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1,265 results for “discharges”
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
A 3D kinetic Monte Carlo study of streamer discharges in CO2
<p>The dataset contains compilable files and input files for the paper "A 3D kinetic Monte Carlo study of streamer discharges in CO2".</p>
Discharge of springs monitored during the WABEsense project
<p>Water discharge and temperature of the springs monitored during the <a href="https://www.aramis.admin.ch/Grunddaten/?ProjectID=47484">WABEsense project (UTF 642.21.20)</a>.</p><p>The data corresponding to the all field measurements performed between Feb. 2021 and Dec 2023. Data for each spring might not cover the whole period.</p><p>For each spring there are two files: *.csv and *.meta. The .csv file contains the recorded data. The .meta fiel contains further information about the spring (e.g. location) and the data.</p><p>## List of springs:</p><p>- Bonaduz BS Paliu Fravi<br>- Bonaduz BS Salums Friedrich<br>- Bonaduz BS Salums Leo<br>- Bonaduz SS Leo Friedrich<br>- Bregaglia BS Acqua d'Balz 1<br>- Bregaglia BS Acqua d'Balz 2<br>- Hergiswil BS Rossmoos<br>- Hergiswil SS Muesli<br>- Oberriet SS Ulrika<br>- Schiers BS Chalta Wasser<br>- Schiers BS Grapp rechts<br>- Susch BS Prada bella suot<br>- Susch BS Prada bella sura<br>- Zernez BS Sarsura<br>- Zug SS Nidfuren</p><p>More details about each spring is given in the corresponding .meta file.</p><p>We are very grateful for the work of the operators responsible for collecting the data.</p><p>## Files formats</p><p>All files are plain text. The .csv files are UTF-8 encoded and separated by ";" (semi-colon) . The .meta files follow the YAML format.</p><p>An example of loading the data in python using pandas is given below:</p><p>import pandas as pd data = pd.read_csv("Bonaduz.BS.Paliu_Fravi_discharge.csv", encoding="utf-8", sep=";")</p><p> </p>
Subnanosecond-electromagnetic-pulse-generated-by-a-long-spark-discharge:-Lightning-implication-data
<p><strong>Data description</strong></p><p>The data is used in the paper "Subnanosecond electromagnetic pulse generated by a long spark discharge: Lightning implication" (M. Gushchin et. al.) submitted in December 2023 in Geophysical Research Letters. Two types of files are presented. First are photos stored in "png" format. Second are waveforms stored in text files. First column is time and second is value. The delimiter is ";".</p><p><strong>Data is used in second figure</strong></p><p>Figure_2a.png -- Photo of the the appearance and growth of leaders with their streamer zones from the upper (HV) electrode</p><p>Figure_2b.png – First flash on the lower (grounded) electrode.</p><p>Figure_2c.png -- Common streamer zone formation after the upward leader starts.</p><p>Figure_2d.png -- Current increase in downward and upward leader channels, reduction in the size of the common streamer zone.</p><p>Figure_2e.png -- Discharge main stage.</p><p>Figure_2f_curve_1.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2a.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_2.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2b.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_3.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2c.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_4.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2e.png" photo. Time unit is mks, value unit is a.u.</p><p>Figure_2f_curve_5.dat -- Voltage waveform from the capacitive probe corresponds to "Figure_2f.png" photo. Time unit is mks, value unit is a.u.</p><p><strong>Data is used in third figure</strong></p><p>Figure_3b_curve1.dat -- The power waveform from RF analyzer f0 = 6 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve2.dat -- The power waveform from RF analyzer f0 = 5.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve3.dat -- The power waveform from RF analyzer f0 = 4.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve4.dat -- The power waveform from RF analyzer f0 = 3.5 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve5.dat -- The power waveform from RF analyzer f0 = 2 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3b_curve6.dat -- The power waveform from RF analyzer f0 = 1 GHz, df = 40 MHz. Time unit is mks, value unit is dB.</p><p>Figure_3a_curve1.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve1.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve2.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve2.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve3.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve3.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve4.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve4.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve5.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve5.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3a_curve6.dat -- The voltage pulse waveforms from a capacitive probe corresponds to "Figure_3b_curve6.dat" waveform. Time unit is mks, value unit is a.u.</p><p>Figure_3c.dat -- Waveform obtained using TEMH. Time unit is mks, value unit is V/m.</p><p>Figure_3d.dat – Detailed waveform obtained using TEMH. Time unit is ns, value unit is V/m.</p><p> </p><p><strong>Data is used in fourth figure</strong></p><p>Figure_4b_curve_1.dat – TPMP waveform obtained during calibration. Time unit is ns, value unit is A/m.</p><p>Figure_4b_curve_2.dat – IPPL waveform obtained during calibration. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_1.dat – TEMH waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4c_curve_2.dat – IPPL waveform obtained in shot #104 at 12-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_1.dat – TEMH waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4d_curve_2.dat – IPPL waveform obtained in shot #19 at 13-oct-22. Time unit is ns, value unit is E/m.</p><p>Figure_4e_curve_1.dat – TPMP waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4e_curve_2.dat – IPPL waveform obtained in shot #19 at 25-sept-23. Time unit is ns, value unit is E/m.</p><p>Figure_4f_curve_1.dat – TPMP waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is A/m.</p><p>Figure_4f_curve_2.dat – IPPL waveform obtained in shot #2 at 27-sept-23. Time unit is ns, value unit is E/m.</p><p><strong>Data is used in fifth figure.</strong></p><p>Figure_5a.png – The photo of negative discharge</p><p>Figure_5b.dat -- Waveform from a capacitive probe. Time units is mks, value units is a.u.</p><p>Figure_5c.dat -- The power waveform from RF analyzer f0 = 0.98 GHz, df = 40 MHz obtained simultaneously with Figure_5b.dat. Time unit is mks, value unit is dB. </p><p>Figure_5d.dat -- UWB EMP waveform obtained using TEMH obtained simultaneously with Figure_5c.dat. Time unit is ns, value unit is V/m.</p>
Daily continental discharge data for Bay of Bengal
Open the record for dataset details and reuse information.
Ecologically mediated differences in electric organ discharge drive evolution in a sodium channel gene in South American electric fishes
<p>Active electroreception — the ability to detect objects and communicate with conspecifics via the detection and generation of electric organ discharges (EODs) — has evolved convergently in several fish lineages. South American electric fishes (Gymnotiformes) are a highly species-rich group, possibly in part due to evolution of an electric organ (EO) that produces diverse EODs. Neofunctionalization of a voltage-gated sodium channel accompanied the evolution of electrogenic tissue from muscle and resulted in a novel gene (scn4aa) uniquely expressed in the EO. Here, we investigate the link between variation in scn4aa and differences in EOD waveform. We combine gymnotiform scn4aa sequences encoding the C-terminus of the Nav1.4a protein with biogeographic data and EOD recordings. We test whether physiological transitions among EOD types accompany differential selection pressures on scn4aa. We found positive selection on scn4aa coincided with shifts in EOD types. Species that evolved in the absence of predators, which likely selected for reduced EOD complexity, exhibited increased scn4aa evolutionary rates. We model mutations in the protein that may underlie changes in protein function and discuss our findings in the context of gymnotiform signalling ecology. Together, this work sheds light on the selective forces underpinning major evolutionary transitions in electric signal production.</p>
Discharge spectrum of Acetonitrile
<p>Discharge experiment of acetonitrile taken with acetonitrile in an external reservoir and a backing gas of Ar/He flowed over the reservoir. The discharge was performed with a 1 kV discharge.</p>
Daily flood discharge dataset for 10 basins in the Third Pole during 1981‒2100
<p><span>This dataset describes the daily discharge during each river flood event in the 10 Third Pole basins (Indus, Yamuna, Upper Ganges, MahaKali, Karnali, Gandaki, Koshi, Brahmaputra, Salween, and Mekong) in the historical (1981</span><span>‒</span><span>2020) and future (2021</span><span>‒</span><span>2100). This was achieved using a hybrid model encompassing a validated physical model (Water and Energy Budget-based Distributed Hydrological Model, WEB-DHM) and deep-learning model (<a name="_Hlk153977673"></a>Long Short-Term Memory model, LSTM) with the latest climate projections. </span></p> <p><span>River flood events are defined using both the annual-maximum approach and peak-over-threshold (POT) approach. Details for identification of the annual-maximum and POT flood events are described in the file “readme.txt”. Future data was generated based on the climate projections </span><span>from five climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and </span><span>UKESM1-0-LL</span><span>) in phase 6 of the Coupled Model Intercomparison Project (CMIP6) under </span><span>two shared socio-economic pathway scenarios (SSP)</span><span> (a high-emission scenario of SSP585 and a low-emission scenario of SSP245). The unit of discharge is m<sup>3</sup>/s.</span></p>
Dataset for "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", Part II
<p>This is Part II of the output dataset from coupled ice sheet-climate model simulations that investigate the interactions between ice sheet freshwater flux and the warming climate. Description of models, coupling scheme, and design of these simulations is provided in a paper titled "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", which is currently under peer review. More information will be updated when available.</p>
Dataset for "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", Part I
<p>This is Part I of the output dataset from coupled ice sheet-climate model simulations that investigate the interactions between ice sheet freshwater flux and the warming climate. Description of models, coupling scheme, and design of these simulations is provided in a paper titled "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", which is currently under peer review. More information will be updated when available.</p>
Results Discharge Observation Uncertainty and Model Performance Pub
<p><br>Dataset for Publication:<br>On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance</p> <p>Authors and Affiliations:<br>- Jerom P. M. Aerts<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Jannis M. Hoch<br> Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br> Fathom, Bristol, United Kingdom<br>- Gemma Coxon<br> Geographical Sciences, University of Bristol, Bristol, United Kingdom<br>- Nick C. van de Giesen<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Rolf W. Hut<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands</p> <p>Correspondence:<br>For inquiries about the dataset or publication, contact:<br>Jerom P. M. Aerts<br>Email: j.p.m.aerts@tudelft.nl</p> <p>---</p> <p>Overview:<br>This dataset supports the research article, "On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance."<br>It includes outputs and analyses from hydrological models and observational data across multiple flow categories and performance metrics. The dataset enables replication of the analyses and insights into the impacts of discharge observation uncertainty on model evaluation.</p> <p>---</p> <p>Dataset Structure:</p> <p>Folder: Model_Results<br>Contains results from hydrological model runs and associated analyses.</p> <p>Subfolders:</p> <p>1. MARRMoT_Models<br> - Contains results for six hydrological models implemented using the MARRMoT framework.</p> <p>2. PCR-GLOBWB<br> - Includes results for the PCR-GLOBWB hydrological model.</p> <p>3. wflow_sbm<br> - Contains results for the wflow_sbm hydrological model.</p> <p>Each Subfolder Contains:</p> <p>- Flow Categories<br> - Results categorized by flow regime:<br> - Low_Flow<br> - Average_Flow<br> - High_Flow</p> <p>- Gumboot Temporal Sampling Analysis<br> - Results of temporal sampling analyses conducted using the Gumboot method.</p> <p>- Objective Functions<br> - Files containing model performance metrics based on different objective functions.</p> <p>- Observations<br> - Observed discharge time series data used for model calibration and validation.</p> <p>- Simulation Time Series<br> - Simulated discharge time series generated by the models.</p> <p>---</p> <p>Data Description:</p> <p>- File Formats<br> - All data files are in CSV format with UTF-8 encoding.</p> <p>- Variable Descriptions<br> - Date: YYYY-MM-DD<br> - Discharge Values: Cubic meters per second (m³/s).<br> - Flow Categories: Indicates if data corresponds to low, average, or high flow conditions.</p> <p>- Performance Metrics:<br> - Includes common hydrological metrics (e.g., Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency) computed for each model and flow category.</p> <p>---</p> <p>Usage Guidelines:</p> <p>Citation:<br>If you use this dataset in your research, please cite the publication as follows:<br>> Aerts, J. P. M., Hoch, J. M., Coxon, G., van de Giesen, N. C., & Hut, R. W. (2024). On the importance of discharge observation uncertainty when interpreting hydrological model performance. [Journal Name, Volume, Pages]. DOI: [Insert DOI].</p> <p>Licensing:<br>This dataset is made available under the [Insert License, e.g., Creative Commons Attribution 4.0 International License (CC BY 4.0)].</p> <p>---</p> <p>Contact:<br>For further questions, feel free to contact:<br>Jerom P. M. Aerts<br>j.p.m.aerts@tudelft.nl</p>
WBM Output daily discharge and water temperature all GCMs
<p>The WBM model was run at daily temporal resolution over a 6 arc min. spatial grid river network (WECC Domain v.3a) using the monthly time series (2006-2099) CMIP-5 climate drivers as an input:</p> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <p>In addition the model was run using the NASA MERRA2 climate drivers for historical time series (1980-2018).</p> <p>Two WBM output variables (Discharge and Water Temperature) have been extracted (sampled) for the 715 power plants of the WECC database using their WBM river-network (snapped, Lat Lon) locations which were derived from their original site locations. Extracted point data represents daily time series for 55 years (2006-2099) of CMIP-5 WBM simulations and 39 years (1980-2018) in the case of the historical MERRA-2 run.</p> <p>Post-processing of this data is done for statistical quantification of water temperature and discharge pertinent to usage of water resources by WECC power plants as cooling agents of thermoelectric electricity production (e.g. cooling towers) that may have some regulatory limitations to the heating of river waters from return flows of the power plant cooling technological processes.</p>
Spectrum and Temperature Evolution of Lightning Channel and Dependence on Discharge Feature
<p>Temperature is one of the crucial parameters reflecting the energy and current transfer characteristics in the lightning discharge channel. According to the spectra of eight return strokes recorded simultaneously by two high-speed slitless spectrographs with different time resolutions, the spectral-structure and temperature evolution of the return stroke channels over time was quantitatively analyzed. Different from the previous report, one of the spectrographs has recorded the ionic lines in the spectra within approximately 200 microseconds during the return stroke. The ionic line intensity decayed rapidly with time as the current declined, while the atomic line intensity decreased more slowly. The spectral-structure evolution characteristics indicate that the ionic lines in the spectra existed throughout the discharge current process (including the continuing current (CC) stage). Additionally, it further suggests that the ionic line intensities are associated with the discharge currents and that their radiation mechanism is closely related to the collision excitation under the action of strong currents. The temperature calculated using the ionic lines can reflect the thermodynamic properties of the current-carrying channel. The temperature calculated using atomic lines is significantly lower than that calculated using the ionic lines in the same spectrum. The radiation mechanism of the atomic lines differs from that of the ionic lines. Compared with the decay of the ionic-line intensity, the decline of the channel temperature is slower, and there is even a slight increase in the CC stage. This property reflects the effect of the current action integral.</p>
Spectrum and Temperature Evolution of Lightning Channel and Dependence on Discharge Feature
<p>Temperature is one of the crucial parameters reflecting the energy and current transfer characteristics in the lightning discharge channel. According to the spectra of eight return strokes recorded simultaneously by two high-speed slitless spectrographs with different time resolutions, the spectral-structure and temperature evolution of the return stroke channels over time was quantitatively analyzed. Different from the previous report, one of the spectrographs has recorded the ionic lines in the spectra within approximately 200 microseconds during the return stroke. The ionic line intensity decayed rapidly with time as the current declined, while the atomic line intensity decreased more slowly. The spectral-structure evolution characteristics indicate that the ionic lines in the spectra existed throughout the discharge current process (including the continuing current (CC) stage). Additionally, it further suggests that the ionic line intensities are associated with the discharge currents and that their radiation mechanism is closely related to the collision excitation under the action of strong currents. The temperature calculated using the ionic lines can reflect the thermodynamic properties of the current-carrying channel. The temperature calculated using atomic lines is significantly lower than that calculated using the ionic lines in the same spectrum. The radiation mechanism of the atomic lines differs from that of the ionic lines. Compared with the decay of the ionic-line intensity, the decline of the channel temperature is slower, and there is even a slight increase in the CC stage. This property reflects the effect of the current action integral.</p>
Data corresponding to "The Impact of Multi-sensor Land Data Assimilation on River Discharge Estimation"
<p>This dataset is corresponding to the input and output files that were used in this study:</p> <p>Wu, W.-Y., Z.-L. Yang, L. Zhao, P. Lin (2022), Joint Multi-sensor Data Assimilation for Constraining Water Storages and its Impact on Global Discharge Estimation (<em>in revision, RSE</em>)</p>
Data Availability - DRAFT - 'Process-based similarity' revealed by discharge-dependent relative submergence dynamics of thousands of large bed elements
<p>Datasets and R code related to confidential manuscript submission entitled "‘Process-based similarity’ revealed by discharge-dependent relative submergence dynamics of thousands of large bed elements". Restrictions apply to the availability of the 2014 DTM and 2D model results, which were used under contractual agreement from the project sponsor. These are available from the senior author with the permission of Yuba Water Agency. Restrictions apply to the availability of the 2014 DTM and 2D model results, which were used under contractual agreement from the project sponsor. These are available from the senior author with the permission of Yuba Water Agency.</p>
Data supporting On the step type of continuous propagating positive leader in laboratory-scale long spark discharges
<p>The data supports the manuscript entitled "On the step type of continuous propagating positive leader in laboratory-scale long spark discharges". The *.txt file contains the applied voltage waveform and discharge current waveform. The*.doc file contains the data for Figure 5. The *.rar file contains the high-speed video frames, and can be decompressed and opened. The data can be used freely for scientific purposes with appropriate citation.</p>
Effect of freshwater discharge from estuary dam on residual circulation in bifurcated channel
<p>Zip-files contain matlab m-files and raw data of transects data in Yeoungsan Rver estuary. The code was designed to analysis the changes from the raw data.</p> <p>During freshwater discharge.zip file contains data set with a freshwater impact from a estuary dam,</p> <p>Whereas during non-freshwater discharge.zip file contains data set without a freshwater impacts period.</p> <p> </p> <p> </p>
Data from: Experimental assessment of downstream environmental DNA patterns under variable fish biomass and river discharge rates
<p><span>The development of environmental DNA (eDNA) methods towards implementation as a cost-effective, nonlethal tool for fish biomonitoring in lotic environments requires insights on the temporal and spatial distribution of eDNA in river systems. Yet, little is known on how downstream eDNA dispersal is affected by the combination of river discharge and source biomass effects. In this study, we aimed at unravelling the effect of source- and system-specific processes on the stream reach of eDNA. We used a longitudinal cage study in two river sections characterized by a significantly different discharge rate, where two invasive and two native fish species were introduced under two contrasting biomass treatments. Using droplet digital PCR (ddPCR) analyses, we found that eDNA concentrations become strongly reduced 2 km downstream from the source, an effect that is strengthened with increasing river discharge and coinciding dilution effects. Higher discharge rates resulted in equal or even higher detection probabilities at increasing distance from the source. The introduction of high fish stock biomass resulted in an increase of eDNA concentrations, as well as detection probabilities and in parallel reduced the stochasticity of the measurements. A peak in eDNA concentrations at a downstream distance ranging between 300 m and 2 km confirms the complexity of plume-shaped downstream eDNA patterns. Our results showed interspecific variation in eDNA emission and suggest species-specific differences in eDNA persistence. This study underlines the impact of both river discharge rate and source biomass on downstream eDNA detection and dispersal patterns. </span></p>
Control of groundwater-lake interaction zone structure on spatial variability of lacustrine groundwater discharge
<p>This is the paper "Control of groundwater-lake interaction zone structure on spatial variability of lacustrine groundwater. Data set of discharge ". The data includes various isotopes, water temperature and conductivity data for lake and groundwater, wind speed and air temperature data during the study period, and water depth measurements.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.