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128 results for “Water flow”
Fig. 3 in Distribution of Agonostomus monticola and Brycon behreae in the Río Grande de Térraba, Costa Rica and relations with water flow
Fig. 3. Capture per unit effort (CPUE) by month for Brycon behreae recruits in the Térraba River and three tributaries.
Fig. 4 in Distribution of Agonostomus monticola and Brycon behreae in the Río Grande de Térraba, Costa Rica and relations with water flow
Fig. 4. Capture per unit effort (CPUE) by month for Agonostomus monticola recruits in the Térraba River and three tributaries.
Fig. 2. Water level oscillation measured between August 1998 and July 2000 in Flow seasonality and fish assemblage in a tropical river, French Guiana, South America
Fig. 2. Water level oscillation measured between August 1998 and July 2000 at the Hydrological station on the Comté River. The numbers indicate the mean water level during the month of sampling.
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Data from: Fish communicate with water flow to enhance a school's social network
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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
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Bridging the flux gap: sap flow measurements reveal species-specific patterns of water-use in a tallgrass prairie
<p>Predicting the hydrological consequences following changes in grassland vegetation type (i.e., woody encroachment) requires an understanding of water flux dynamics at high spatiotemporal resolution for predominant species within grassland communities. However, grassland fluxes are typically measured at the leaf or landscape scale, which inhibits our ability to predict how individual species contribute to changing ecosystem fluxes. We used external heat balance sap flow sensors and a hierarchical Bayesian state-space modeling approach to bridge this "flux-gap" and estimate continuous species-level water flux in common tallgrass prairie species. Specifically, we asked: 1) How do diurnal and nocturnal water fluxes differ among woody and herbaceous plants? (2) How sensitive are woody and herbaceous species to environmental drivers of diurnal and nocturnal water flux? We highlight three results: (1) <i>Cornus drummondii</i>, the primary woody encroacher in this grassland, exhibited the greatest canopy-level water loss, (2) nocturnal transpiration was a large component of the water lost in this ecosystem and was driven primarily by C<sub>4</sub> grasses and <i>C. drummondii</i>, and (3) the sensitivity of canopy transpiration to environmental drivers varies among plant functional types and throughout a 24-hour period. Our data reveal important insights regarding the water-use strategies of woody versus herbaceous species in tallgrass prairies, and about the potential hydrological consequences of ongoing woody encroachment. We suggest that the high, static flux rates observed in woody species will likely deplete deep water stores over time, potentially creating hydrological deficits in grasslands experiencing woody encroachment and concomitantly increasing the vulnerability of these ecosystems to drought.</p>
US Water Network Observed and Inferred Flows
<p>This dataset provides a synthesized national record of river gauges. It includes inferred flows which are based on the structure of the river network and empirical flow relationships. In total, the network contains 22619 gauges (or virtual junction gauges) and over 1 million years of monthly data.</p> <p>The dataset is provide as a saved data file for R, flowdata.RData, containing two variables: nodes and allflow.</p> <p>`nodes` describes each gauges or virtual junction gauge in the water flow network, representing a combination of gauges from multiple sources. The source is specified by the `collection` column, as follows:</p> <ul> <li>`rivdis`: From the RivDIS dataset, at http://iridl.ldeo.columbia.edu/SOURCES/.UNH/.CSRC/.RivDIS/index.html?Set-Language=en</li> <li>`usgs`: GAGES II river gauge</li> <li>`reservoir`: National Inventory of Dams reservoir</li> <li>`usgsres`: USGS gauged reservoir</li> <li>`canal`: Canal cross county boundaries</li> <li>`junction`: Junction node added to capture the structure of rivers from HydroSHEDS</li> </ul> <p>The `colid` specifies the ID of the gauge within its collection, and combined `collection` and `colid` are unique. The location of the gauge is specified by the latitude `lat` and longitude `lon`. The elevation is provided in meters in column `elev`.</p> <p>The flow data is contained in the `allflow` matrix. This matrix has a column for each node (as many columns as there are rows in `nodes)`, and a row for each month starting from Jan. 1915. All the flows are in units of m^3 / s.</p>
Dataset for "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography"
<p>This dataset includes soil temperature, moisture, and electrical conductivity measurements taken at a depth of approximately 50 cm, as well as the digital elevation model, electrical resistivity tomography, and self-potential data used in the manuscript "Mapping Water Flow Pathways in the Fengjiaping Landslide Using Self-Potential and Electrical Resistivity Tomography" submitted to <em>Comptes Rendus Geoscience</em>.</p>
Dataset of paper "Wavelength synergistic effects in continuous flow-through water disinfection systems"
<p>Dataset of paper "Wavelength synergistic effects in continuous flow-through water disinfection systems"</p>
Dissolved organic carbon concentrations, pH and conductivity of water flowing from eroding and restored peatland catchments
<p>Water was collected from gullies within an eroding blanket bog. The bog is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl). </p> <p><span>At Balmoral DOC concentrations were measured from water flowing through six v-notch weirs. Three weirs measured drainage from mini catchments that had undergone restoration and three measured drainage from<span> </span>degraded mini-catchments with multiple upstream erosion gullies and bare peat. Restoration at this site included reprofiling and vegetating (turving) of peat haggs, bunding using coir logs to ‘slow the<span> </span>flow’ and encourage </span><em><span>Sphagnum</span></em><span> growth, and mulching of areas of bare peat with locally sourced vegetation.</span></p> <p><span>The six V-notch weirs conforming to British Standard 3680:Part 4A:1981 </span><span>(British Standards Institute, 1981)</span><span> were constructed from 18 mm marine plywood. Initially 90</span><span>°</span><span>, 65 l s<sup>-1</sup>, V-notch plates cut from 1 mm aluminium plate were fitted. Weirs were installed at sites identified in the experimental design phase. Each weir was embedded into the peat by 20cm vertically and 20-40cm horizontally and supported by two posts embedded 60-80cm into the peat. Where required the weirs were extended to ensure that there were no leaks between the bank and the weir. A stilling well equipped with a capacitive water logger was installed at each weir. The stilling wells were manufactured from 800 mm long, 43 mm diameter, ABS waste pipe tubing. After a period of evaluation (November 2020 – May 2021) the 90</span><span>°</span><span>, 65 l/s, V-notch plates were replaced with 28.4</span><span>°</span><span>, 15 l s<sup>-1</sup> plates in order to improve low-flow (<6cm head) accuracy. The water loggers were set to record water levels every 10 minutes. Raw data was downloaded every 6 months and processed in a Python script using the BS 3680 formula.</span></p> <p><span><span>Water samples were taken from each weir (if water was present behind the weir on the sampling day)<span> </span>over a two year period from September 2021 to September 2023<span> </span>After samples are delivered to the laboratory the protocol for wet chemistry analysis follows the protocol of the National Water Inventory of Scotland (NWIS) project. pH, conductivity and turbidity were then measured on all samples before a 100ml subsample was filtered through a 0.45 </span><span>µ</span><span>m membrane. The remaining unfiltered sample is stored in a cold room and the filter membrane was retained, air dried and stored in plastic bags, for potential further analysis. The filtered water was analysed for DOC on a Skalar TOC analyser (Norcross, USA). </span></span></p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
Continuous Flow Synthesis of Iron Oxide Nanoparticles Using Water-in-Oil Microemulsion_experimental_dataset
<p>This dataset contains the raw experimental data to the article Sopoušek et al., Continuous Flow Synthesis of Iron Oxide Nanoparticles<br> Using Water-in-Oil Microemulsion, Colloid Journal, 2020, Vol. 82, No. 6, pp. 727–734. </p>
Flowing days Derived from CubeSat Imagery and Ground Observations in Hassayampa River (HR), Arizona from 2019-2021 (Water Years)
<p>Flowing days derived from CubeSat imagery and ground observations in Hassayampa River (HR), Arizona from October 2018 to September 2021. This database supports the following paper:</p> <div> <div>Wang, Z., & Vivoni, E. R. (2022). Detecting streamflow in dryland rivers using CubeSats. <em>Geophysical Research Letters</em>, 49, e2022GL098729. <a href="https://doi.org/10.1029/2022GL098729">https://doi.org/10.1029/2022GL098729</a></div> </div> <div> <div> </div> </div> <p>This database includes three folders.</p> <ol> <li>GroundObservations: Ground observations along the HR retrieved from USGS website (https://waterdata.usgs.gov/nwis) and Flood Control District of Maricopa County using Single Sensor Data Reports tool (https://alert.fcd.maricopa.gov/showrpts_mc.html): <ol> <li><strong>Precip</strong>: Records of daily precipitation amount.</li> <li><strong>Stream</strong>: Records of daily streamflow amount.</li> </ol> </li> <li>GIS: GIS layers used in deriving flowing days. <ol> <li><strong>Subreach</strong>: Shapefiles of nine sub reaches along the HR.</li> <li><strong>Buffer segment</strong>: Buffer zones perpendicular to HR reaches at 90 m resolution. Column ID is sorted in ascending order along the HR.</li> <li><strong>Channel masks: </strong>Channel masks of HR derived from Planet data.</li> </ol> </li> <li>Results <ol> <li><strong>BufferSegment_Flowdays.xlsx:</strong> Days with flow determined using the NIR difference threshold for water years (WY) 2019 to 2021 for each 90 m buffer areas. The relative location of each buffer areas can be found in the Buffer Segment GIS layer. ‘NaN’ suggests no data.</li> </ol> </li> </ol> <div> <div> </div> </div>
Fig. 2 in Distribution of Agonostomus monticola and Brycon behreae in the Río Grande de Térraba, Costa Rica and relations with water flow
Fig. 2. Month flow variation of study sites.
Dataset for the Figure 2 in manuscript "Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles"
<p>This dataset includes the gathered experimental measurements of a freezing test by Wu (2017) for the model's verification shown in Figure 2 of the manuscript entitled 'Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles'' by Huang, X., and Rudolph, D.L.</p>
Physiology of the widespread pulsating soft coral Xenia umbellata is affected by food sources, but not by water flow
<p>Coral energy and nutrient acquisition strategies are complex and sensitive to environmental conditions such as water flow. While high water flow can enhance feeding in hard corals, knowledge about the effects of water flow on the feeding of soft corals, particularly those pulsating, is still limited. In this study, we thus investigated the effects of feeding and water flow on the physiology of the pulsating soft coral <em>Xenia</em> <em>umbellata</em>. We crossed three feeding treatments i) no feeding, ii) particulate organic matter [POM] as phytoplankton, and iii) dissolved organic carbon [DOC] as glucose, with four water volume exchange rates (200, 350, 500 and 650 Lh<sup>-1</sup>) over 15 days. Various ecophysiological parameters were assessed including pulsation rate, growth rate, isotopic and elemental ratios of carbon (C) and nitrogen (N) as well as photo-physiological parameters of the Symbiodiniaceae (cell density, chlorophyll-<em>a</em> and mitotic index). Water flow had no significant effect but feeding had a substantial impact on the physiology of the <em>X. umbellata </em>holobiont. In the absence of food, corals exhibited significantly lower pulsation rates, lower Symbiodiniaceae cell density, and lower mitotic indices compared to the fed treatments, yet significantly higher chlorophyll-<em>a</em> per cell and total N content. Differences were also observed between the two feeding treatments, with significantly higher pulsation rates and lower chlorophyll-a per cell in the DOC treatment, but higher C and N content in the POM treatment. Our findings suggest that the <em>X. umbellata</em> holobiont can be viable under different trophic strategies, though favouring mixotrophy. Additionally, the physiology of the <em>X. umbellata</em> may be regulated through its own pulsating behaviour without any positive nor negative effects from different water flow. Thus, this study contributes to our understanding of soft coral ecology, particularly regarding the competitive success and widespread distribution of <em>X. umbellata</em>.</p>
Time series of flow measurement and water quality monitoring at a large combined sewer overflow in Berlin
<p>The table contains 3 years CSO monitoring time series, already split in 22 separated CSO events. All details about the monitoring set up in the papers and reports indicated below.</p> <p>Fields:</p> <ul> <li>evtID: event ID</li> <li>myDateTime; date/time</li> <li>v: m/s velocity</li> <li>Q: m³/s flow</li> <li>TSS: mg/l TSS concentration</li> <li>COD: mg/l COD concentration</li> <li>CODf: mg/l dissolved COD concentration</li> <li>EC: µS/cm electric conductivity</li> <li>NH4_N: mg/l NH4_N concentration</li> </ul> <p>TSS and COD have been measured with a spectrometer using a linear local calibration as presented in Lepot et al., 2016.</p> <p>ore information about the monitoring set up in </p> <p>Sandoval, S., Torres, A., Pawlowsky-Reusing, E., Riechel, M., Caradot, N. (2013): The evaluation of rainfall influence on CSO characteristics: the Berlin case study. Water Science & Technology Vol. 68 (12): 2683-2690 10.2166/wst.2013.524</p> <p>Caradot, N. (2012): Continuous Monitoring of Combined Sewer Overflows in the Sewer and the Receiving River: Return on Experience. Kompetenzzentrum Wasser Berlin gGmbH; MIA-CSO Project Report</p> <p>Riechel, M., Matzinger, A., Pawlowsky-Reusing, E., Sonnenberg, H., Uldack, M., Heinzmann, B., Caradot, N., von Seggern, D., Rouault, P. (2016): Impacts of combined sewer overflows on a large urban river - Understanding the effect of different management strategies. Water Research 105: 264-273 10.1016/j.watres.2016.08.017</p> <p>Caradot, N., Sonnenberg, H., Riechel, M., Matzinger, A., Rouault, P. (2013): The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring. Water Practice & Technology Vol 8 (No 3-4): 417-425 10.2166/wpt.2013.042</p> <p>Lepot, M., Torres, A., Hofer, T., Caradot, N., Gruber, G., Aubin, J.-B., Bertrand-Krajewski, J.-L. (2016): Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers. Water Research 101 (15 September 2016): 519-534 10.1016/j.watres.2016.05.070</p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
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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.