include Delta Smelt catch from the USFWS Enhanced Delta Smelt Monitoring program, zooplankton and Microcystis abundance from the Environmental Monitoring Program, historic Delta Smelt catch from the Summer Townet Survey, Delta Outflow from the Dayflow model, extent of Delta Smelt habitat from the UnTRIM Bay-Delta model, and water quality (Salinity, Temperature, Chlorophyll, and Turibidity) collected at continuous sondes at three locations. These data are associated with the manuscript "Evaluation of a large-scale flow manipulation to the upper San Francisco Estuary: Response of habitat conditions for an endangered native fish," by Dr. Ted Sommer, et al. 2020 PLOS One, in review.
Six decades (1959-2022) of water quality in the upper San Francisco Estuary: an integrated database of 16 discrete monitoring surveys in the Sacramento San Joaquin Delta, Suisun Bay, Suisun Marsh, and San Francisco Bay
The upper San Francisco Estuary (SFE) is simultaneously a central hub of water delivery in California and home to commercially important and endangered fishes, such as Chinook Salmon, Green Sturgeon, and Delta and Longfin Smelt. Extensive ecological monitoring has been conducted for over 50 years, mainly under the auspices of the Interagency Ecological Program for the San Francisco Estuary (https://iep.ca.gov/). We integrated water quality data from 16 boat-based long-term monitoring surveys in the upper SFE. This integrated dataset includes measurements of temperature (surface and bottom), conductivity (surface), salinity (surface), Secchi depth, qualitative concentration of the toxic alga Microcystis (surface), Chlorophyll-a concentration (surface), nutrients (surface), and other parameters from 1959 - 2022. The component surveys range in sampling frequency from thrice weekly to monthly and range in duration from 5 – 60 years. Most component surveys sample at fixed stations, but the Enhanced Delta Smelt Monitoring survey uses random sites and some stations (with “EZ” in the station name) of the Environmental Monitoring Program follow the salinity field. It is highly recommended to inspect the documentation of the component surveys for more information on their methods.
Interagency Ecological Program: Drift invertebrate and ichthyoplankton catch and water quality from the Sacramento River channel, and Sacramento River floodplain and tidal slough, collected by the Yolo Bypass Fish Monitoring Program, 1998-2022
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. Aquatic and terrestrial insects are an important component in the diet of juvenile and adult fishes within the San Francisco Estuary, including two important native fishes: juvenile Chinook Salmon and Sacramento Splittail. The YBFMP collects drift invertebrates year-round from two sites. Currently, samples are collected biweekly (every other week) to weekly (during floodplain inundation) using a rectangular aquatic drift net that sits at the surface of the water. Invertebrates are identified and enumerated by contractors (currently EcoAnalysts, Inc.). The goals of the monitoring program are to compare the seasonal variations in densities and species trends of aquatic and terrestrial insects/non-insects within the Sacramento River channel and the Yolo Bypass, the river’s seasonal floodplain. Drift invertebrate Key findings to date include: (1) Chinook Salmon sampled in the floodplain had diets comprised of 90% Dipterans and zooplankton, with Chironomidae being the dominant Diptera family (Sommer et al., 2001), (2) The floodplain of the Yolo Bypass contains significantly higher densities of Diptera (Diptera densities being positively associated with flow) and terrestrial invertebrates than the adjacent Sacramento River (Sommer et al. 2001b: Sommer
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International Brain Laboratory public data
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OpenNeuro
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