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68 results for “water sources”
Data from: Uncorrected soil water isotopes through cryogenic vacuum distillation may lead to a false estimation of plant water sources
<p><span>Successful use of stable isotopes (δ<sup>2</sup>H and δ<sup>18</sup>O) in ecohydrological studies relies on the accurate extraction of unfractionated water from different types of soil samples. Cryogenic vacuum distillation (CVD) is a common laboratory-based technique used for soil water extraction; however, the reliability of this technique in reflecting soil water δ<sup>2</sup>H and δ<sup>18</sup>O is still of concern. </span><span>This study examines the </span><span>reliability of a newly developed automatic cryogenic vacuum distillation (ACVD) system through a set of pure water extraction. The impacts of extraction parameters (i.e., extraction time, temperature, and vacuum) and soil properties on the recovery of soil water δ<sup>2</sup>H and δ<sup>18</sup>O were further assessed for the ACVD and traditional extraction systems (TCVD) systems. Finally, the potential influences of the CVD technique on the prediction of plant water uptake were investigated through a sensitivity analysis. </span><span>We demonstrated that the ACVD system was reliable for recovering the isotopic composition of pure water, with negligible biases of − 0.1 ± 0.3‰ for δ<sup>2</sup>H and 0.04 ± 0.09‰ for δ<sup>18</sup>O. Both ACVD and TCVD similarly extracted water from the rewetted soils when the extraction time of the ACVD system reached 240 min, but none of the CVD systems successfully recovered the isotopic signatures of doped water from soil materials. </span><span>Mean </span><span>δ</span><sup><span>2</span></sup><span>H </span><span>offsets</span><span> of extracted soil water were − 2.6 ± 1.3‰ and − 2.4 ± 1.7‰ for ACVD and TCVD, respectively; while mean δ<sup>18</sup>O </span><span>offsets</span><span> were − 0.16 ± 0.14‰ and − 0.39 ± 0.37‰. The isotopic offsets of CVD systems were positively correlated with soil clay content, and negatively correlated with soil water content. The use of corrected soil data (with CVD offsets) could improve the prediction of plant water uptake based on its high correlation with environmental factors. </span><span>This study identifies the isotopic offsets of CVD systems (i.e., ACVD and TCVD) and provides possible solutions for better-predicting plant water sources. Even so, the wide use of CVD techniques probably induces noticeable uncertainties in predicting plant water uptake depths. The </span><span>dataset of soil water extraction </span><span>in this study</span><span> will have implications for the technological development of CVD techniques.</span></p>
Supplementary material for "Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic"
<p><span>Dataset presented and discussed in the research article “</span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic<span><span>” by Zocher et al. The article is published in <em>Geochimica et Cosmochimica Acta</em> and was prepared by the following authors: Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></span></p> <p> </p> <p>(1) School of Science, Constructor University, Bremen, Germany</p> <p>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</p> <p>(3) Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</p> <p>(4) SINTEF Ocean, Trondheim, Norway</p> <p> </p> <p><span><span>Table A1 includes the general information and data for all stations, Table A2 the more detailed salinity data for stations Nidelva A, B and C. </span></span></p> <p> </p> <p><span><span>Reference research article:</span></span></p> <p><span><span> </span></span></p> <p><span><span>Zocher, A.-L., Ciesielski, T. M., Piarulli, S., Farkas, J., & Bau, M. (2024). </span></span><span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic. Geochimica et Cosmochimica Acta.</span></span></p>
Figure 8. Comparison between the exact function v p in Reconstruction of a passive tracer boundary source in an open water area
Figure 8. Comparison between the exact function v p and the reconstructed vα h.
Figure 6 in Reconstruction of a passive tracer boundary source in an open water area
Figure 6. Solution of the optimal control problem.
Figure 7 in Reconstruction of a passive tracer boundary source in an open water area
Figure 7. Difference between φ p and φα h.
Figure 2. Velocity field U, t in Reconstruction of a passive tracer boundary source in an open water area
Figure 2. Velocity field U, t = 0.
Figure 4 in Reconstruction of a passive tracer boundary source in an open water area
Figure 4. Convergence on the boundary.
Compilation of mean monthly water table depth data (2015-2023) and linkages to further published sources of water table data, from European peatlands
<p>This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.</p>
Data sources for the manuscript on groundwater stress indicators published in Water Resources Research
<p>We computed seven global-scale groundwater stress indicators at the 0.5° grid-cell level and for transboundary aquifers> 20,000 km². All indicators were calculated for current conditions (1981-2010 or 2001-2010) based on a homogenized version of the concatenated WATCH Forcing Data ERA-40 (WFD) and WFD ERA-Interim data sets (WFDEI). In addition, four of the indicators were computed for the 2050s (2041-2070) under the worst-case greenhouse gas emissions scenario RCP8.5 applying ten climate and irrigation scenarios. The scenarios were derived by combining two irrigation scenarios (“AAI constant” and “AAI LandSHIFT”) with model output from the five global climate models GFDL-ESM2M, HadGEM2 -ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M.</p> <p>Here, we provide the WaterGAP model output used to compute the groundwater stress indicators. A description of the indicators and the underlying data can be found in the reference below. Moreover, a table with coordinates and grid-cell area [km²] used in WaterGAP is provided for the conversion of units.</p> <p>The model output comprises:</p> <p>1. Monthly groundwater recharge (GWR and GWRswb) 1981-2010 and 2041-2070 [mm/month, km³/month]</p> <p>2. Monthly groundwater withdrawals (WWg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month]</p> <p>3. Monthly net abstractions from groundwater (NAg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month]</p> <p>4. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run with human water use (transWU) [mm/month]</p> <p>5. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run without human water use (NAT) [mm/month]</p> <p>6. Monthly groundwater storage 2001-2010 from a model run with human water use (transWU) [mm]</p> <p>7. Monthly groundwater storage 2001-2010 from a model run without human water use (NAT) [mm]</p>
Identification of the atmospheric water sources and pathways responsible for the East Asian summer monsoon rainfall
<p><strong>era_hydro_easm_jul2013_*.csv.gz:</strong> These three files are the raw output from the TRACMASS trajectory model. They stored positions of each trajectory at the start (ini file), during run (run file) and at the end (out file) during July 2013 and has been used for generating Figure 1.</p> <p><strong>mask.nc:</strong> The basin definition in Figure 1 was plotted using this netcdf file.</p> <p><strong>ep_traj_*.gz: </strong>The four zip files corresponds to four summer months (June, July, August, and September) and used to plot Figure 2.</p> <p><strong>ep_traj_basins_*.gz: </strong>The four zip files corresponds to four summer months (June, July, August, and September) and used to plot Figure 3.</p> <p><strong>quantification.gz: </strong>These files were used for quantification provided in Figure 4 and Figure 8.</p> <p><strong>traj_pathways_*.gz: </strong>The two zip files (one for the South Indian Ocean, SIO and another for the Pacific Ocean, PAC) were used to generate Figure 5.</p> <p><strong>traj_rt.gz: </strong>The residence time of atmospheric waters in Figure 6 was created using the files in this zip.</p> <p><strong>traj_age_*.gz: </strong>The two zip files (one for the South Indian Ocean, SIO and another for the Pacific Ocean, PAC) were used to generate Figure 7.</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>
Data from: Drought effects in Mediterranean forests are not alleviated by diversity-driven water source partitioning
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Physiology of the widespread pulsating soft coral Xenia umbellata is affected by food sources, but not by water flow
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Data from: Uncorrected soil water isotopes through cryogenic vacuum distillation may lead to a false estimation of plant water sources
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Physical and biogeochemical properties of California Current upwelled source waters
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Upwelling intensity and source water properties drive high interannual variability of corrosive events in the California Current
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Data from: Coalescent models characterize sources and demographic history of recent round goby colonization of Great Lakes and inland waters
The establishment and spread of aquatic invasive species is ecologically and economically harmful and a source of conservation concern internationally. Processes of species invasion have traditionally been inferred from observational data of species presence/absence and relative abundance. However, genetic-based approaches can provide valuable sources of inference. Restriction-site associated DNA sequencing was used to identify and genotype single nucleotide polymorphism (SNP) loci for Round Gobies (Neogobius melanostomus) (N=440) from 18 sampling locations in the Great Lakes and in three Michigan, USA drainages (Flint, Au Sable, and Cheboygan River basins). Sampled rivers differed in size, accessibility, and physical characteristics including man-made dispersal barriers. Population levels of genetic diversity and inter-population variance in SNP allele frequency were used in coalescence-based Approximate Bayesian Computation (ABC) to statistically compare models representing competing hypotheses regarding source population, post-colonization dispersal, and demographic history in the Great Lakes and inland waters. Results indicate different patterns of colonization across the three drainages. In the Flint River, models indicate a strong population bottleneck (< 3% of contemporary effective population size) and a single founding event from Saginaw Bay led to the colonization of inland river segments. In the Au Sable River, analyses could not distinguish potential source populations, but supported models indicated multiple introductions from one source population. In the Cheboygan River, supported models indicated that colonization likely proceeded from east (Lake Huron source) to west among inland locales sampled in the system. Despite the recent occupancy of Great Lakes and inland habitats, large numbers of loci analyzed in an ABC framework enable statistically supported identification of source populations and reconstruction of the direction of inland spread and demographic history following establishment. Information from analyses can direct management actions to limit the spread of invasive species from identified sources and most probable vectors into additional inland aquatic habitats.
Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites
<p>The datasets include:</p> <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> <p>The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.</p>
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes
<p>The original dataset of the manuscript: "Measurement Report: Investigation of pH- and particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes"</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.