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484 results for “water quality”
The Jefferson Project 2022 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 2022, 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 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.
The Jefferson Project 2022 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 2022, 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 2022. These vertical profiler stations are named VP_HarrisBay and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less 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.
AquaMatch Total Suspended Solids from Water Quality Portal ~1970-2025
This dataset, “AquaMatch Total Suspended Solids Data from Water Quality Portal ~1970-2025”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat Total Suspended Solids ("TSS") dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 10, 1970, to April 9, 2025 from the conterminous US, Alaska, Hawaii, American Samoa, Puerto Rico, United States Virgin Islands, Guam, and Commonwealth of the Northern Mariana Islands. It contains 335,099 records from locations classified as estuaries; 411,979 records from locations classified as lakes, reservoirs, and impoundments; and 3,858,957 records from locations classified as streams. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National
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
Interagency Ecological Program: Discrete water quality monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2023
\<markdown\> The Interagency Ecological Program’s (IEP) Environmental Monitoring Program (EMP) was initiated in compliance with the Water Right Decision D-1379 (now mandated by Water Right Decision D-1641) and has monitored discrete water quality and nutrients in the upper San Francisco Estuary since 1975. The objectives of the EMP are to obtain consistent and accurate monthly data at established monitoring stations, provide and document information necessary to achieve compliance with salinity, flow, and dissolved oxygen standards, and to report this information for the purpose of management and conservation of the upper San Francisco Estuary. While the EMP also collects biological data, this dataset only includes the discrete water quality and nutrient data collected by the EMP from 1975-2021. Links to other EMP datasets can be found [here](https://emp-des.github.io/emp-reports/data-links.html) \</markdown\>
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Water Quality
These data describe annual estimates of wetland water quality, measured as the average duration of hypoxia (time dissolved oxygen concentration below 3 mg/l), collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
Interagency Ecological Program: Monitoring of water quality, phytoplankton, zooplankton, clams, and Delta Smelt to support the Summer-Fall Suisun Marsh Salinity Control Gates Action 2018-2024
The Suisun Marsh Salinity Control Gates (SMSCG) have the potential to increase low-salinity-zone habitat for endangered Delta Smelt (Hypomesus transpacificus, California Endangered Species Act listed as Endangered, Federal Endangered Species Act listed as Threatened), and to allow them to more frequently occupy Suisun Marsh, especially Montezuma Slough, one of their most important rearing habitats. Operation of the SMSCG in summer and fall to improve Delta Smelt habitat are called for in the Biological Opinion and Incidental Take permit for the Central Valley Project and State Water Project. To support the adaptive management of the action, the California Department of Water Resources and collaborating agencies monitored water quality, phytoplankton, zooplankton, clams, and fishes during the SMSCG management actions in 2018. Monitoring has continued during the summer and fall months of all subsequent years, including both those with and without actions. This data package includes data collected by the Interagency Ecological Program’s (IEP) long-term monitoring programs supplemented with targeted sample collection where existing surveys lacked spatial or temporal coverage. Monitoring during no-action years will be used as a baseline for comparison during action years. This data package will be updated annually.
LAGOS-NE-GEO v1.05: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-GEO v1.05, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NEGEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE-GEO v1.05 module includes information on the ecological context of the census lakes, all lakes > 4 ha in the study extent, their watersheds, and their regions. The information provided in the data tables for this module is organized into three main themes: CHAG - climate, hydrology, atmospheric deposition of nitrogen and sulfur, and surficial geology; LULC - land use/cover, impervious co
Water-quality monitoring in Tempe Town Lake, Tempe, Arizona, USA (2005-2021)
Constructed in 1997, the Tempe Town Lake is a small man-made reservoir that transforms a section of the typically-dry Salt River bed into a 224-acre lake in the heart of Tempe, Arizona. To accommodate the river when it flows, the lake features hydraulically-operated steel gates that allow water to pass through the system unimpeded. The lake has been a remarkable success as a community amenity and as a driver of economic growth in the area around the lake. The lake provides an ideal model system for the many artificial lakes constructed in arid-land cities owing to management decisions, such as draining, that affect their operation and ecology. At the same time, dramatic shifts in hydrology and chemistry when the lake is transformed to a flowing river and back into a lake during and after floods, provide opportunities to study the system's dynamic evolution to new limnological steady states. The CAP LTER has been measuring water quality, including temperature, pH, conductivity, and dissolved oxygen, dissolved organic carbon (DOC), and total dissolved nitrogen (TDN), in the lake since 2005.
Water Quality Data (Extensive) from the Taylor Slough, just outside Everglades National Park (FCE), from August 1998 to December 2006
Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). Starting in December 2006 for Sites SRS1d, SRS2, SRS3, and June 2007 for TS/Ph1a, TS/Ph2, and TS/Ph3 - the 3 day composite samples are now combined in a 2 liter bottle upon returning to the lab to form 6 day composite sample. Samples are retrieved every 3-4 weeks. Retrieval of the composite samples may result in a composite sample which is less than three or six days. The recorded date for each composite sample indicates the end date of the sample interval. Samples are analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples are filtered through a Whatman GF/F filters (0.7 um) immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC (TOC is no longer analyzed starting in August 2005 for all sites). We use these montly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with a YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwat
Institutional Dimensions of Restoring Everglades Water Quality -Interview Notes (FCE), September 2014-July 2015
These data were compiled through semi-structured and open-ended interviews as part of the Institutional Dimensions of Restoring Everglades Water Quality research project. The notes represent responses from farmers, agricultural extension agents, state and federal government officials, private water consultants, and nonprofit officials involved in the implementation of the Everglades Forever Act regulations. These mandate that farms in the Everglades Agricultural Area implement best management practices to reduce phosphorus enrichment. Since the regulations started in 1994, water quality has steadily improved. The research sought to explain why the regulations have been effective.
Water quality monitoring on the Altamaha River and major tributaries from September 2000 through November 2001
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through November 2001. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate) and dissolved organics (DOC, DON, DOP) were measured using standard methods. The concentrations of 6 elements (Ca, K, Mg, Na, Si, and Sr) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS).
Long-term water quality monitoring in the Altamaha, Doboy and Sapelo sounds and the Duplin River near Sapelo Island, Georgia from May 2001 to August 2009
Water samples were collected on Georgia Coastal Ecosystems LTER oceanographic monitoring cuirses approximately every three months from May 2001 through December 2006 and monthly from November 2006 to August 2009. Concentrations of dissolved nutrients (ammonium, nitrate+nitrite, phosphate), dissolved organics (DOC, DON, DOP), particulate organic carbon and nitrogen, total suspended sediment, and total particulate iron and phosphorus were measured using standard analytical methods. The concentrations of six elements (calcium, potassium, magnesium, sodium, silicon, and strontium) were also determined occasionally using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS).
Long-term water quality monitoring on the Altamaha River and major tributaries from September 2000 through April 2009
Water samples were collected from the Altamaha River (approximately weekly) and several tributaries (bimonthly) from September 2000 through September 2001. Samples were then collected at less frequent intervals from September 2001 through April 2009. The concentration of dissolved nutrients (ammonium, nitrate+nitrite, phosphate, silicate), dissolved organics (DOC, DON, DOP) and total suspended solids were measured using standard methods. The concentrations of 20 elements (Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sr and Zn) were also determined using elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS). Total dissolved inorganic carbon (DIC) was measured using a custom automated DIC analyzer. Total alkalinity (TA)was determined by Gran titration and pH of surface water was measured using a glass electrode.
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.
Minneapolis-St. Paul Metro Area Lakes Surface Water Quality Characteristics
Urban lakes are heavily impacted by human activities and climate variability, and they provide many ecosystem services to residents. The MSP LTER program is studying long term changes in urban lake water quality, ecology and management as part of our long term studies of urban environments. The goal of this dataset is to understand how land-use change, management, and climate have impacted urban lake biogeochemistry over time. This dataset includes parameters characterizing the long term (> 5 years) surface water quality and chemistry of 294 lakes and ponds in the Minneapolis-Saint Paul Seven County Metropolitan Area, Minnesota, USA. The dataset draws from data publicly available through the Minnesota Pollution Control Agency and data provided by individual agencies, park districts and cities. The dataset is distinct from other lake datasets because it is curated to only report a single value per lake x date x parameter, minimizing the amount of data manipulation needed before use in statistical analyses. All data come from the top two meters of the water column. In the case of multiple spatial measurements on a single lake or multiple agencies sampling the same lake on the same day, chemistry data were averaged to generate a single value. For Secchi data, the deepest reported observation on a given lake x date was used. Parameters: total phosphorus, total nitrogen, total Kjeldahl nitrogen, nitrate, nitrite, nitrate + nitrite (NOx), ammonium, chlorophyll a (corrected and not corrected for pheophytin), specific conductivity, chloride, and Secchi depth. These waterbodies are identified by their DNR Division of Water (DOW) number with minor alterations for subbasin identification. This dataset does not comprehensively represent all lentic waterbodies that have substantial water quality data in the metro area, and some included waterbodies may be considered wetlands according to state classifications. The data brought together in this database has undergone QAQC by the
Lake Mendota long term water quality model
The data are associated with the following manuscript: Hanson, P. C., Ladwig, R., Buelo, C., Albright, E. A., Delany, A. D., & Carey, C. (2023). Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake. Lake water and ice observational data and lake bathymetry are from the North Temperate Lakes Long Term Ecological Research program. Brief abstract of the work: To investigate how water quality in Lake Mendota might respond to nutrient pollution reduction, we used computer models to simulate the elimination of phosphorus inputs from the catchment and track water quality change. The data herein are used to drive and calibrate the model. In addition, model code and simulation output are included as "other entities."
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
North Temperate Lakes LTER: Multiparameter Water Quality Data -- CFL Pier, Lake Mendota.
This is data from a SUNA V2 nitrate sensor and a YSI EXO 2 sonde instrumented with water temperature, dissolved oxygen, pH, chlorophyll, phycocyanin, conductivity, turbidity, and fDOM sensors. The sensors are located at the lake end of the pier serving the Center for Limnology on the UW-Madison campus. The YSI sonde is fixed on the pier with sensors nominally at 0.5 meters depth, while the SUNA is suspended below the pier at one meter depth in the open water season. In the winter season, the SUNA is placed in a cage on the lake bottom close to shore with the sensor 18cm off the bottom. The depth of the sensors will vary with lake level over the season. The water depth at the lake end of the pier is normally about 3 meters. YSI sonde data are sampled once per minute. Hourly and daily averages are provided as separate CSV files. The SUNA sample rate varies. In the winter (under the ice) it relies on single battery charge, so the wiper is deactivated and the sensor samples every 1-2 hours. Daily averages of SUNA are also provided as a separate CSV. The YSI sonde is deployed only during the ice-free season coinciding with the placement of the pier. Sensors are cleaned and maintained roughly every two weeks. Number of sites: 1. Location lat/long: 43.07758, -89.40297
PIE LTER, Year 2018, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts
Year 2018, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.
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OpenNeuro
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