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486 results for “water use”
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper. </p>
Data from: Overestimated gains in water-use efficiency by global forests
<p><span>Increases in terrestrial water use efficiency (WUE) have been reported in many studies, pointing to potential changes in physiological forcing of global carbon and hydrological cycles. However, gains in WUE are of uncertain magnitude over longer (i.e. >10 years) periods of time largely owing to difficulties in accounting for structural and physiological acclimation. <sup>13</sup>C signatures (i.e., δ<sup>13</sup>C) of plant organic matter have long been used to estimate WUE at temporal scales ranging from days to centuries. Mesophyll conductance is a key uncertainty in estimated WUE owing to its influence on diffusion of CO<sub>2</sub> to sites of carboxylation. Here we apply new knowledge of mesophyll conductance to 464 δ<sup>13</sup>C chronologies in tree-rings of 143 species spanning global biomes. Adjusted for mesophyll conductance, gains in WUE during the 20th century (0.15 ppm y<sup>-1</sup>) were considerably smaller than those estimated from conventional modelling (0.26 ppm y<sup>-1</sup>). Across the globe, mean sensitivity of WUE to atmospheric CO<sub>2</sub> was 0.15 ppm ppm<sup>-1</sup>. Ratios of internal-to-atmospheric CO<sub>2</sub> (on a mole fraction basis; <em>c</em><sub>i</sub>/<em>c</em><sub>a</sub>) in leaves were mostly constant over time but differed among biomes and plant taxa – highlighting the significance of both plant structure and physiology. Together with synchronized responses in stomatal and mesophyll conductance, our results suggest that ratios of chloroplastic-to-atmospheric CO<sub>2</sub> (<em>c</em><sub>c</sub>/<em>c</em><sub>a</sub>) are constrained over time</span><span>. We conclude that forest WUE may have not increased as much as previously suggested and that projections of future climate forcing via CO<sub>2</sub> fertilization may need to be adjusted accordingly</span><span>. </span></p>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Leaf traits predict water‐use eficiency in U.S. Pacific Northwest grasslands under rain exclusion treatment
<p>Does drought stress in temperate grasslands alter the relationship between plant structure and function? Here we report data from an experiment focusing on growth form and species traits that affect the critical functions of water‐ and nutrient‐use efficiency in prairie and pasture plant communities. A total of 139 individuals of 12 species (11 genera and four families) were sampled in replicated plots maintained for three years across a 520-km latitudinal gradient in the Pacific Northwest, USA. Rain exclusion did not alter the interspecific relationship between foliar traits and stoichiometry or intrinsic water‐use efficiency (iWUE). Rain exclusion reduced iWUE in grasses, and effect was primarily species‐specific, although leaf morphology, life history strategy, and phylogenetic distance predicted iWUE for all 12 species when analyzed together. Variation in specific leaf area explained most of the variation in iWUE between different functional groups, with annual forbs and annual grasses at opposite ends of the resource‐use spectrum. Our findings are consistent with expected trait‐driven tradeoffs between productivity and resource‐use efficiency and provide insight into strategies for the sustainable use and conservation of temperate grasslands.</p>
Spatio-temporal variation in deep soil water use patterns of overstory and understory layers in subtropical plantations predict community assembly
<p><span>1. </span><span>Deep soil water utilization allows plants to cope with drought stress. However, little is known about the roles of the understory layers in driving spatio-temporal variations of deep soil water in forests and how the patterns of deep soil water use among life forms contribute to community assembly processes.</span></p> <p><span>2. </span><span>We assessed the spatio-temporal patterns and determinants of deep water utilization of tree, shrub and herb layers in subtropical coniferous plantations and investigated associations between deep water use parameters and dominance and richness of understory vegetation. </span></p> <p><span>3. </span><span>We found that the understory layer had a higher reliance on deep soil water in the dry season, a larger seasonal plasticity of deep soil water uptake, but lower spatial variability in deep soil water utilization than the tree layer. We showed that greater reliance of the tree layer on deep soil water was associated with decreased shrub layer diversity, whereas greater reliance of the shrub layer on deep water was associated with increased herb layer diversity. </span></p> <p><span>4. </span><span>Synthesis</span><span>. Our results highlight the roles of understory layers in driving the temporal dynamics of deep soil water in forests and improve our understanding of how deep soil water use patterns amongst life forms shape community assembly in forests.</span></p>
The natural abundance of stable water isotopes method may overestimate deep-layer soil water use by trees
<p>The dataset is the basic data of the author's paper 'The natural abundance of stable water isotopes method may overestimate deep-layer soil water use by trees'. The main content of this paper is to study the water use strategy of trees in deep vadose zone regions by first identifying the soil layer depths from which trees derive their water source using isotopic labeling in deep layers and then calculating water sources based on the natural abundance of stable isotopes. We also compared the results with the natural abundance of stable water isotopes method. Taking apple plantation as an example, the data set includes the soil moisture, isotopic value in soil water, xylem water and precipitation.</p>
Geospatial data used in "Estimation of river water surface elevation using UAV photogrammetry and machine learning"
<p>Geospatial data used in article "Estimation of river water surface elevation using UAV photogrammetry and machine learning" by Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch and Paweł Ćwiąkała (AGH UST).</p> <p>Each zip archive contains the following files:</p> <ul> <li>dsm.tif - raster of digital surface model,</li> <li>ortho.tif - raster of orthophoto,</li> <li>gnss_wse.json - geojson multipoint shape containing RTN GNSS measurements of water surface elevation,</li> <li>grid.json - geojson multipolygon shape containing square areas of samples used in deep learning solution.</li> <li>centerline.json - geojson multipoint shape containing values sampled from DSM along centerline,</li> <li>wateredge.json - geojson multipoint shape containing values sampled from DSM along "water-edge".</li> </ul> <p>Data in AMO18.zip archive was collected by Bandini et. al (https://doi.org/10.5281/zenodo.3519888).</p> <p>Preprocessed machine learning dataset and source codes are available in github repository at: https://github.com/radekszostak/river-wse-uav-ml</p>
A Global Gridded Municipal Water Withdrawal Estimation Method Using Aggregated Data and Artificial Neural Network
<p>Global gridded municipal water withdrawal estimations for the following WST paper.</p> <p>Jiabao Yan, Shaofeng Jia; A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network. <em>Water Science Technology</em>, 2023; 87 (1): 251–274. <a href="https://doi.org/10.2166/wst.2022.399" target="_blank" rel="noopener">https://doi.org/10.2166/wst.2022.399</a></p> <p>The representative year of the data is 2015, and the unit of the data is in millimeters (mm).</p>
Determination of Raman spectra of water H2O and heavy water D2O using Thorlabs modular Raman kit
Open the record for dataset details and reuse information.
Monitoring the effect of water erosion on soil surface microtopography using Lidar
<p>This dataset contains three dimensional (3D) reconstructions of laboratory soil surfaces eroding under rainfall simulation experiments. Experiments were conducted using the Walnut Gulch Rainfall Simulator at the USDA-ARS Southwest Watershed Research Center in Tucson, Arizona. A terrestrial laser scanner was used to collect soil surface 3D data.</p>
Figure 2 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 2. Results of Cluster Analysis.
Figure 1 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 1. Rusałka Lake in Szczecin City, own elaboration, after Poleszczuk et al. (2012).
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
Data files and electromagnetic simulation software used in the paper "Radar evidence of subglacial liquid water on Mars" By Orosei et al. (2018)
<p>This archive contains radargrams, geometric information and visualizations of MARSIS radar observations over the area centered at 193°E, 81°S within Planum Australe, Mars, together with scripts and results of electromagnetic propagation simulations used to interpret the data. This archive contains everything needed to reproduce the results presented in the paper "Radar evidence of subglacial liquid water on Mars" by Orosei et al. (2018). The software made available in this archive consists of scripts written in the Matlab© computing language, and is provided "as is" without warranty of any kind, either express or implied. Queries on the content of the archive can be sent to Roberto Orosei (roberto.orosei@inaf.it).</p> <p>Three types of MARSIS data files are contained in this archive:</p> <p>* orbit_XXXXX_frequency_Y_MHz_radargram.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many columns as the number of radar echoes acquired during the orbit (usually 3200), and 980 lines, one for each echo sample. Values are samples of the uncalibrated echo voltage, without phase information (i.e. positive real numbers instead of complex echo samples). Echo samples are acquired every 0.3571 microseconds (2.8 MHz sampling rate). The first sample of an echo is located at a round-trip time corresponding to an altitude of 25 km above the Martian IAU ellipsoid.</p> <p>* orbit_XXXXX_frequency_Y_MHz_geometry.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many rows as the number of radar echoes acquired during the orbit (usually 3200), and contains the following auxiliary parameters:</p> <p> - EPHEMERIS TIME - Number of seconds elapsed since Jan 1, 2000, 12:00 UTC corresponding to the time at which data collection for the current echo started.</p> <p> - MARS SOLAR LONGITUDE - Angle between the Mars-Sun line at the time corresponding to EPHEMERIS TIME and the Mars-Sun line at the vernal equinox, in degrees.</p> <p> - MARS SUN DISTANCE - Distance from the centre of Mars to centre of the Sun at the time corresponding to EPHEMERIS TIME, in Km.</p> <p> - SPACECRAFT ALTITUDE - Distance from the Mars Express spacecraft to the reference surface of the target body measured normal to the surface at the time corresponding to EPHEMERIS TIME, expressed in Km.</p> <p> - SUB-SPACECRAFT LONGITUDE - East longitude of the point on the target body that lies closest to the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees and in the [ 0 -360 ] range.</p> <p> - SUB-SPACECRAFT LATITUDE - Planetocentric latitude of the point on the target body that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees.</p> <p> - RADIAL VELOCITY - Radial component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - TANGENTIAL VELOCITY - Tangential component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - LOCAL TRUE SOLAR TIME - Angle between the extension of the vector from the Sun to Mars and the projection on Mars' ecliptic plane of a vector from the center of the target body and the point on the target body surface that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed on a 24-hour clock with decimal fractions of the hour.</p> <p>* orbit_XXXXX_frequency_Y_MHz.png, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file is a visualization of the corresponding radargram, of the spacecraft ground track during the observation, and of surface and subsurface echo power.</p> <p>Electromagnetic propagation model and results consist of several files:</p> <p>* model_Pss_over_Ps_ratio.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust.</p> <p>* figure_S5.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust, either overlaid by or overlaying a layer of pure CO2 ice of variable thickness.</p> <p>* fftvars.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes vectors containing the correct time and frequency values for FFT computations.</p> <p>* genrefl.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the frequency-dependent complex electromagnetic reflectivity of a plane parallel stratigraphy at normal incidence.</p> <p>* GPR_1D_simulation.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It simulates the radar echo produced by the propagation of a broadband signal in a plane parallel stratigraphy.</p> <p>* matzler.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the relative dielectric permittivity of pure water ice (ice Ih) for any temperature and frequency according to formulas provided in Matzler (1998).</p> <p>* maxgarmix.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the effective dielectric constant of a medium containing intrusions of different dielectric properties according to the Maxwell-Garnett dielectring mixing model.</p> <p>* Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv is a comma-separated value file containing model results for subsurface to surface echo power ratio as a function of central frequency, basal temperature, dust content and basal permittivity. One file is produced for every value of the central frequency and of the dust fraction. The value of these two parameters for a given files are reported in the file name, where X is the frequency in MHz, and Y.YY is the dust fraction. Each file contains a matrix in which there is a row for every value of basal temperature from 170 K to 270 K, and a column for every value of basal permittivity from 4 to 100 with logarithmic spacing. Basal temperature and permittivity values are reported in different comma-separated value files called "basal_temperatures.csv" and "basal_permittivities.csv".</p> <p>* basal_permittivities.csv is a comma-separated value file containing the values of basal permittivity corresponding to the columns of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p> <p>* basal_temperatures.csv is a comma-separated value file containing the values of basal temperature corresponding to the rowso of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p>
Supporting data and code for "Water vapor estimation using wireless two-way interferometry (Wi-Wi)"
<p>Notes on generating figures for Radio Science paper.<br> Nobuyasu Shiga<br> 2019/3/9</p> <p>Fig. 4<br> File name: PhaseShifter.eps<br> Matlab file: PhaseShifter.m<br> Raw Data: 20181002124143PhaseShifter.csv</p> <p>Fig. 7<br> File name: WMRain091418.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180914_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180918_2350.txt<br> 01_min1_20180914_20180918Mod.csv %rain gauge</p> <p>Fig. 8<br> File name: WMRainDec.eps<br> Matlab file: WaterVaporAnalysis1202.m<br> Raw Data: avg20181130184636.csv %WiWi<br> WXT520_M_20181202_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20181208_2350.txt<br> WeatherAVG20181202-20181208.csv %meteorological equipment @SWT<br> 01_min1_20181202_20181208Mod.csv %precipitation</p> <p>Fig. 9<br> File name: WMR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> North15_MP3000.txt<br> South15_MP3000.txt</p> <p>Fig. 10<br> File name: WMGR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> KGN3_ZWD.txt<br> Zenith_MP3000.txt</p> <p> </p>
Data supplementing the article "Benthic diatom communities in an Alpine river impacted by waste water treatment effluents as revealed using DNA metabarcoding" , submitted to Frontiers in Microbiology
<p>These data supplement the article"Benthic diatom communities in an Alpine river impacted by waste water treatment effluents as revealed using DNA metabarcoding" submitted to Frontiers in Microbiology: </p> <p>The directory contains the following files:</p> <p><strong>64 PGM sequencing files (raw data, fastq files) </strong>- one file is provided for each sample by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>Sample_Names.xlsx</strong> - contains the information relative to the 64 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name and the raw reads number for each sample.</p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Figure 2 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 2. Classification of stations based on similarities of macroinvertebrate communities.
Figure 1 in The use of BMWP and ASPT indices for evaluation of water quality according to macroinvertebrates in Değirmendere Stream (Isparta, Turkey)
Figure 1. Study area and stations.
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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)
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DANDI Archive for NWB datasets
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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.