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Water Body Checklists: Southern Ocean Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Southern Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: North Pacific Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the North Pacific Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: South Pacific Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the South Pacific Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: Indian Ocean Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: Arctic Ocean Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Arctic Ocean region using effechecka and modified polygons from the International Hydrographic Organization.

opencc-by-4.0Jul 2024View details →

Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)

CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).

opencc-by-4.0Mar 2024View details →

Geochemistry of a weathering profile and natural waters in the upland Mediterranean catchment of Sapine, Mt-Lozère, southern France

This publication reports geochemical data from an instrumented catchment of the Service National d’Observation “Observatoire Hydro-Météorologique Cévennes-Vivarais” (OHMCV) and of the National Research Infrastructure “Observatoires de la Zone Critique: Applications et Recherche” (OZCAR) of the French “Centre National de la Recherche Scientifique” (CNRS). The presented data are originally contained in Kuessner, M.L. (2018). The interplay between chemical weathering and hydrology in the Critical Zone - insight from trace elements and lithium isotopes [Doctoral dissertation, Institut de Physique du Globe de Paris] Contents Table SC1. Analyses of soil samples in Sapine Creek Table SC2. Analyses of rock samples in Sapine Creek Table SC3. Analyses of rain samples in Sapine Creek and Cloutasse catchments Table SC4. Analyses of stream samples in Sapine Creek Table SC5. Analytical limits for quadrupole ICP-MS measurements Table SC6. Analyses of reference materials for isotope measurements

Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023

The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023. Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis.  Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.

opencc-by-4.0Aug 2024View details →

Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones

<h2>Abstract</h2> Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor. This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories. <h2>Metadata information</h2>   <table> <tbody> <tr> <td> Identification </td> </tr> <tr> <td> Full Title </td> <td> Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones </td> </tr> <tr> <td> Abstract </td> <td> Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor. This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories. </td> </tr> <tr> <td> Keywords </td> <td> Raw hyperspectral data, underwater hyperspectral data, UHI, seafloor, seabed, mineral, sea region, Vigo, Vigo-Rias Biaxas </td> </tr> <tr> <td> Pilot area </td> <td> Ria de Vigo </td> </tr> <tr> <td> Associated resources </td> <td>   </td> </tr> <tr> <td> Language </td> <td> English </td> </tr> <tr> <td> URL </td> <td>   </td> </tr> <tr> <td> Categories </td> <td> Bio-geographical regions </td> </tr> <tr> <td> Temporal reference </td> </tr> <tr> <td> Creation date (dd.mm.yyyy) </td> <td> 27.09.2023 </td> </tr> <tr> <td> Revision date (dd.mm.yyyy) </td> <td>   </td> </tr> <tr> <td> Quality and validity </td> </tr> <tr> <td> Representation type </td> <td>   </td> </tr> <tr> <td> Format </td> <td> HDF5 </td> </tr> <tr> <td> Lineage </td> <td>   </td> </tr> <tr> <td> Spatial resolution </td> <td> 2mm (with UHI about 2m away) </td> </tr> <tr> <td> Positional accuracy </td> <td>   </td> </tr> <tr> <td> Maintenance information </td> <td>   </td> </tr> <tr> <td> Coordinate system </td> <td> 3035 </td> </tr> <tr> <td> Constranits related to access and use </td> </tr> <tr> <td> Use limitation </td> <td> no </td> </tr> <tr> <td> Access constraint </td> <td> no </td> </tr> <tr> <td> Public/Private </td> <td> Public </td> </tr> <tr> <td> Responsible organisation </td> </tr> <tr> <td> Responsible Contact </td> <td> Ecotone As </td> </tr> <tr> <td> Responsible Party </td> <td> Ecotone As </td> </tr> <tr> <td> Metadata on metadata </td> </tr> <tr> <td> Contact </td> <td> Ecotone As, info@ecotone.com </td> </tr> <tr> <td> Metadata language </td> <td> English </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

SSP-aligned projected European water withdrawal/consumption at 5 arcminutes

Release 0.9.1 – What is new? ·         Industrial water withdrawals were initially overestimated due to a problem with the input data, but they are now fixed. ·         Historical water withdrawal covering 1960-2020 added. Consistency between historical and projected water withdrawals is maintained. Contents and naming conventions Annual European water withdrawal: {scenario}_{sector} _year_millionm3_5min_Europe_{from_year}_{to_year}.nc Scenarios: historical, ssp1, ssp2, ssp3, ssp5 Sector: dom, ind; stand for domestic/industrial                 From/to_year: 1960/2020, 2020/2100; historical/ssp projections. Each file holds two variables: {sector}ww and {sector}wc representing gross and net (i.e. consumptive use) water withdrawal. For exmaple, for the industrial sector, there are two variables: indww and indwc. The fraction ‘1 – indwc/indww ‘ represents the share of return flows. ------------------------- The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the periods 1960-2020 (historical) and 2020-2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs. The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at <a href="https://data.ece.iiasa.ac.at/ssp/" target="_new">https://data.ece.iiasa.ac.at/ssp/</a>). Since this update is still in progress as of August 25th, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1).  All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file. <a name="_Ref175610392"></a>Table 1: Data availability in different versions of the SSP database as of August 25th 2024. <table> <tbody> <tr> <td> Data </td> <td> SSP DatabaseVersion </td> <td> Module </td> </tr> <tr> <td> Population </td> <td> SSP v3.0.1 2024 </td> <td> Domestic </td> </tr> <tr> <td> GDP per capita </td> <td> SSP v3.0.1 2024 </td> <td> Domestic/industrial </td> </tr> <tr> <td> Energy use per capita </td> <td> SSP 2013 </td> <td> Industrial </td> </tr> <tr> <td> Electricity use per capita </td> <td> SSP 2013 </td> <td> Industrial </td> </tr> </tbody> </table>

High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL, DAg(), DAs(), DAgs())

NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload contains 7 of the 8 experiments with ERA5. The baseline OL experiment without irrigation is on a separate zenodo link (see below). 8 experiments forced with ERA5 meteorology po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739 po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling 2 experiments forced with MERRA2 meteorology --> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data. po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling

High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation

NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below). 8 experiments forced with ERA5 meteorology po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454 2 experiments forced with MERRA2 meteorology --> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data. po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling

opencc-by-4.0Aug 2024View details →

Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"

This datasset contains data to reproduce the following figures of the paper Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site: <ul> <li> Time series data of Figures 1c and 2 </li> <li> Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4) </li> <li>Pléiades snow depth map (Figure S1)</li> <li> Data used for plotting Figure S2 </li> </ul>

opencc-by-4.0Jul 2024View details →

IODP Expedition 398 ICP-AES elemental analysis (interstitial water)

Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.

opencc-by-4.0Jul 2024View details →

IODP Expedition 398 Interstitial water composite report

This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.

opencc-by-4.0Jun 2024View details →

Surface water loss hotspots and areas of human pressure in Italy

In Italy, surface water bodies are the main source of water withdrawals. However, growing human pressures are significantly changing surface water availability, gradually reducing its extent. We analyze the influence of human activities on surface water losses occurred in Italy between 1984 and 2021. To do so, we identify three areas of human pressure, i.e., regions of human activities that heavily rely on the use of surface water: <ol> <li>Irrigated area (IRR);</li> <li>Built-up area (BUP), indicating areas of human settlements (urban and industrial areas);</li> <li>Anthropogenic area (ANT), indicating areas of either irrigation practices or human settlements.</li> </ol> Here, we provide the datasets describing the spatial distribution of surface water loss (SWL), irrigated areas, built-up areas, and anthropogenic areas, and the land cover classification for 2021 across Italy (LC). Such datasets have been derived from remotely-sensed products. In particular, the location of SWL is determined using the Transitions layer of the Global Surface Water dataset (Pekel et al., 2016), whereas the maps of irrigated and built-up areas are obtained from the Corine Land Cover (CLC) 2018 dataset (EEA, 2018). Finally, the land cover map is extracted from the ESA WorldCover map (version 2) for the year 2021 (Zanaga et al., 2022). In the map of SWL, irrigated areas, built-up areas, and anthropogenic areas the value 1 indicates the presence of SWL or irrigated area or built-up area or anthropogenic area, respectively. The 2021 land cover map follows the classification system of the ESA WorldCover map (11 classes). References: Pekel, JF.; Cottam, A.; Gorelick, N.; Belward, A.S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422. European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA). Zanaga, D.; Van De Kerchove, R.; Daems, D.; De Keersmaecker, W.; Brockmann, C.; Kirches, G.; Wevers, J.; Cartus, O.; Santoro, M.; Fritz, S.; Lesiv, M.; Herold, M.; Tsendbazar, N.E.; Xu, P.; Ramoino, F.; Arino, O. ESA WorldCover 10 m 2021 v200, 2022.

Simulated submerged aquatic vegetation spectral signatures under different water quality conditions using Hydrolight

Reflectance spectra were simulated using the Hydrolight radiative transfer model (Sequoia Scientific, Bellevue, WA) for four different submerged macrophyte species under a range of water quality conditions at two different depths. We used the four-component case-2 model with spectral reflectance of four submerged species, Egeria densa, Ceratophyllum demersum, Cabomba caroliniana, and Stukenia pectinata. These four reflectance spectra were calculated from the median of 10 measurements of the canopies of the representative species placed in clear tap-water made with a handheld ASD FieldSpec Pro spectrometer. Total suspended solids concentration was varied from 1 to 40 g·m−3, chlorophyll-a concentration was varied from 0.5 to 50 mg·m−3, and colored dissolved organic matter (CDOM) was varied from 0.25 to 3.5 m−1. A total of 4,742 spectra were simulated for all four species and a mud substrate at two different depths, 1 m and 5 m, and for optically deep water.

Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows

Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba.

opencc-by-4.0Aug 2016View details →

IODP Expedition 355 Interstitial water composite report