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79 results for “High-Frequency data”
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Time series of high-frequency meteorological data at Carvins Cove Reservoir, Virginia, USA 2021-2025
This dataset consists of variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Carvins Cove Reservoir. Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.36944, -79.95778), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetically active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every minute from 2021-03-29 19:00:00 to the end of the dataset at 2025-12-31 23:59:00, except for periods of maintenance. We applied extensive quality assurance/quality control protocols to the observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
Lake Sunapee Instrumented Buoy: High-Frequency Weather Data, 2007-2022
The Lake Sunapee/Global Lake Ecological Observatory Network (GLEON) instrumented buoy, operated by the Lake Sunapee Protective Association (LSPA www.lakesunapee.org), is equipped with meteorologic (weather) sensors. The Lake Sunapee buoy is located near Loon Island Lighthouse (43.391°N, 72.058°W) during the summer months and in the Lake Sunapee Harbor (43.386°N, 72.081°W) (2010-current) from late fall to spring. During the first few years of data collection (2007-2010), the buoy was located near Loon Island year-round. The meteorological sensors include: a LI-COR LI-190R Quantum Photosynthetically Active Radiation (PAR) sensor (May 2018 - present), an air temperature sensor (HMP50 Vaisala, 2010-present), and a wind speed/direction sensor (WXT511 Vaisala, 2009-present) sensor. These sensors are located approximately 1.7 meters above the water's surface. The buoy is also equipped with below-water surface sensors comprised of water temperature thermistors and dissolved oxygen sensors, and as of 2021, a YSI EXO multiparameter sonde. The data from the below water sensors are available in a separate dataset (EDI data package edi.499). All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency meteorological data at Falling Creek Reservoir, Virginia, USA 2015-2025
This dataset consists of meteorological variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Falling Creek Reservoir (37.3025, -79.83667). Falling Creek Reservoir (Vinton, Virginia, USA), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetic active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every 5 minutes from 2015-07-07 15:45:00 to 2015-07-13 11:28:00 (YYYY-MM-DD hh:mm:ss) and every minute thereafter to the end of the dataset at 2025-12-31 23:59:00. We applied substantial Quality Assurance/Quality Control (QA/QC) protocols to the raw observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and QA/QC analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
EMS - Automated High-Frequency Methane Data
Automated high-frequency methane (CH4) in ambient air measurements were made at the Harvard Forest (HF) research site since 1992. The proximity and the relative location of the site to numerous industrial/urban areas presents the opportunity to sample air flows that have been influenced by known CH4 sources on a regular and repeatable basis and to characterize the atmospheric chemical signature of the sampling location and assess its sensitivity to both local and regional sources at different time scales.
Otsego Lake (NY, USA) high-frequency buoy data 2017-2021
Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org).Yokota Lab, SUNY Oneonta BIological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2017 to 2021. Additional support for this project has been provided by New York State Water Research Institute, SUNY Oneonta Faculty Research Grant, and NSF awards to the Global Ecological Lake Observatory Network (GLEON.org).
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
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.
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Northern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>three of the six paths of the northern HOME tomography array.</p>
The 2001 Hawaiian Ocean Mixing Experiment (HOME): High-frequency (>1cpd) Barotropic Current Data from the Southern Tomographic Array
<p>Ocean acoustic tomography was used to measure tides in the farfield of the Hawaiian Ridge in 2001 during the Hawaiian Ocean Mixing Experiment (HOME). The measurements were components of a suite of large- and small-scale measurements obtained during HOME with the aim of illuminating the pathways of tidal energy that may be driving deep-ocean mixing. Using reciprocal transmissions, the tomographic arrays were designed to measure the radiation of mode-1 internal tides from the Ridge, together with barotropic tidal currents. This publication makes available the tomographic estimates for barotropic currents derived from<br>the six paths of the southern HOME tomography array.</p>
Otsego Lake (NY, USA) high-frequency buoy data 2022-2024
Yokota Lab, SUNY Oneonta Biological Field Station Volunteer Dive Team and Otsego Lake Association has been operating an NSF-funded (award #1624527) automated lake data buoy since 2017. This data set contains Ice-free season data from 2022 to 2024.
Short-term high-frequency water dissolved carbon dioxide, temperature, dissolved oxygen, salinity and pH data from 8 Estonian lakes in year 2014
This dataset was used in the analysis described in the manuscript by Khan, H., A. Laas, R. Marcé, B. Obrador. Major effects of alkalinity on the relationship between metabolism and dissolved inorganic carbon dynamics in lakes. In review: Ecosystems. This dataset was used to calculate short term changes of dissolved inorganic carbon (DIC) concentrations and its variability in Estonian lakes. DIC data were compared with measured dissolved oxygen (DO) data in lakes covering a range of different alkalinity levels. Our results suggest that a large part of the measured variability in DO and DIC reflects non-metabolic processes. In lakes of lower alkalinity, DIC dynamics appear to be mostly driven by aquatic metabolism, whereas in lakes of higher alkalinity calcite precipitation plays a major role on DIC dynamics and needs to be considered along with metabolism.
High-Frequency Water Temperature Data at Lake Lillinonah, Connecticut, USA, 2011-2015
Since 2011, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University has been deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy is deployed early each summer and remains in the lake until late October. From 2011-2015, the instrumented buoy was equipped with a Nexsens thermistor chain, collecting data every meter from 0.1 to 12 meters and every other meter from 12 to 18 meters. Data were collected every fifteen minutes. Included in this data package are datasets containing 15-minute data as well as daily averages. Following the summer of 2015, the buoy was equipped with an RBR thermistor chain with sensors at different depths. Available data from the RBR thermistor chain (beginning in 2018) can be found in EDI package 558. Additional high-frequency water quality data for Lake Lillinonah including dissolved oxygen, specific conductance and pigment fluorescence are available in packages EDI559 and EDI560.
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Data for "Excitation of Low- and High-frequency Magnetosonic Whistler Waves Associated with SLAMS in the Terrestrial Foreshock" by Yao et al.
<p>The database includes the plasma data used in instability analyses and the theoretical analysis results based on the linear model.</p> <h3>Captions:</h3> <div><strong>Plasma_input.mat</strong> file is the plasma data used in the instability analyses, which is used to plot Figure 2a-2d.</div> <ul> <li><strong><em>B</em></strong>: magnetic field strength</li> <li><strong><em>n</em></strong>: plasma number density</li> <li><strong><em>Te_para</em></strong>: parallel electron temperature</li> <li><strong><em>Te_perp</em></strong>: perpendicular electron temperature</li> <li><strong><em>Tp</em></strong>: proton temperature</li> </ul> <div> </div> <div><strong>WWs_theo_predictions.mat</strong> file is the calculation result of linear growth rate and wave frequency. The results are used to plot Figure 2e and 2f.</div> <ul> <li><strong><em>f_theo</em></strong>: wave frequency in theoretical predictions</li> <li><strong><em>gamma_theo</em></strong>: growth rate in theoretical predictions</li> </ul>
High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013
Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.
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