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19,393 results for “water”
Lake Tahoe particle size distribution (PSD) data for discrete water samples
Particle size distribution data measured on discrete water samples from Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe Chlorophyll a concentration for discrete water sample
Chlorophyll a concentrations for discrete water samples taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
Historical and future Lake Surface Water Temperature for 80 major lakes in Southeast Asia [LSWT-SEA]
The present dataset is part of a study delving into the intricate relationship between lake surface temperature (LSWT) and the broader context of climate change in the ecologically diverse region of Southeast Asia (SEA). Recognizing LSWT as a highly responsive indicator of climatic shifts, the research aims to shed light on the region's vulnerability to these changes. Using a suite of predictive models (namely Multilinear Regression (MLR), Multilayer perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGB), Multilayer perceptron (MLP)) the study reconstructs historical LSWT trends from 1986 to 2020 and projects future scenarios until 2100, contingent upon various Representative Concentration Pathway (RCP) trajectories. Using MODIS-derived LSWT as predicted variable. The dataset package includes the data used to carry out the research: ECMWF ERA5 and CHIRPS climatic predicting variables, MODIS-derived daytime and nighttime LSWT, historically predicted daily daytime and nighttime LSWT, future predictions of LSWT for multiple Representative Concentration Pathways (RCPs), long term historical and future trends.
Hourly time series of Ives Lake (Huron Mountains, Marquette County, MI) Water Temperature-Depth Profiles, 2013-2022 (continuing study)
Long-term measurements of lake temperatures are essential to providing insights into local and regional changes in climate since large, still water bodies effectively act as a high-frequency filter. Ives Lake is a 30.7 m deep water body in northern Marquette County, in Michigan's Upper Peninsula. Beginning in 2013, temperature readings have been collected hourly from a string of twelve sensors located near the deepest point of the lake (approximately 46.84874 N lat, 87.84895 W long; identified by sonar survey in 2010 by first author). Measurements are continuing. The purpose of the project is to collect a data record of sufficient duration to determine if water temperatures are warming, and if the dates of autumn lake turnover are shifting toward later in the year.
Local water years for 4-digit hydrologic unit areas across the conterminous United States
Quantifying and predicting precipitation and water flow, and their influence on ecosystems is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the U.S. have limited considerations of areal variations in climate and hydrology, which need to be considered when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed a spatial interpolation technique to assign the start and end months of two LWY timeframes to 202 subregions across the conterminous U.S. that range from 4,384 to 134,755 km2. This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad-scale studies.
Seedling emergence and biomass data of nine dryland plant species characterizing the impact of soil residual auxin herbicide across two soil types and water pulse events on greenhouse growth; Las Cruces, New Mexico, Spring 2021.
Synthetic-auxin herbicides are often used to control woody plants and aid in grassland restoration. Seed-based restoration is common alongside herbicide applications and there may be unintended effects of these herbicides on dryland plant species at the seed and seedling stages. Additionally, abiotic conditions at the time of herbicide application may influence herbicide-soil-plant interactions. We conducted a greenhouse study to examine the effects of a common shrub-control herbicide mix and its interaction with soil type and a post-herbicide water pulse on common desert plant seeds and seedlings. In this greenhouse study, we found that a subset of species responded negatively to soil residual herbicide activity of a mixture of aminopyralid, clopyralid, and triclopyr at the seed and seedling stages. Species sensitive to soil herbicide residues were primarily shrub and forb species that are often the target species of herbicide applications for woody plant control, such as Prosopis glandulosa (honey mesquite) and Larrea tridentata (creosote bush). However, two shrub species (Atriplex canescens [four-wing saltbush] and Yucca elata [soaptree yucca]) and one perennial grass species (Digitaria californica [Arizona cottontop]), which are used in dryland restoration projects, were found to be particularly sensitive to soil residual herbicide activity. Thus, if using these herbicides to control woody plants and restore herbaceous vegetation via active seeding or relying on the in situ seed bank, considerations should be given to what species are used in the seed mix, what species are already present in the soil seed bank, and other details of the circumstances of herbicide application.
Kawe Gidaa-naanaagadawendaamin Manoomin Tribal-University Research Collaborative, University of Minnesota, Manoomin / Psiη (Wild Rice) Density Survey for Northern Minnesota and Wisconsin Waters
Wild Rice (Ojibwemowin: Manoomin; Dakodiapi: Psiŋ; Latin: Zizania palustris) abundance, harvest, and water level data, across the upper Great Lakes region collected by tribal organizations.
Water-soluble organic matter and nutrients from stormwater control measure and urban wetland soils
Water-soluble organic matter (WSOM) represents organic matter that has the potential to be readily released from soils. WSOM has been understudied in urban, engineered soils relative to natural soils. To understand the potential for organic matter and nutrient release, we extracted WSOM from the soils of stormwater control measures (SCM) and urban wetlands. In February 2022, we sampled soils from 20 SCMs and natural wetlands in the Rappahannock River watershed of the mid-Atlantic United States. The SCMs reflected a variety of design configurations including bioretention, rain gardens, wet ponds, and swales. We also sampled naturally occurring floodplain wetlands that are located in this urban watershed. Soils were sampled to a depth of approximately 40 cm. If there was standing water present in the SCMs and wetlands at the time of sampling, we also collected surface water samples. If present, grab samples of leaf litter or biomass were collected. Soil characteristics, such as pH, bulk density, soil moisture, soil organic matter, and cation exchange capacity were also determined for each site. WSOM was extracted from soils and biomass in the laboratory and analyzed for organic matter concentration (dissolved organic carbon) and composition (absorbance and fluorescence metrics), along with dissolved nutrient concentrations (total dissolved nitrogen, total dissolved phosphorus, nitrate, ammonium, and orthophosphate). In addition to the 20 sites in the Rappahannock watershed, soils from 2 additional bioretention SCMs on the Virginia Tech campus were sampled on a monthly basis from February 2022 to February 2023 to explore temporal variability in WSOM. To characterize changes in soil hydrologic conditions during monthly SCM sampling, we applied a Thornthwaite-type monthly water balance model. Finally, we performed a simple scaling exercise to WSOM results based on SCM area, sample depth, and soil bulk density to estimate potential SCM contributions of organic matter.
Time series of stable water isotopes (d18O, d2H) from Carvins Cove Reservoir in Southwestern Virginia, USA 2024-2025
Samples of stable water isotopes (delta 18O and delta 2H) were collected from surface waters and depth profiles in Carvins Cove Reservoir (Roanoke, Virginia, USA). Carvins Cove Reservoir is owned operated by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. Samples were collected approximately monthly at two sites along a primary tributary and depth profiles at multiple transects within Carvins Cove Reservoir from May 2024 - April 2025. Additional isotope samples were analyzed from precipitation collected at the Carvins Cove Reservoir dam in 2024. Samples were analyzed using cavity ringdown spectroscopy and reported as deviation of concentration from that of Vienna standard mean ocean water. An Rmarkdown file to visualize the dataset accompanies the package.
The Jefferson Project 2021 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 2021, 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 2021. These vertical profiler stations are named VP_AnthonysNose, 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.
The Jefferson Project 2021 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 2021, 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.
AquaMatch Chlorophyll a Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Chlorophyll a Data from Water Quality Portal ~1970-2024”, 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 chlorophyll a 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 can not 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 6, 1970, to June 20, 2024. 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 Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Research, 5
AquaMatch Dissolved Organic Carbon Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Dissolved Organic Carbon Data from Water Quality Portal ~1970-2024”, 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 dissolved organic carbon 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 5, 1970, to June 27, 2024. 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 Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water
Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022
Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Missouri reservoir water quality data (2022 - current) from the Statewide Lake Assessment Program (SLAP)
This dataset of limnological water quality data continues from North et al., 2025, starting in 2022 until present. The data is from reservoirs, primarily within the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: ammonium (NH4), anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, dissolved organic carbon, microcystin, nitrate & nitrite (NO3), pheophytin, particulate inorganic matter, particulate organic matter, phycocyanin, saxitoxin, Secchi disk depth, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids, and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete depths in the hypolimnion.
Continuous water quality measurements at Lindenwood Lake, Highlands Biological Station, Highlands, North Carolina, USA, 2022-2025
Measurements of turbidity, conductivity, dissolved oxygen, and water temperature were collected via an YSI EXO3 sonde in a 1.1 ha lake on the campus of Highlands Biological Station, Macon County, North Carolina. The sonde collects measurements every 15 minutes at a depth of ~0.5 m.
Daily Summary of Continuous water quality measurements at Lindenwood Lake, Highlands Biological Station, Highlands, North Carolina, USA, 2022-2025
Measurements of turbidity, conductivity, dissolved oxygen, and water temperature were collected via an YSI EXO3 sonde in a 1.1 ha lake on the campus of Highlands Biological Station, Macon County, North Carolina. The sonde collects measurements every 15 minutes at a depth of ~0.5 m.
Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 01, Highlands Biological Station, Highlands, NC, USA, 2022-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Betula alleghanensis and formerly Tsuga canadensis, the latter of which has mostly succombed to the Hemlock Woolly Adelgid.
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Allen Brain Atlas
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OpenNeuro
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