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19,365 results for “watering”
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2024.
Four YSI EXO2 water quality sondes were deployed from April 2024 to October 2024 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2024. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER YSI EXO2 sonde 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, algae, fluorescent dissolved organic matter, turbidity, and depth at four sites in the Plum Island Estuary in year 2025.
Four YSI EXO2 water quality sondes were deployed from May 2025 to November 2025 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, algae, organic matter, turbidity, and depth in 15-minute intervals.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2025. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2000. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2000,15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2001. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2001,15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2002. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2002, 15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
SBC LTER: Pore water constituents and residence times (Radon activity) from Santa Barbara beaches, 2012-2013
Constituents of beach pore water and parameters for calculating residence times are reported for two beaches in the Santa Barbara area, Isla Vista Beach and East Campus Beach, from July 2012 to June 2013. This dataset reports beach pore water concentrations of ammonium and nitrate, total dissolved Nitrogen and Carbon, particulate Nitrogen and Carbon, Radon, salinity, conductance, Oxygen and water temperature. Residence time ("Tau") can be calculated from Radon-222 activities in nearshore seawater, in pore water and at equilibrium, which are presented in a second table (also available in published paper). Results from these data were reported in: Goodridge, B. M. and J. M. Melack. 2014. Temporal evolution and variability of dissolved inorganic nitrogen in beach pore water revealed using radon residence times. Environmental Science and Technology, 48: 14211-14218. DOI:10.1021/es504017j
Record of storm events and associated water levels for the Virginia Coast Reserve, 1980-2013
This empirical storm record for the Virginia Coast Reserve was created using a 34-year record of hourly wave hindcast data - including wave height (Hs) and wave period (Tp) - from the USACE's Wave Information Studies buoy offshore Hog Island in the Virginia Coast Reserve (Station 63183, 22 m water depth) and hourly records of water level from the nearest NOAA tide gauge (Station 8631044, Wachapreague, VA). The record includes wave and water level statistics for each event relevant for coastal modeling applications: storm start and end times, duration, total water level, still water level, as well as concurrent tidal amplitude, non-tidal residual, Hs, and Tp. The raw data is processed by first removing the 1 yr running median, which accounts for non-stationarity in wave and water level parameters due to inter-annual and decadal variability while maintaining seasonality. The median of the last 3 years is then applied to the entire time series such that the new time series is representative of the current climate. A year-by-year tidal analysis is performed to obtain the tidal amplitude and non-tidal residual. Lastly, water elevations are calculated following the run-up equations of Stockdon et al. (2006). Storm events are then extracted from the corrected time series by conditioning on Hs: events are identified as periods of 8 or more consecutive hours with deep-water significant wave heights greater than 2.1 m, which is the minimum monthly averaged wave height for periods in which waters levels exceeded the measured average dune toe elevation (1.9 m NAVD88) of barriers in the Virginia Coast Reserve. In total, we identify 282 independent sea-storm events over the 34-year record, resulting in an average of 8.3 events per year. See Reeves et al. (2021; https://doi.org/10.1029/2021GL092958) and supplementary information therein for complete details of the methodology.
Supplementary data to: Importance and vulnerability of the world's water towers
<p>This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">MountainHydrology Github page</a></p> <p>The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">Github</a> to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4.</p> <p><strong>Archive contents</strong></p> <p>The archives contents are organized in eight separate directories, which are listed here, along with their contents:</p> <ul> <li><strong>ERA5</strong></li> </ul> <p>Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu</p> <p>This directory includes:</p> <p><em>Input</em></p> <pre><code>ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017 ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017 era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017 era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm) P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units P_var_interannual.tif - Interannual variablity in precipitation 2001-2017 P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017 P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit</code></pre> <ul> <li><strong>Glaciers</strong></li> </ul> <p>Glacier volume and mass balance are derived from published datasets. This directory includes:</p> <p><em>Output</em></p> <pre><code>Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute</code></pre> <p><em>External data</em></p> <p>Glacier volume data published in<em> Farinotti et al., 2019, Nature Geoscience</em>, were used.<br> Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).<br> Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available <a href="https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/315707/global_fraction-of-degree_grids.zip?sequence=60&isAllowed=y">here</a>.<br> The used files are <em>p05_degree_glacier_area_km2.tif</em> and <em>p05_degree_glacier_volume_km3.tif</em></p> <p>Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit.<br> References:<br> Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).<br> World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06</p> <ul> <li><strong>HydroLAKES</strong></li> </ul> <p>Surface lake and water storage per Water Tower Unit was calculated. This directory includes:</p> <p><em>Output</em></p> <pre><code>WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units</code></pre> <p><em>External data</em></p> <p>For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile <em>HydroLAKES_polys_v10.shp</em> can be downloaded from <a href="http://https://97dc600d3ccc765f840c-d5a4231de41cd7a15e06ac00b0bcc552.ssl.cf5.rackcdn.com/HydroLAKES_polys_v10_shp.zip">HydroSheds</a></p> <p>Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016).</p> <ul> <li><strong>Indicators</strong></li> </ul> <p>All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit.</p> <p>This directory includes:</p> <pre><code>indicators.csv - Table with all indicators and subindicators per Water Tower Unit</code></pre> <ul> <li><strong>Snow</strong></li> </ul> <p>The MODIS MOD10CM006 snow cover product was used to derive snow persistence.<br> Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006</p> <p>This archive includes:<br> <em>Input</em></p> <pre><code>MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017 MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017 Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017 Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017 Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit</code></pre> <ul> <li><strong>Uncertainty</strong></li> </ul> <p>The directory contains the uncertainty ranges used in the uncertainty analysis<br> The directory includes:</p> <pre><code>ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit</code></pre> <ul> <li><strong>Water demands</strong></li> </ul> <p>Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output.<br> Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).</p> <p>The directory includes:<br> <em>Input</em></p> <pre><code>Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3) Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3) Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3) Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014</code></pre> <p><em>Output</em></p> <pre><code>Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014 Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014 Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014 Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water demand 2001-2014 aggregated to basins Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin WTU_Demand_indicators.csv - Table listing demand per sector per basin WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin</code></pre> <ul> <li><strong>WTU units</strong></li> </ul> <p>The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes:</p> <p><em>Output</em></p> <pre><code>basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution basins_vector.shp - Definition of basins with Water Tower Units as vector data downstream_vector.shp - Definition of downstream basins as vector data gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence gmba_ss.shp - GMBA mountain ranges included in Water Tower Units WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution WTU_specs.csv - Table with set of specifications of Water Tower Units WTU_vector.shp - Definition of Water Tower Units as vector data</code></pre> <p><em>External data</em></p> <p>FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at <a href="http://www.fao.org/nr/water/aquamaps/">FAO Aquamaps</a></p> <p>The specific shapefiles used are:</p> <p><a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38047&fname=Major_hydrological_basins.zip&access=private">major_hydrobasins.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37039&fname=hydrobasins_asia.zip&access=private">hydrobasins_asia.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37174&fname=hydrobasins_southam.zip&access=private">hydrobasins_southam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38044&fname=hydrobasins_northam.zip&access=private">hydrobasins_northam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37299&fname=hydrobasins_neareast.zip&access=private">hydrobasins_neareast.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37250&fname=hydrobasins_europe.zip&access=private">hydrobasins_europe.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37173&fname=hydrobasins_centralam.zip&access=private">hydrobasins_centralam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37251&fname=hydrobasins_austpacific.zip&access=private">hydrobasins_austpacific.shp</a>:</p>
Acclimation to water restriction implies different paces for behavioral and physiological responses in a lizard species
<p>Raw data of the article "Acclimation to Water Restriction Implies Different Paces for Behavioral and Physiological Responses in a Lizard Species" by Rozen-Rechels D. et al., published in Physiological and Biochemical Zoology 93(2):160-174 in 2020 (https://doi.org/10.1086/707409). These data are freely available in csv format. See the readme file for metadata explanation.</p> <p>Data were formatted by the first author David Rozen-Rechels and collected according to standards and procedures described in the companion journal article.</p> <p> </p>
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake - Dataset
<p>Dataset of the research article <em>Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake.</em></p> <p>Granados, I., Toro, M., Giralt, S., Camacho, A., Montes, C., 2020. Water column changes under ice during different winters in a mid-latitude Mediterranean high mountain lake. Aquatic Sciences 82, 30. <a href="https://doi.org/10/ggmkhv">https://doi.org/10/ggmkhv</a></p> <p> </p>
Data and R-Scripts for "Quality and timing of crowd-based water level class observations"
<p>This are the data and the R-scripts used for the manuscript "Quality and timing of crowd-based water level class observations" accepted for publication in the journal Hydrological Processes in July 2020 as a Scientific Briefing. To run the code, just run the R-script with the name "RunThisForResults.R". Results will be written to the "Figures" and the "Results" folder.</p>
Viral Metagenomes from water systems in the Mediterranean Sea
<p>The Water Framework Directive (WFD; 2000/60/EC) is the European umbrella for the assessment and regulation of ecological quality of water systems (lakes, rivers, transitional waters, coastal waters). The scope of WFD is to improve the ecological status of the aquatic ecosystems. To do so, a long time-series of monitoring campaigns within WFD exists with the ultimate goal to protect coastal ecosystems from degradation. Further, WFD project employs the calculation and improvement of ecological quality indices for the definition and assessment of eutrophication. In the submitted sub-project within WFD, the viral metagenome of 15 samples collected in 2014 and 2015 is sequenced and analyzed for several genes for the study of taxonomy and potential function of double-stranded DNA viruses.</p>
Sonic Kayaks geolocated air pollution, water turbidity, temperature and hydrophone analysis
<p>These data sets are the result of five trips using <a href="https://fo.am/activities/kayaks/">Sonic Kayaks</a> to collect data as part of the <a href="https://actionproject.eu/">ACTION Project</a>. The sampling was carried out in the Penryn river, around Falmouth docks and the Helford estuary. A variety of sensors were used:</p> <ol> <li>Thermometer recording water temperature.</li> <li>PMS7003 air pollution sensor recording a variety of particulate sizes.</li> <li>DolphinEar DE PRO hydrophone for detecting noise pollution and biological signals.</li> <li>A custom turbidity sensor to detect changes in water cloudiness.</li> </ol> <p>The sound has been processed in this data set in order to classify sound sources from different boat engines. More information, source code and <a href="https://github.com/fo-am/sonic-kayaks/wiki">open hardware plans for construction can be found here</a>.</p>
Global reanalysis of riverine water levels at the river mouth
<p>Dataset prepared for manuscript "The effect of surge on riverine flood hazard and impact in deltas globally" (Eilander <em>et al </em>2020)</p> <p>This dataset includes water level data and discharge at 3433 river mouth locations globally, including several components of nearshore still water levels based on a model framework for global compound flood simulations. We usedof runoff from tier 2 of the EartH2Observe (E2O) project (Dutra <em>et al</em> 2017, Schellekens <em>et al</em> 2017) with meteorological forcing from ERA-Interim (Dee <em>et al</em> 2011) and MSWEP v1.2 (Beck <em>et al</em> 2017), surge levels from the Global Tide and Surge Reanalysis (GTSR) based on the GTSM model (Muis <em>et al</em> 2016), and tide levels from the FES2012 model (Carrere <em>et al</em> 2012). These runoff and dynamic sea level (surge and tide) data were used to force the global river routing model CaMa-Flood (Yamazaki <em>et al</em> 2011) to simulate riverine water levels.</p> <p>The accompanying excel file provides an table explaining the data dimensions, variables and metadata.</p>
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p> <p>The details of the product is described in "<a href="https://zenodo.org/api/files/59060333-b9fc-45ad-b381-3b05a866de6c/FUI_WaterColor_2015China_Readme_V1.1.docx?versionId=44bb5bbb-407f-4dc8-b183-fa1a3843488f">FUI_WaterColor_2015China_Readme_V1.1.docx</a>".</p>
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
Water availability and temperature scenarios for water-dependent power plants in the Danube river basin and the Iberian Peninsula
<p>The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula.</p> <p>The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered.</p> <p>The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study.</p> <p>Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s).</p> <p>For a description on how these files were obtained, please refer to <a href="https://doi.org/10.2777/135510">https://doi.org/10.2777/135510</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.