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69 results for “inflow”
Discharge time series for the primary inflow tributary entering Falling Creek Reservoir, Vinton, Virginia, USA 2013-2025
Discharge rates and water temperature of the primary inflow tributary into Falling Creek Reservoir (Vinton, Virginia, USA), also known as Tunnel Branch, were measured at a gauged weir on a 15-minute temporal resolution from May 2013 to December 2025. The gauged weir is located at 37.30858, -79.83494. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA). The dataset consists of water temperatures and discharge rates calculated from a pressure transducer deployed by the WVWA in a rectangular weir (15 May 2013 - 06 June 2019) and in a v-notched weir (07 June 2019 - 31 December 2025). From 07 June 2019 to 31 December 2025, water temperature and discharge data were also collected from a Virginia Tech-deployed (VT) pressure transducer installed in the same weir. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Manually-collected discharge data for multiple inflow and outflow tributaries at Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir, Virginia, USA from 2019-2025
Discharge rates at multiple inflow streams into Falling Creek Reservoir (Vinton, Virginia, USA), Beaverdam Reservoir (Vinton, Virginia, USA), and Carvins Cove Reservoir (Roanoke, Virginia, USA), and one outflow at Falling Creek Reservoir were measured manually using multiple methods from 2019-2025. Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of discharge rates calculated using one of four methods: handheld flowmeter, salt injection, velocity float, or bucket method. Data were collected weekly to monthly from February through October 2019 at Falling Creek and Beaverdam Reservoir, and approximately weekly to seasonally at Falling Creek and Carvins Cove from 2020 to 2025. The dataset is accompanied by a maintenance log and quality assurance/quality control analysis scripts.
DU00-W-212 airfoil polars, sinusoidal inflow
<p>Averaged lift and drag coeffficients of a DU00-W-212 profile in sinusoidally varying inflow (turbulence level approx. 5%) generated with an active grid at Reynolds numbers 500,000 and 900,000.</p> <p>Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord.<br> Standard wind tunnel corrections according to Allen & Vincenti are applied.</p> <p>Data sets 182, 183, 186, 188: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side<br> Data sets 183, 188, 242, 245: measured starting at positive angles of attack (AOA) to negative AOAs<br> Data sets 182, 186, 243, 244: measured starting at negative angles of attack (AOA) to positive AOAs</p> <p>The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).</p>
DU00-W-212 airfoil polars, laminar inflow
<p>Averaged lift and drag coeffficients of a DU00-W-212 profile in laminar flow (background turbulence level approx. 0.3%) at Reynolds numbers 500,000 and 900,000.</p> <p>Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord.<br> Standard wind tunnel corrections according to Allen & Vincenti are applied.</p> <p>Data sets 119, 120, 121, 122: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side<br> Data sets 104, 107, 119, 122: measured starting at negative angles of attack (AOA) to positive AOAs<br> Data sets 105, 106, 120, 121: measured starting at positive angles of attack (AOA) to negative AOAs</p> <p>The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).</p>
DU00-W-212 airfoil polars, mimicked DanAero inflow
<p>Averaged lift and drag coeffficients of a DU00-W-212 profile in turbulent inflow generated with an active grid at Reynolds numbers 500,000 and 900,000. The inflow pattern was mimicked from measurements a the blade with a 5-hole pressure probe performed in teh DanAero project.</p> <p>Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord.<br> Standard wind tunnel corrections according to Allen & Vincenti are applied.</p> <p>Data sets 203, 204, 210, 211: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side<br> Data sets 203, 211, 225, 230: measured starting at positive angles of attack (AOA) to negative AOAs<br> Data sets 204, 210, 224, 239: measured starting at negative angles of attack (AOA) to positive AOAs</p> <p>The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).</p>
Warragamba inflow data, predictors and code
<p>This repository contains data and code used for the analyses presented in Devanand et al. (2023) Can climate knowledge enable Warragamba Dam, Sydney, Australia to be used to manage flood risk?, 10.1088/1748-9326/ad0afb. The csv files contain historical inflow into the Warragamba dam in eastern Australia for the period 1970-2022, the large scale climate indices, and antecedent soil moisture. The climate predictors are obtained from https://psl.noaa.gov/gcos_wgsp/Timeseries/. The shape file 'warragamba_catchment.shp' is the catchment area that is used to estimate catchment average soil moisture from the AWRA-L datatset (https://dx.doi.org/10.25914/NDY0-J367). The python notebook 'probability_of_dam_inflows.ipynb' contains the code used to estimate inflow exceedance probabilities as a function of the predictors.</p>
Long term limnological measurements in Acton Lake, a southwest Ohio reservoir, and its inflow streams: 1992-2023
Long-term data were collected from Acton Lake and its inflow streams on a suite of physical, chemical and biological variables. The data are collected as part of long-term research investigating how Acton Lake, a eutrophic reservoir, responds to changes in ecosystem subsidies of detritus (sediments) and nutrients. Our data span 31 years, from 1992-2023, although for some parameters the data set spans a shorter time frame within this period. In three of Acton Lake’s inflow streams, collectively constituting ~86% of the lake’s watershed, we include data on hourly stream discharge, as well as concentrations of suspended sediments, ammonium-N, nitrate+nitrite-N, and soluble reactive phosphorus (P). Data on the concentrations of these constituents were collected at various time scales depending on stream discharge (usually every 6-8 hours during storms, every 1-3 days during baseflow). In Acton Lake, we collected data on several parameters from an “outflow” site, at the deepest part of the lake where the water column is usually thermally stratified in summer. Vertical profile data were collected for temperature, dissolved oxygen, photosynthetically active radiation (PAR), and chlorophyll. Depth profile data were collected at 0.5 or 1 m intervals (depending on depth and year), usually weekly from April or May until September or October. In addition, data for several parameters were obtained from “integrated samples” that collect water from the lake surface to the bottom of the euphotic zone (defined as the depth at which PAR equals 1% of surface PAR) with a tube or pump. Parameters for which integrated data are presented include chlorophyll; suspended solids; non-volatile suspended solids; particulate (seston) carbon, nitrogen and phosphorus; and total nitrogen and total phosphorus. We also collected Secchi depth data, using standard methods. Larval fish were collected in the top 1-3 meter stratum of the water column with a metered net to generate estimates of lake-wide l
2004 Estuarine Fish Sampling - Managing freshwater inflow to estuaries in northeast Puerto Rico
Historical data are often one of the only resources for documenting and assessing causes of environmental change, particularly in developing regions where funding for ecological studies is limited. In this study, previously unpublished data from a 1977 year-long study of the fish community of the Espiritu Santo estuary are presented. This dataset is among the oldest and most extensive surveys of a Caribbean island estuarine fish community. A comparison of these historical data with data collected in June and July 2004 using identical sampling methods allowed description of potential long-term changes in the fish community, identification of vulnerable species, and assessment of potential drivers of change. Results strongly suggest a decline in species richness and abundance in the Espiritu Santo estuarine fish community, with greater declines in freshwater-tolerant than marine or euryhaline species. Declines in freshwater inflow to the estuary, due to large-scale upstream water abstractions for municipal use, have increased since the initial 1977 survey. This is the first study to examine long-term change in the fish community of a tropical island estuary. Additional research and conservation efforts are needed to understand mechanisms of change and to protect Caribbean island estuarine fish communities. Chapter 2 (isotope and gut content data): The contribution of riverine-derived organisms and organic matter to four fishes along the salinity gradient in two Puerto Rican estuaries, the Espiritu Santo and Mameyes, was examined via stable isotope and gut content analyses. Stable isotope analyses indicated that riverine organic matter potentially contributed as much as 69% of the diet of one (caitipa mojarra, Diapterus rhombeus) of four fishes sampled. In contrast, riverine organic matter was of little direct importance to the three other fishes, tarpon snook (Centropomus pectinatus), ground croaker (Bairdiella ronchus), and white mullet (Mugil curema) contributing le
SBC LTER: Land: Hydrology: Stream discharge and associated parameters at Devereaux Creek at Slough inflow (DV01)
Stream Discharge and water temperature were collected with a Solinst Model 3001 LT Levelogger at Devereaux Creek at Slough inflow in the Santa Barbara coastal area (site ID: DV01). Data are reported hourly. Stage values were converted to discharge using a rating curve developed with stream channel cross-sections, roughness estimates and the HEC-RAS model.
ERCOT Reservoir Watershed Delineations and Inflow Scenarios
<p>This dataset includes daily runoff totals for historical (reanalysis) and future (GCM simulations) climate. The scenarios are generated for upstream watersheds of 44 reservoirs that supply water for thermal plant cooling throughout the Electric Reliability Council of Texas (ERCOT) power grid region. These data are derived from 1/24 degree gridded CONUS-scale hydrological simulations. Runoff projections are masked to delineated watershed areas (included as shape files) and summed across grid cells within the target watershed. These data may be aggregated to monthly scale to produce unregulated inflow scenarios for ERCOT reservoirs. Reservoir attributes and storage records are included in a supplementary excel spreadsheet. Please see README.txt for further details.</p>
Quenching of star formation from a lack of inflowing gas to galaxies
<p>This dataset provides HST and ALMA mosaics of the REQUIEM-ALMA survey of six strong gravitationally lensed quiescent galaxies at z=1.6 to z=3.2 (MRG-M1341, MRG-M0138, MRG-M2129, MRG-M0150, MRG-M0454, MRG-M1423) from Whitaker et al. (2021). The HST mosaics were produced with the <a href="https://github.com/gbrammer/grizli">grizli</a> software module. The ALMA data products include the full spectrally-averaged continuum data weighted for optimum sensitivity (with MRG-M2129 and MRG-M0138 including a correction for the primary beam response).</p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
Hydropower dataset of hourly inflow values for European bidding zones for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010).</p> <p> </p> <p><strong>Method Description </strong><br> The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff.</p> <p>The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full).</p> <p><strong>Data coverage</strong><br> This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). </p> <p>The countries and their acronyms for both technologies included in this dataset are:</p> <table> <thead> <tr> <th scope="col">Country</th> <th scope="col">Run-of-River </th> <th scope="col">Storage</th> </tr> </thead> <tbody> <tr> <td>Austria</td> <td>AT_ROR</td> <td>AT_STO</td> </tr> <tr> <td>Belgium</td> <td>BE_ROR</td> <td>BE_STO</td> </tr> <tr> <td>Bulgaria</td> <td>BG_ROR</td> <td>BG_STO</td> </tr> <tr> <td>Switzerland</td> <td>CH_ROR</td> <td>CH_STO</td> </tr> <tr> <td>Cyprus</td> <td>CZ_ROR</td> <td>CZ_STO</td> </tr> <tr> <td>Germany</td> <td>DE_ROR</td> <td>DE_STO</td> </tr> <tr> <td>Denmark</td> <td>DK_ROR</td> <td> </td> </tr> <tr> <td>Estonia</td> <td>EE_ROR</td> <td> </td> </tr> <tr> <td>Greece</td> <td>EL_ROR</td> <td>EL_STO</td> </tr> <tr> <td>Spain</td> <td>ES_ROR</td> <td>ES_STO</td> </tr> <tr> <td>Finland</td> <td>FI_ROR</td> <td>FI_STO</td> </tr> <tr> <td>France</td> <td>FR_ROR</td> <td>FR_STO</td> </tr> <tr> <td>Great Britain</td> <td>GB_ROR</td> <td>GB_STO</td> </tr> <tr> <td>Croatia</td> <td>HR_ROR</td> <td>HR_STO</td> </tr> <tr> <td>Hungary</td> <td>HU_ROR</td> <td>HU_STO</td> </tr> <tr> <td>Ireland</td> <td>IE_ROR</td> <td>IE_STO</td> </tr> <tr> <td>Italy</td> <td>IT_ROR</td> <td>IT_STO</td> </tr> <tr> <td>Luxembourg</td> <td>LU_ROR</td> <td> </td> </tr> <tr> <td>Latvia</td> <td>LV_ROR</td> <td> </td> </tr> <tr> <td>the Netherlands</td> <td>NL_ROR</td> <td> </td> </tr> <tr> <td>Norway</td> <td>NO_ROR</td> <td>NO_STO</td> </tr> <tr> <td>Poland</td> <td>PL_ROR</td> <td>PL_STO</td> </tr> <tr> <td>Portugal</td> <td>PT_ROR</td> <td>PT_STO</td> </tr> <tr> <td>Romania</td> <td>RO_ROR</td> <td>RO_STO</td> </tr> <tr> <td>Sweden</td> <td>SE_ROR</td> <td>SE_STO</td> </tr> <tr> <td>Slovenia</td> <td>SI_ROR</td> <td>SI_STO</td> </tr> <tr> <td>Slovakia</td> <td>SK_ROR</td> <td>SK_STO</td> </tr> </tbody> </table> <p> </p> <p><strong>Data structure description</strong><br> The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the row number <ul> <li>Label: unlabeled</li> <li>Contents: interger range [1,262968]</li> </ul> </li> <li>second column (or B) contains the valid-time <ul> <li>Label: T1h</li> <li>Contents represent time with text as [DD/MM/YYYY HH:MM])</li> </ul> </li> <li>column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type <ul> <li>Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR)</li> <li>Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.).</li> </ul> </li> </ul> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
NACLIM - Fluxes: Hornbanki Section Atlantic inflow volume and heat fluxes
<p><strong>Last Update: 31 October 2014</strong></p> <p><strong>Data set:</strong> Atlantic inflow volume and heat fluxes at Hornbanki section </p> <p><strong>Description:</strong> Monthly mean of Atlantic inflow volume and heat transports </p> <p><strong>Period: </strong>January 1994 – July 2014</p> <p><strong>Location:</strong> 66°50′ N 21°30′ W (map) </p> <p><strong>Instruments: </strong>CTD, moored current meters </p> <p><strong>Variables:</strong> AW_transp, Heat_transp - Atlantic Water volume transport [Sv] and respective heat transport [TW]</p> <p><strong>Source:</strong> Steingrímur Jónsson and Hedinn Valdimarsson (MRI)</p>
NACLIM - Fluxes: Iceland-Faroe Atlantic inflow transport
<p><strong>Last update: 31 October 2014</strong></p> <p><strong>Data set: </strong>Iceland-Faroe Atlantic inflow transport </p> <p><em>Preliminary results: HAV is in the process of recalculating the whole series (Update 31 October 2014)</em></p> <p><strong>Description: </strong>Weekly averaged volume flux of Atlantic water through section N (see map) crossing the Faroe Current</p> <p><strong>Period:</strong> June 1997 – May 2014 </p> <p><strong>Location:</strong> 62°20′ – 63°40′ N 6°05′ W</p> <p><strong>Instruments:</strong> Moored Acoustic Doppler Current Profilers (ADCP) and CTD sections </p> <p><strong>Variables: </strong>Atlantic water component of transport (Sv) through the section, which has come across the Iceland-Faroe Ridge </p> <p><strong>Source:</strong> Bogi Hansen and Karin Margretha H. Larsen (HAV)</p>
Hydro Energy Inflow for Power System Studies
<p>Energy inflow time series for hydro power on the European country level.</p> <p>Inflow was derived from reanalysis data using a potential energy approach.</p> <p>Dataset includes ten years (2003-2012) of data with daily resolution for 30 European countries.</p> <p>The dataset is described in more detail in</p> <p>A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann ,Simulation of renewable feed-in for power system studies, RESTORE 2050 project report</p> <p> </p>
Apulian Aqueduct demo site: daily time series of reservoirs inflows 2010-2019
<p>This dataset contains the time series of net inflows to the main reservoirs of Apulian aqueduct (demo site 1), computed with a mass-balance equation at a daily time step.</p> <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m<sup>3</sup>/sec</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô Deliverable D4.1.</p>
Apulian Aqueduct demo site: daily time series of simulated system behavior for future inflows conditions (RCP4.5)
<p>This dataset contains the daily time series obtained from the strategic model simulation, considering net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) considering climate projection RCP 4.5 and two decades, in the medium (2050-2059) and long-term future (2090-2099). In all simulations, a modified version of the drinking water demand is considered, following a different distribution of the populations, an increase in density in coastal areas also due to investments in the tourism sector, to the detriment of density in inland areas.</p> <p>For each decade, two simulations are performed, with or without the rehabilitation of some well-fields making the water withdrawn drinkable with innovative purification techniques.</p> <p>. More precisely, the dataset contains:</p> <ul> <li>the daily level of the main reservoirs;</li> <li>the water supplied to all users (drinking water users, irrigation, and industrial districts) from each reservoir;</li> <li>the corresponding single irrigation and industrial deficit;</li> <li>the total drinking water deficit;</li> <li>the aggregated distribution cost.</li> </ul> <p>Two simulations are performed, with or without the environmental flow constraint acting on each reservoir release activated.</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: <ul> <li>Springs: Sele, Calore;</li> <li>Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo;</li> <li>Users: drinking water users, irrigation, and industrial districts supplied by Apulian aqueduct.</li> </ul> </li> <li>Unit of measure: <em>m</em>, <em>m3/s</em> depending on the variable</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>.</p>
Apulian Aqueduct demo site: daily time series of simulated system behavior for baseline inflows conditions
<p>This dataset contains the daily time series obtained from the strategic model simulation, considering baseline inflows conditions (natural springs and main reservoirs of Apulian aqueduct - demo site 1). More precisely, the dataset contains:</p> <ul> <li>the daily level of the main reservoirs;</li> <li>the water supplied to all users (drinking water users, irrigation, and industrial districts) from each reservoir;</li> <li>the corresponding single irrigation and industrial deficit;</li> <li>the total drinking water deficit;</li> <li>the aggregated distribution cost.</li> </ul> <p>Two simulations are performed, with or without the environmental flow constraint acting on each reservoir release activated.</p> <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: <ul> <li>Springs: Sele, Calore;</li> <li>Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo;</li> <li>Users: drinking water users, irrigation, and industrial districts supplied by Apulian aqueduct.</li> </ul> </li> <li>Unit of measure: <em>m</em>, <em>m3/s</em> depending on the variable</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>.</p>
InTheMED WP6 Data Archive – Tympaki, Greece: Inflow Timeseries and Pumping Rates
<p>This data archive includes the files “InTheMED_WP6_DS_InflowTimeSeriesGreece<strong>.</strong>mat” and “InTheMED_WP6_DS_PumpingRatesGreece.mat” which contain the datasets that were used as inputs for the simulation runs of the Tympaki numerical groundwater model.</p>
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