Find research datasets worth reusing

openCC (other)Jan 2025View details →

Concentration–discharge relationships of chlorophyll describe the origin and fluxes of river algae across ecoregions

Since 2017, the National Ecological Observatory Network (NEON) program provides an extensive network of automated instruments and field sampling at 34 different aquatic sites across the U.S. NEON data provides a rare opportunity to combine high-frequency measurements from sensors with physical samples collected at multiples sites from different biomes. Such combination of data is frequently available only for 1, or few, sites. NEON provides the necessary data to translate some of the methods and research previously applied to single sites to a large spatial and temporal scale. Our approach in this study was to obtain all the available and reliable data from NEON aquatic sites, and analyze high frequency measurements of Chlorophyl a (Chl a) concentration and turbidity during high flows with a common analytical procedure. Then, we relate these high flow patterns to watershed conditions and algal communities to elucidate the controls on river phytoplankton communities and fluxes. The data provided here summarizes multiple storm characteristics and C-Q metrics (hysteresis index) for Chl a and turbidity for several high flow events at 26 different NEON stream and river sites.

openCC (other)Jan 2026View details →

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.

openCC (other)Feb 2026View details →

Stage height, discharge, water temperature, pH, conductivity, and dissolved oxygen at Cascade Brook in Black Rock Forest, Cornwall, NY 1998 - 2015

Black Rock Forest established a stream monitoring station in the Cascade Brook watershed in 1998. The station is equipped with a 120-degree sharp-crested v-notch weir and was built to continuously monitor the flow, temperature, pH, conductivity, and dissolved oxygen content of the stream water in Cascade Brook in New York. The Cascade Brook watershed encompasses 135 hectares (334 acres), is generally a southern aspect, and has been little affected by human activity in the last century. The forest is deciduous and dominated by oak species. The station records data hourly, and while water chemistry sensors have been used intermittently through the decades, stage height and discharge has been measured consistently from1998 - 2015.

openCC (other)Jan 2026View details →

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.

openCC (other)Oct 2025View details →

NEON Provisional Continuous and Field Discharge - Water Year 2024 (2023-10-01 - 2024-09-30), United States

As of the date of this publication in EDI, NEON publishes one-minute continuous discharge data that have been corrected and gap-filled in the Continuous Discharge (DP4.00130.001, https://data.neonscience.org/data-products/DP4.00130.001) data product. As part of a recent manuscript describing the data quality improvements to NEON's continuous discharge data brought on my implementation of corrections and gap-filling, a mixture of released and provisional NEON data were downloaded. To ensure the reproducibility of the data set used to conduct the analysis presented in the manuscript, the downloaded provisional data, which is subject to change, is saved as a static data set in this EDI publication. The same is done for the Discharge Field Collection (DP1.20048.001, https://data.neonscience.org/data-products/DP1.20048.001) data product, which is used in the same analysis.

openCC (other)Jan 2020View details →

Toolik Lake Inlet discharge data collected during summers of 2010 to 2018, Arctic LTER, Toolik Research Station, Alaska.

Stream discharge, temperature, and conductivity of Toolik Lake Inlet stream for 2010 - 2018 study season. Water level was recorded with a Stevens PGIII Pulse Generator and Conductivity (EC) and Temperature measured with a Campbell Scientific Model 247 Conductivity and Temperature probe.

openCC (other)Jan 2021View details →

Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019

Oksrukuyik Creek stage height and calculated discharge for the summer of 1989 to present. Stream temperature and discharge measured each summer for several streams in the Toolik area. Stream height is converted into stream discharge based on a rating curve calculated from manual discharge measurements throughout the season. The principal investigator in charge of the temperature and discharge measurements is Dr. Breck Bowden. Note: This file combines the previous individual yearly files.

openCustomJan 2024View details →

Discharge of three small creeks along the Delmarva Peninsula 2003-2009

This dataset contains flow and discharge data for three small creeks along the Atlantic Coast of the Delmarva Peninsula in Virginia. The data are not continuous, but were collected irregularly on an event-basis. It includes both base-flow and storm measurements.

opencc-by-4.0Jul 2020View details →

A 2-minute rainfall (12 locations) and discharge time series at the Vallon de Nant catchment, Switzerland, for 2018 summer seasons

The data set contains rainfall time series within the experimental 13.4 km² Vallon de Nant catchment, Switzerland (Michelon et al., 2020), from June 30th to September 23rd 2018 at 12 locations. A network of Pluvimate drop-counting raingauges (www.driptych.com) measured continuously the rainfall intensity at a 2-minute resolution. Operation and characteristics of the raingauges are detailed in Benoit et al. (2018) and Michelon et al. (2020), and the rating curve is described by Ceperley et al. (2018). Description of the files: <ul> <li>data.csv contain the rainfall intensities for the observation period, along with the main river discharge measured at the <a href="https://map.geo.admin.ch/?lang=fr&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.zeitreihen,ch.bfs.gebaeude_wohnungs_register,ch.bav.haltestellen-oev,ch.swisstopo.swisstlm3d-wanderwege,KML%7C%7Chttps:%2F%2Fpublic.geo.admin.ch%2FaLKDanGXRPGMpB_D51f2Tg&layers_visibility=false,false,false,false,true&layers_timestamp=18641231,,,,&E=2574619.27&N=1122462.26&zoom=8">outlet</a> over the same 2-minutes time step as the rainfall intensity. We also provide areal rainfall intensity aggregated over the whole catchment: Columns: <ul> <li>year [-]</li> <li>month [-]</li> <li>day [-]</li> <li>hour [-]</li> <li>minute [-]</li> <li>specific discharge 95% inf. [mm/day]: inferior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge 95% sup. [mm/day]: superior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge mean [mm/day]: mean value of the specific discharge over 2 minutes</li> <li>specific discharge median [mm/day]: median value of the specific discharge over 2 minutes</li> <li>P St. #X [mm]: rainfall amount measured at the station X over 2 minutes</li> <li>P stochastic mean [mm/h]: rainfall amount interpolated over the whole catchment over 2 minutes</li> <li>P stochastic std [mm/h]: standard deviation of the stochastic rainfall interpolation, over 2 minutes</li> </ul> </li> <li>stations.csv describes the raingauge locations. Columns: <ul> <li>Station ID [-]</li> <li>lon [WGS84]: decimal longitude of the station into WGS84</li> <li>lat [WGS84]: decimal latitude of the station into WGS84</li> <li>E [CH1903]: east coordinate into Swiss Coordinate System</li> <li>N [CH1903]: north coordinate into Swiss Coordinate System</li> <li>elevation [m asl]: altitude of the station in meters above the sea level</li> <li>data in 2017 [-]: flag if the station was working over the 2017 observation period</li> <li>data in 2018 [-]: flag if the station was working over the 2018 observation period</li> </ul> </li> <li>rainfall_viewer.m is a MatLab script (created with MatLab 2017b) which allows the joint visualization of the rainfall intensities and river discharge. It produces a composite figure with the following plots: <ul> <li>On top the general hydrograph over the whole observation period [mm/day]. The red dashed lines mark out period that the other plots are focus on. The shaded orange areas correspond to when the river stage data was not available.</li> <li>Below, the zoomed hydrogram show a detailed view of the river discharge (and uncertainty). In case a river reaction is associated, the discharge event is marked out by red dashed lines. Between these vertical lines is drawn a line joining the initial and final baseflow, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff part.</li> <li>In the middle a zoomed magnification of the hydrograph that shows a detailed view of the discharge in the river [mm/day]. When a river response is associated, the discharge event is marked with dashed red lines. Between these vertical lines a line joining the initial and final baseflow is drawn, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff.</li> <li>At the bottom are shown the rainfall recorded by each of the 12 rain gauges (the y-axis scale between 2 stations is about 20 mm/h). The rainfall event is marked out by green dashed lines.</li> <li>Above is shown the rainfall amount (and uncertainty) interpolated over the catchment using the stochastic method. The rainfall event is marked out by green dashed lines.</li> <li>On the left, a map with the 12 raingauge locations show the total amount of rainfall recorded by each station during the event (a red cross shows missing data). It is possible to zoom in the plots by clicking with the left and right mouse buttons to define respectively the starting and ending of the visualization window. The middle button defines a third time reference used to identify rainfall intensity peaks or discharge peaks. Statistics concerning the visualization period are displayed on the MatLab console. Pressing [enter] will save the figure into a PNG file named with the starting and ending dates of the visualization window.</li> </ul> </li> <li>Q_stats.m is a MatLab function used by the main code rainfall_viewer.m</li> <li>print_figure.m is a MatLab function used by the main code rainfall_viewer.m</li> <li>data.mat is a MatLab data file with all data required by the main code rainfall_viewer.m</li> </ul>

opencc-by-4.0Nov 2024View details →

RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean

The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8). Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).

opencc-by-4.0Mar 2024View details →

Rating curves based on satellite altimetry and in-situ discharge data

<h1>Context: </h1> The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV. <h1>Data description :</h1> Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series: ⋅ Method 1: The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period. ⋅ Method 2: The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q). <h1>Approaches to derive Rating Curve (RC) :</h1> <h2>Bayesian Approach :</h2> The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates.  According to this, the estimation of the rating curve using the Bayesian method involves several steps: <ul> <li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li> </ul>                                                                                       Q = a⋅(WSE-z0)b Here, a, z0 and b are the parameters of the rating curve. a, is a scaling coefficient governing the magnitude of the Q-WSE relationship, b, characterizes the nature of this relationship, and z0, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959). <ul> <li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li> </ul> - Coefficient “a”:  adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S1/2 (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:                                                                                       a ∈ [0; 3000] - Coefficient “b”: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:                                                                                       b ∈ [0; 5] - Coefficient “z0”: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this reason, the value cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:                                                                                       z0 ∈ [min(WSE)-30; min(WSE)] <ul> <li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li> </ul> <h2>Quantile approach : </h2> The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013). Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously. <h1>File description :</h1> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>basin-station</td> <td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td> </tr> <tr> <td>lon</td> <td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td> </tr> <tr> <td>lat</td> <td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td> </tr> <tr> <td>a</td> <td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td> </tr> <tr> <td>b</td> <td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td> </tr> <tr> <td>z0</td> <td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td> </tr> <tr> <td>a_sd</td> <td>Standard deviation of the coefficient "a". Number with 3 decimals.</td> </tr> <tr> <td>b_sd</td> <td>Standard deviation of the coefficient "b". Number with 3 decimals.</td> </tr> <tr> <td>z0_sd</td> <td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td> </tr> <tr> <td>period</td> <td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td> </tr> <tr> <td>nb</td> <td>Number of overlap dates to compute the rating curve.</td> </tr> <tr> <td>Methodology</td> <td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td> </tr> <tr> <td>Source</td> <td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td> </tr> </tbody> </table> --------- THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

opencc-by-4.0Nov 2024View details →

Global River BankFull Discharge (GQBF) - Siberia(SI) & South Pacific/Australia(SP)

The GQBF is the estimated bankfull discharge across ~2.87 million km (length) of global river reaches. The bankfull discharge here is defined as the maximum flow rate contained within a river just before inundation occurs in the surrounding floodplain. We based our river bankfull discharge estimation on a newly developed river network, Global RIver Topology (GRIT), using GRIT’s river reaches as the spatial scale to represent the variation in bankfull discharge. We included all GRIT river reaches that coincided with the Global River Width from Landsat (GRWL) river masks (with overlapping ratio >=0.5). This selects river reaches with satellite-derived width measurements >=30 m, resulting in a total length of ~2.87 million km. Here, the GQBF represents the time-averaged bankfull discharge at <1 km (river length) spatial resolution. Regions Added regions SI, SP Vector files. <ul> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> The subcontinental catchment groups (vector, polygons) can be found at <a href="https://zenodo.org/records/11219313">GRIT domain polygon</a> (GRITv06_domain_GLOBAL.gpkg.zip). They allow for more fine-grained subsetting of data . Vector files are provided in geographic WGS84 coordinates (EPSG:4326). Change log <ul> <li>v0.1 - 2024-09-29 <ul> <li>First globally complete dataset published</li> </ul> </li> <li>v0.1 - 2024-11-19 <ul> <li>Add vector files for regions SI, SP</li> </ul> </li> </ul>

opencc-by-4.0Feb 2022View details →

Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)

Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021. Data were collected by the research teams of Bettina Schaefli2 and Stuart N. Lane1. 1 Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland 2 Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland For further information, please contact: <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> Description of data A detailed description of the dataset is provided in the data_description_analysis.pdf file. In particular, the methodology and stage-discharge rating curves are provided in this file. Stream data were measured in three locations from glacier snout (Station 1); after the outwash plain (Station 2) and at the end of the glacier forefield (Station 3) (see overview_GS.png). A shapefile is also provided (coordinate system LV95). 2 datasets are available in the data.zip file: <ul> <li> River_2019_2021_10T.csv : contains the measured River Electrical conductivity (EC) [μS/cm], Stage [meters] and Temperature [°C] data for all stations in a tidy data format (see pdf for detailed description), with a 10 minutes timestep. </li> <li> Discharge2020_10T.csv & Discharge2021_10T.csv : contains the estimated discharge [m3/s] at Station 1 and Station 2 from July 2020 to October 2021 and estimated error (2 standard deviations) in a tidy data format (see pdf for detailed description), with a 10 minutes timestep. </li> </ul> Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the Point_discharge_measurements_2020_2021.xlsx file. Plots of river parameters and discharge are also provided in data.zip for vizualisation.

opencc-by-4.0Dec 2016View details →

Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"

This dataset corresponds to the output files that were produced for the study reported in: Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, Journal of Hydrology, 554, 331-341 Data sources The following sources were used to produce files in this dataset: <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> Description of files The files in this dataset contain are described below: <ul> <li>Flowlines: This folder contains a shapefile (GL_coastcatchment_NHDflowline) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li>Catchment: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li>Centroid: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li>DischargeVulnerabilities.csv. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km2).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m2)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km2).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m2)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m2/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul>

opencc-by-4.0Mar 2018View details →

Low overtopping discharges over sea walls and breakwaters

The design of seawalls and breakwaters is often required to achieve very low target overtopping discharges when these structures protect vulnerable infrastructure or activities. The balance between economically viable protection and performance requirements is often difficult to achieve without good knowledge on low overtopping. The paucity of data in this space and the higher uncertainty associated with existing methods, increases the challenge. The occurrence of low number of overtopping waves has the consequence that any test results are substantially more affected by the inherent variation of random waves, therefore more uncertain. Within the multi-institute project RECIPE under the HYDRALAB+ project, experimental studies for RECIPE Task 8.2 have generated new data on the response of seawalls, breakwaters and related coastal structures with the aim of improving future model testing. Tests by HRW have explored wave overtopping with contributions of data from UPORTO and LNEC. These physical model test results explore these issues and provide example data. The tests were successful in obtaining low to very low overtopping discharge test data. For low / very low overtopping discharges, these test data present considerable scatter relative to the latest empirical prediction. A number of repetitions were performed for wave conditions resulting in very low overtopping discharges, which illustrated the inherent uncertainty associated with low overtopping.

opencc-by-4.0Aug 2024View details →

Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment

This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.

opencc-by-4.0Jul 2021View details →

Predicted locations and nitrate pollution of groundwater discharge from D3pl karst aquifer in Latvia

Description A georeferenced raster data layer [5_predicted_D3pl_GW_discharge_zone.tif] showing predicted likelihood that groundwater polluted with nitrate (NO3-) is discharging as springs or diffuse seepage from the Upper Devonian Pļaviņas (D3pl) dolomite karst aquifer in Latvia is presented. The cell value indicates the likelihood (0 – low, 1 - high) that groundwater with nitrate contamination is discharging from the D3pl aquifer at this location. Value of 0 means that no groundwater is discharging there. The BalticTM 93 (EPSG:25884) references system is used. The rationale and methodology for elaborating the map of groundwater discharge as springs or diffuse seepage from the D3pl dolomite karst aquifer is described in the main article (Kalvāns et al. under review). In short, a 3D regional geological model (Popovs et al. 2015), land surface elevation model and bedrock surface elevation model (Popovs et al. under review) were combined to identify locations where aquitard at the base of D3pl aquifer is outcropping at bedrock surface and in the nearby depressions (in a distance up to 0.25 km) the land surface was below the surface of this aquitard. The likely contamination with NO3- was estimated from proportion of arable land (European Environment Agency 2018) within 4.75 km window. It is assumed that the NO3- contamination in the D3pl karst aquifer is likely only close to its distribution margins, where groundwater table is deeper than the top of the aquifer. This work was supported by the EU Interreg Est–Lat program project GroundEco No. Est-Lat62, and base funding grant from the Latvian Ministry of Education and Science to the University of Latvia, No. ZD2016/AZ03. References European Environment Agency (2018) Corine Land Cover 2018. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download (CLC). Accessed 1 Jun 2020 Popovs K, Kalvāns A, Jemeljanova M, et al (under review) Bedrock surface topography map of Latvia. J Maps Popovs K, Saks T, Jātnieks J (2015) A comprehensive approach to the 3D geological modelling of sedimentary basins: example of Latvia, the central part of the Baltic Basin. Est J Earth Sci 64:173–188. https://doi.org/10.3176/earth.2015.25

opencc-by-4.0Aug 2021View details →

Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power

This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.