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59 results for “irrigation water”
Shadow Spaces for Water Stress Adaptation: Supplemental Irrigation Application in Rainfed Fig Production
<p>Data Sources is a SPSS file. Common descriptive statistics and inferential statistics are used.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Digital solutions and early warning system for decision support and risk management in water reuse for irrigation
<p>Video presentation for IWA World Water Congress & Exhibition, 11-15 September 2022, Copenhagen, Denmark.</p>
Genova - Water used for irrigation
<p>Calculated rainwater or greywater use for irrigation purposes for three rainwater collection scenarios in Genova.</p>
GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin (Irrigated, Rain-fed)
<p>GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin resolution for year 2015 for irrigated and rain-fed GCAM crop breakout along with forest, urban, sparse, snow, shrub land classes. This run was generated for use by the `teleconnect` package (see <a href="https://github.com/IMMM-SFA/teleconnect">https://github.com/IMMM-SFA/teleconnect</a>). The following is the full README found in the zipped data resource:</p> <blockquote> <p>GCAM v5.2 to Demeter </p> <p>Title:<br> Demeter output for GCAM v5.2 with water constraints - Reference scenario</p> <p>Description:<br> Demeter run conducted using the base layer combining Mirca and Modis v6 type 5 to generate rain-fed and irrigated crops constrained to Modis crop area. GCAM projection split RockIceDesert into snow and sparse land classes.</p> <p>Building the Demeter base layer for use with GCAM allocated land classes and use types:<br> Described in the readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf document the docs directory of this data archive.</p> <p>GCAM Version: https://github.com/JGCRI/gcam-core/tree/gcam-v5.2 ; https://doi.org/10.5281/zenodo.3528353 </p> <p>GCAM Reference:<br> Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.</p> <p>Demeter Reference:<br> Vernon, C.R., Le Page, Y., Chen, M., Huang, M., Calvin, K.V., Kraucunas, I.P. and Braun, C.J., 2018. Demeter – A Land Use and Land Cover Change Disaggregation Model. Journal of Open Research Software, 6(1), p.15. DOI: http://doi.org/10.5334/jors.208</p> <p>Run:<br> GCAM reference scenario with water constraints conducted by Sonny Kim (skim@pnnl.gov) originally retrieved from PNNL's Constance here: /pic/projects/GCAM/water_market/database_basexdbGCAM51WaterConstr. </p> <p>Contents:<br> teleconnect_agu2019<br> -- config_gcam5p1_watconstr_ref.ini (Demeter configuration file) <br> -- code (code to run Demeter pre-, run, and post-processing)<br> ---- README.txt (Description of run order and process for Demeter on Constance)<br> ---- demeter_preprocess.py (Python script to extract land data from the GCAM database and split RockIceDesert into snow and sparse)<br> ---- demeter_postprocessing.py (Python script to create fractional output of Demeter's native output in square kilometers)<br> ---- run_demeter.py (Python script to run Demeter)<br> ---- run_demeter_gcam5p1_watconstr_ref.sh (sbatch script to submit a Demeter run on Constance)<br> ---- run_postprocessing.sh (sbatch script to submit a post-processing run on Constance)<br> ---- run_preprocessing.sh (sbatch script to submit a pre-processing run on Constance)<br> ---- slurm-11504861.out (Slurm output from Demeter run)<br> -- GCAM <br> ---- database_basexdbGCAM51WaterConstr (GCAM output database)<br> -- inputs (input files used by Demeter) <br> ---- allocation <br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_constraint_alloc.csv (weighting of constraints)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_observed_alloc.csv (reclassification table for observed land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_order_alloc.csv (processing order for land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_projected_alloc.csv (reclassification table for GCAM land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_transition_alloc.csv (transition order for land classes)<br> ---- constraints<br> ------ 000_nutrientavail_hswd_5arcmin.csv (nutrient availability constraint weighted by grid cell)<br> ------ 001_soilquality_hswd_5arcmin.csv (soil quality constraint weighted by grid cell)<br> ---- observed<br> ------ gcam_reg32_basin235_modis_v6_2010_mirca_2000_5arcmin_sqdeg_wgs84_11Jul2019.csv (Demeter base layer)<br> ---- projected<br> ------gcam_5p1_watconst_reference.csv (output from demeter_preprocess.py from GCAM output)<br> ------gcam_5p1_watconst_reference_split.csv (output from demeter_preprocess.py from GCAM output with RockIceDesert split into snow and sparse land classes)<br> ---- reference (see https://github.com/IMMM-SFA/demeter)<br> ------ aezcoord.csv<br> ------ countrycoord.csv<br> ------ gcam_basin_lookup.csv<br> ------ gcam_regions_32.csv<br> ------ limits.csv<br> ------ query_land_reg32_basin235_gcam5p0.xml (land allocatio query)<br> ------ regioncoord.csv<br> -- for_teleconnect<br> ---- usa_demeter.csv (file used by the `teleconnect model` containing only 5-arcmin grid cells that are in GCAM region 1 (USA))<br> -- outputs (output files from Demeter run)<br> ---- ref_watconstr_2019-11-07_07h20m46s (output Demeter run directory)<br> ------ log_files (log file directory)<br> -------- logfile_ref_watconstr_2019-11-07_07h20m46s.log (log file from Demeter run)<br> ------ spatial_landcover_tabular<br> -------- landcover_2015_fraction.csv (fraction of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_sqkm.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_timestep.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -- docs <br> ---- readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf (creation of the Demeter base layer)</p> </blockquote>
Nonylphenol in soil-celery system simulating long-term reclaimed water irrigation: occurrence, migration, and health risk assessment
<p>The data uploaded were the biomass of celery tissues and the concentraion of nonylphenol in soil and celery tissues after harvest.</p>
Irrigation well water in Nebraska: essential nutrient contents and other properties
<p>Water nutrient concentrations and other properties were surveyed by sampling water from 642 irrigation wells in Nebraska. The amount of Ca, Mg, S, Cl, and B applied in irrigation exceeds removal in 15 Mg ha<sup>-1</sup> of corn (<i>Zea mays</i> L.) grain harvest for most wells. Irrigation supply exceeded corn grain harvest removal of K, Mn and Mo for >20% of the wells. The supply of P, Zn, Cu, Fe, and Mo was generally very low but sufficient with some wells to be considered in nutrient management plans. The median level of nitrate N was 4.4 ppm with 25% of the wells having >10 ppm NO<sub>3</sub>-N which is above the suitability limit for human consumption. The agricultural lime equivalent applied with one or two ML of irrigation was enough to neutralize the acidifying effect of 200 kg ha<sup>-1</sup> of fertilizer-N for 70% or 89% of the wells. Nutrient and lime supply was relatively low for Sandhills wells and relatively high for wells in river valleys of <100 ft depth. No wells had excessive Na levels but 0.3% of the wells had salinity levels of concern. A grouping of wells into 11 aquifer, geological formation and well-depth combinations accounted for >20% of the variation for most water properties but much variation occurred within groups. Sampling of the well water is needed for full optimization of nutrient and soil management. Information on nutrients supplied through irrigation should be complemented by regular soil testing and the use of recommended nutrient management guidelines.</p>
Supporting Dataset for the study "A simple Approach to Represent Irrigation Water Withdrawals in Earth System Models"
<p><span><span>This archive contains the following information (8 directories):</span></span></p> <ol> <li> <p><span><span>surfex_v8.0climat : ISBA-CTRIP source code from CNRM-ESM-2 used in the study</span></span></p> </li> <li> <p><span><span>model_data : parameters used by the model and to plot the figures</span></span></p> </li> <li> <p><span><span>fig : ncl scripts to plot the figures & figures in eps</span></span></p> </li> <li> <p><span><span>discharges : simulated and observed river discharges data</span></span></p> </li> <li> <p><span><span>fluxes : simulated water fluxes plotted on the figures</span></span></p> </li> <li> <p><span><span>tws : estimated and simulated terrestrial water storage data</span></span></p> </li> <li> <p><span><span>withdrawals : imposed (impirrig) and simulated (irrig) irrigation water withdrawals</span></span></p> </li> <li> <p><span><span>wtd : simulated and estimated groundwater levels and trends</span></span></p> </li> </ol>
Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona
<p>This dataset contains the simulation results of the combined effects of future urban growth and climate change on irrigation water use in the Phoenix Metropolitan Area, central Arizona. The simulation is conducted with the Variable Infiltration Capacity (VIC) model at 1-km, hourly resolution from 1981-2100 and aggregated to 30-yr average in this dataset. </p> <p>The 30-yr average results are compressed and organized into three files: <strong>Baseline</strong>, <strong>ICLUS2050</strong>, and <strong>ICLUS2100</strong>. The Baseline file contains results using the historical land cover map (year 2010). The <strong>ICLUS2050</strong> and <strong>ICLUS2100</strong> contain results using future land cover maps. The filename of modeling results contains the associated land cover and climate change scenario as follows: "fluxes.irri.ICLUS_<em>$YEAR</em>_<em>$LCSCE</em>.<em>$CLSCE.$GCM</em>.nc", where <em>$YEAR</em> is the year of land cover change projection (2050 or 2100), <em>$LCSCE</em> is the land cover change scenario (SSP2 or SSP5), <em>$CLSCE</em> is the climate change scenario (RCP45 or RCP85), and <em>$GCM</em> is the GCM used (eight in total) </p> <p>More details can be found on the associated paper (this record will be updated when the paper is published):</p> <p>Wang, Z., and Vivoni, E.R. 2021. Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona. <em>Journal of the American Water Resources Association (in revision)</em>.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Irrigation water salinity impacts Date palm leaf fungi
Open the record for dataset details and reuse information.
Gemcitabine Versus Water Irrigation in Upper Tract Urothelial Carcinoma
ClinicalTrials.gov study NCT04865939. IPD Sharing: NO. Countries: 1. Publications: 1.
Irrigation well water in Nebraska: essential nutrient contents and other properties
Open the record for dataset details and reuse information.
Data from: Evaluation of drip irrigation system for water productivity and yield of rice
Open the record for dataset details and reuse information.
Data from: Water-conscious management strategies reduce per-yield irrigation and soil emissions of CO2, N2O, and NO in high-temperature forage cropping systems.
Open the record for dataset details and reuse information.
GGCMI Phase 2: Crop model emulators of irrigation water demand (IWD)
<p>Polynomial emulators of irrigation water demand for globally gridded crop models from the GGCM phase 2 project. A0: no growing season adaptation. A1: with growing season adaptation. See: https://doi.org/10.5194/gmd-2019-365 and https://doi.org/10.5194/gmd-13-2315-2020 for more details. </p> <p>Note: the GEPIC emulator for maize and soy (A0) is not available. </p>
Manuscript code & data: Foster et al. (2020) "Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy"
<p>Data to reproduce results presented in Foster, T., Mieno, T. and Brozovic, N. (2020). <em>Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy.</em> Water Resources Research. In Review. Files provide include:</p> <ul> <li>"ReviewMetadata_FosterWRR_2020.xlsx" - data needed to reproduce meta-analysis presented in the paper</li> <li>"MeasurementErrorCode_FosterWRR_2020.m" and "Foster2018_ProductionFunction.mat" - Matlab code and data needed to reproduce welfare loss analysis presented in the paper.</li> </ul>
PROTECTION OF IRRIGATED AREAS FROM WATER EROSION
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Figures 2-9 from: Silva GL, Metzelthin MH, Da-Costa T, Rocha MS, Silva DE, Ferla NJ, Silva OS (2017) Responses of water mite assemblages (Acari) to environmental parameters at irrigated rice cultivation fields and native lakes. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19988
Figures 2-9 View of the study sites: (2) Detailed view of the rice-water samples, (3) Rice area 1 – R1, (4) Rice area 2 – R2, (5) Rice area 3 – R3, (6) Rice area 4 – R4, (7) Lake 1 – L1, (8) Lake 2 – L2, (9) Lake 3 – L3.
Figures 12-13 from: Silva GL, Metzelthin MH, Da-Costa T, Rocha MS, Silva DE, Ferla NJ, Silva OS (2017) Responses of water mite assemblages (Acari) to environmental parameters at irrigated rice cultivation fields and native lakes. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19988
Figures 12-13 Ordination diagram (first two axes) of Non-Metric Multidimensional Scaling (NMDS) using (12) Bray-Curtis and (13) Jaccard indexes with Envfit function for the evaluated environments. (▲ Lake 1, ■ Lake 2 and 3, ● Rice Area). Stress: 0.15.
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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
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.