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1,035 results for “Reservoir”
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
Time series of optical measurements (absorbance, fluorescence) for Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir in southwestern Virginia, USA 2019-2025
Depth profiles and surface dissolved samples analyzed for optical analyses (absorbance, fluorescence) were sampled from 2019 to 2025 in three drinking water reservoirs located in southwestern Virginia, USA including Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), and Falling Creek Reservoir (Vinton, Virginia). The reservoirs are owned and operated by the Western Virginia Water Authority as either primary or secondary drinking water sources for Roanoke, Virginia. The dataset consists of depth profiles at the deepest site of each reservoir, surface water samples from reservoir tributaries, and additional samples at within-reservoir sites. In Beaverdam and Falling Creek Reservoir, we collected depth profiles, gauged weir, and wetland samples approximately fortnightly throughout the summer stratified period (June 2019 - November 2019) and surface samples approximately monthly from May 2019 to October 2019 and in March 2020. Beaverdam Reservoir depth samples were additionally collected in summer 2022. We collected depth profiles and tributary samples monthly to seasonally in Carvins Cove Reservoir from late 2021 - 2023. From May 2024 - April 2025, we sampled depth profiles at multiple transects across the reservoir and surface water samples in one tributary approximately monthly at Carvins Cove. Absorbance was measured as colored dissolved organic matter (CDOM) using a spectrophotometer. Fluorescence was measured as fluorescent dissolved organic matter (fDOM) using a spectrofluorometer as excitation emission matrices (EEMs). Absorbance and fluorescence results are reported along with PARAFAC model results applied to the collected EEMs samples. Data visualization and quality assurance/quality control (QA/QC) scripts accompany the data package.
Reservoir ecosystems support large pools of fish biomass
<p>Supplemental data for "Reservoir ecosystems support large pools of fish biomass".</p> <p>Parisek, C.A., F.A., De Castro, J.D. Colby, G.R. Leidy, S. Sadro, A.L. Rypel. Reservoir ecosystems support large pools of fish biomass. <em>Sci Rep</em> <strong>14</strong>, 9428 (2024). https://doi.org/10.1038/s41598-024-59730-z</p>
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Crustacean and rotifer density and biomass for Beaverdam Reservoir, Falling Creek Reservoir, Carvins Cove Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2014-2025
Crustacean and rotifer density and biomass were measured from 2014 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Falling Creek Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Falling Creek, Carvins Cove, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. The dataset consists of integrated vertical tow samples from the whole water column, just the epilimnion, and just the hypolimnion (as the difference between the full water column and epilimnion tows), as well as discrete depth measurements collected with a Schindler trap. Most samples were collected at the deepest site of each reservoir adjacent to the dam. Sampling frequency and duration varied among reservoirs and years and included weekly to monthly routine monitoring as well as intensive 24-hour sampling campaigns. In 2014-2016, zooplankton samples were collected approximately fortnightly in the spring, summer, and autumn months at Beaverdam Reservoir, Carvins Cove Reservoir, and Gatewood Reservoirs. Falling Creek Reservoir samples were collected weekly to monthly in spring and summer 2014, and Spring Hollow Reservoir samples were collected approximately fortnightly in the spring, summer, and autumn months of 2015 and 2016. In 2019, zooplankton samples were collected approximately weekly to monthly from April to November at Beaverdam Reservoir and April to September at Falling Creek Reservoir. In 2020, zooplankton samples were collected approximately weekly to monthly from May to December at Beaverdam Reservoir and June to September at Falling Creek Reservoir. In 2021, zooplankton were collected monthly from M
Water chemistry time series for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2024
Depth profiles of dissolved organic and inorganic carbon and total and dissolved nitrogen and phosphorus were sampled from 2013-2024 in five drinking water reservoirs in southwestern Virginia, USA. The five drinking water reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of depth profiles of water chemistry samples measured at the deepest site of each reservoir adjacent to the dam. Additional water chemistry samples were collected at a gauged weir on Falling Creek Reservoir's primary inflow tributary, as well as multiple upstream, inflow, and outflow sites at Falling Creek Reservoir 2014-2024 and Beaverdam Reservoir in 2019, 2020, and 2022. Inflow sites at Carvins Cove Reservoir were sampled from 2020-2024, and additional within-reservoir sites were sampled in 2021-2024. The water column samples at Falling Creek Reservoir and Beaverdam Reservoir were collected approximately fortnightly from March-April, weekly from May-October, and monthly from November-February. Water column samples at Carvins Cove Reservoir were collected approximately fortnightly from May-August in most years, and approximately fortnightly from 2014-2016 in Gatewood and Spring Hollow Reservoirs, though sampling frequency and duration varied among reservoirs and years. A few additional samples collected in 2025 from Falling Creek Reservoir and Carvins Cove Reservoir are included in this dataset as they were analyzed with 2024 samples.
Time series of total metal and nutrient loads into Falling Creek Reservoir in southwestern Virginia, USA from 2019-2024
Metal and nutrient loads were calculated from 2019-2024 from the inflow stream to Falling Creek Reservoir (FCR), a drinking water reservoir located in Vinton, Virginia, USA. The reservoir is owned and operated by the Western Virginia Water Authority and is managed as a secondary drinking-water source for the city of Roanoke, VA. Only Fe, Mn, and nutrients (TN and TP) were analyzed and calculated in 2019. The full suite of metals (Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba) and nutrients were analyzed from 2020-2024. The loads that were collected using an ISCO automated sampler located at the main inflow tributary to FCR. Sampling frequency was approximately fortnightly from spring to fall (March - November). Load calculations were performed using the calculated cumulative flow over the sampling period from the ISCO and the analyzed total metal and nutrient concentrations. Please note we are publishing this data package before the nutrient samples have been analyzed, but will be included in later versions.
Regional drinking water quality monitoring program: long-term monitoring of water quality in select canals, reservoirs, and treatment plants of the greater Phoenix, Arizona metropolitan area drinking water system, ongoing since 1998
Regional Drinking Water Quality Monitoring Program ================================================== Arizona Statue University (ASU) has been working with regional water providers (Salt River Project (SRP), Central Arizona Project (CAP)) and metropolitan Phoenix cities since 1998 on algae-related issues affecting drinking water supplies, treatment, and distribution. The results have improved the understanding of taste and odor (T&O) occurrence, control, and treatment, improved the understanding of dissolved organic and algae dynamics, and initiated a forum to discuss and address regional water quality issues. The monitoring benefits local Water Treatment Plants (WTPs) by optimizing ongoing operations (i.e., reducing operating costs), improving the quality of municipal water for consumers, facilitating long-term water quality planning, and providing information on potentially future-regulated compounds. ASU has been monitoring water quality in terminal reservoirs (Lake Pleasant, Saguaro Lake, and Bartlett Lake) continuously from 1998 to the present for algae-related constituents (taste and odors, and more recently metals from the upper reservoirs), nutrients, and disinfection by-product precursors (i.e., total and dissolved organic carbon and organic nitrogen). Additional monitoring has been conducted in the SRP and CAP canal systems and in water treatment plants in Phoenix, Tempe and Peoria. During this work the Valley has been in a prolonged drought and recently one above average wet year, and this data provides important baseline data for development of new or expanded WTPs and management of existing WTPs in the future. The current work has improved the understanding of T&O sources and treatment, but additional research and monitoring into the future is necessary. Reservoir monitoring is conducted once per month at Bartlett Lake, Saguaro Lake, and Lake Pleasant, and quarterly at Roosevelt, Apache, and Canyon Lakes. Samples are depth integrated in the epilimnion a
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Production Temperatures Upper Jurassic Reservoir Germany Bavaria
<p>The dataset provides estimated production temperature prognosis for the Upper Jurassic reservoir in the Bavarian Molasse Basin assessed by a correlation of production temperatures and temperatures at the top of reservoir taken from the GeotIS -platform (www.geotis.de).Further expllanation in Zosseder, K., Pfrang, D., Schölderle, F., Bohnsack, D., Konrad, F. (2022): Characterisation of the upper Jurassic geothermal reservoir in the South German Molasse Basin as basis for a potential assessment to foster the geothermal installation development – Results from the joint research project Geothermal Alliance Bavaria. Geomechanics and Tunnelling 15, No. 1, pp. 17–24. https://doi.org/10.1002/geot.202100087.</p>
Yangtze River Basin Water Reservoir (YWR) dataset
<p>The Yangtze River Basin Water Reservoir (YWR) dataset, developed by multi-source satellite remote sensing data, provides monthly time series data for 443 reservoirs (with a total storage capacity of 276.51 km³) in the Yangtze River Basin (YRB) from 1990 to 2023, including area and storage data for all 443 reservoirs and water level data for 175 reservoirs. This dataset is associated with the study: Wang et al., "Advanced monitoring of reservoirs in the Yangtze River Basin from 1990 to 2023 using multi-source satellite remote sensing", Journal of Remote Sensing, under review, 2025.</p>
Indicative distribution map for Ecosystem Functional Group F3.1 Large reservoirs
<p>This archive contains indicative distribution maps and profiles for <strong>F3.1 Large reservoirs</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)
<p><strong>Hyperspectral Water Leaving Reflectance spectra (2988)</strong> measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the <strong>HYPSTAR®</strong> (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in <strong>Goyens et al.- Remote Sens. 2022</strong> - 14(21)- 5607; https://doi.org/10.3390/rs14215607.</p> <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> <p><strong>Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) </strong>were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e.,</p> <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv<strong>:</strong> <strong>Chlorophyll-a concentration</strong> estimated from the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: <strong>Chlorophyll-a concentration</strong> estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: <strong>Suspended particulate matter </strong>estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>
Micro elemental composition of Pike-perch (Sander lucioperca) population in Lipno Reservoir, Czechia
<p>This dataset contains the information on the micro elemental composition of Sagitta otoliths of Pike-Perch (<i>Sander lucioperca</i>) collected in Lipno Reservoir (Czechia). The dataset covers a wide range of micro elemental components (barium, calcium, copper, potassium, lithium, magnesium, manganese, sodium, rubidium, strontium and zinc) obtained from the otolith cores and rims of these fish specimens. The dataset includes readings from Pike-Perch directly collected in Lipno Reservoir, as well as from those reared in facilities and later introduced into the reservoir.</p>
Spatiotemporal variation in internal phosphorus loading, sediment characteristics, water column chemistry, and thermal mixing in a hypereutrophic reservoir in southwest Iowa, USA (2019-2020)
The primary aim of the data product is to quantify seasonal and spatial variation in sediment phosphorus fluxes in a temperate reservoir and evaluate mechanisms responsible for instances of elevated sediment phosphorus release. We studied Green Valley Lake, a hypereutrophic reservoir in southwest Iowa, USA, from 2019 to 2020. We measured sediment phosphorus flux rates and potential explanatory variables at three sites along the longitudinal gradient of the reservoir over six sampling events during winter and summer stratification as well as mixing events in the spring, summer, and fall. Ex situ sediment core incubations were used to measure sediment P release rates under ambient temperature and dissolved oxygen conditions. Explanatory variables measured included sediment phosphorus chemistry, sediment physical characteristics, epilimnetic and hypolimnetic nutrient concentrations, and thermal stratification patterns. These data will be used to identify mechanisms driving hot spots and hot moments of sediment phosphorus release, which will contribute to our understanding of how areas of lakebed and times of the year can disproportionately influence whole-lake water chemistry.
General Lake Model-Aquatic EcoDynamics model parameter set for Falling Creek Reservoir, Vinton, Virginia, USA 2013-2019
The General Lake Model (GLM), an open-source, one-dimensional hydrodynamic model, was used to simulate physical, chemical, and biological variables in Falling Creek Reservoir, Vinton, Virginia, USA between 15 May 2013 and 31 December 2019. GLM (v.3.2.0a3) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires three configuration files to run the model. First, the glm3.nml file configures lake metadata (including hypsometry), meteorological driver data, stream inflow and outflow driver data files, and physical response variables (mixing parameters and sediment heat zones). Second, the aed2_20220111_2DOCpools.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, silica, nitrogen, phosphorus, organic matter, and phytoplankton. Third, the aed2_phyto_pars_4Jan2022.nml file configures all parameters pertaining to phytoplankton dynamics. Meteorological data, two surface stream inflow files, a submerged oxygenation inflow file, and outflow file used in this calibration are also included.
Summer water chemistry, phytoplankton and zooplankton community composition, size structure, and biomass in a shallow, hypereutrophic reservoir in southwestern Iowa, USA (2019).
This data product contains data for Green Valley Lake, a hypereutrophic reservoir in southwest Iowa (USA) from the summer of 2019. We sampled and quantified zooplankton, phytoplankton, and nutrient concentrations (total N, total P, soluble reactive P, nitrate) in the lake weekly with the primary aim of assessing consumer nutrient cycling, specifically zooplankton nutrient cycling, in a hypereutrophic reservoir. Weekly plankton sampling included quantifying zooplankton and phytoplankton biomass, community composition, and size structure. Phytoplankton size was measured as the greatest axial linear distance which would be approached by a zooplankton grazer. Allometric equations from the literature were applied to the zooplankton size measurements to estimate zooplankton community excretion of N and P. We found that the estimated contribution of zooplankton excretion to the dissolved P pool was substantial in the spring. Further, we found evidence that zooplankton affected phytoplankton size distributions through selective grazing of smaller phytoplankton cells likely affecting nutrient uptake and storage by phytoplankton.
Time series of high-frequency sensors measuring water temperature and dissolved oxygen at discrete depths in Falling Creek Reservoir, Virginia, USA in 2012-2018
We measured water temperature and dissolved oxygen at multiple depths in Falling Creek Reservoir (Vinton, Virginia, USA) with high-frequency (10 to 15-minute) sensors for different durations during 2012 to 2018. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. All measurements were collected at discrete depths at the deepest site of the reservoir adjacent to the dam. The sensors consisted of: 1) InsiteIG dissolved oxygen and water temperature sensors (Model 20 dissolved oxygen sensor) at both 1 m (November 2015 - December 2018) and 8 m (September 2012 - December 2018) and 2) HOBO (HOBO Pendant Temperature/Light 64K Data Logger) water temperature loggers deployed at 1, 2, 3, 4, 5, 6, 7, 8, and 9.3 m depths (September 2015 - January 2018).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.