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6 results for “catchment characteristics”

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zenodo52/100

nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in french watercourses

<p><strong>nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in French watercourses</strong></p> <p>Antoine Casquin, Marie Silvestre, Vincent Thieu</p> <p>10.5281/zenodo.10830852</p> <p>v0.1, 18<sup>th</sup> March 2024</p> <p><strong>Brief overview of data: </strong></p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Carbon and nutrients data for 5470 continental French catchments</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Modelled discharge for 5128 of catchments out of 5470</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Geopackages with catchment delineations and outlets</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; DEM conditioned to delimit additional catchments</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Land-use and climatic data for 5470 continental French catchments</p> <p><strong>Citation of this work<br></strong></p> <p>A data paper is currently being submitted with details of methods and results. Once published, it will be the preferential source to cite. The data paper will be link to the new version of the dataset that will be updated on doi.org/10.5281/zenodo.10830852. If you use this dataset in your research or report, you must cite it.</p> <p><strong>Motivations</strong></p> <p>Data was collected and curated for the nuts-STeauRY project (<a href="http://nuts-steaury.cnrs.fr">http://nuts-steaury.cnrs.fr</a>), which deployed a national generic land to sea modelling chain.</p> <p>Data was primarily used (see related works):</p> <ol> <li>To calibrate concentrations of dissolved organic carbon and dissolve silica in headwaters</li> <li>To validate spatially and temporally the modelling chain (DOC, NO3-, NH4+, TP, SRP, DSi)</li> </ol> <p>Hydrochemical large sample datasets have numerous other uses: trends computations elucidate transfer mechanisms, machine learning, retrospective studies etc.</p> <p>The objective here is to provide a large sample curated dataset of carbon and nutrients concentrations along with modelled discharges, catchment characteristics and delimitations for the continental France. Such large sample dataset aims at easing the large sample studies over France and/or Europe. Although part of the data gathered here is obtainable via public sources, the catchments delineations, their characteristics and modelled hydrology were note not publicly available yet.&nbsp;Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.</p> <p><strong>Data sources &amp; processing</strong></p> <p>Sampling points where snapped on the CCM database v2.1 (<a href="http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221">http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221</a>)(Vogt et al., 2007) and catchments were delineated using a 100m resolution Digital Elevation Model &nbsp;(DEM) conditioned by the hydrographic network and elementary catchments&rsquo; delineations of the CCM data v2.1. <strong>More than 6000 catchments were delineated and screened manually</strong> to check consistency: 5470 were retained<strong>.</strong></p> <p>Nutrient data was collected mainly through the Naiades portal (<a href="https://naiades.eaufrance.fr/">https://naiades.eaufrance.fr/</a>), a database collecting water quality data produced by different water related actors across France. Nutrient data was also collected directly with regional water agencies (<a href="https://www.eau-seine-normandie.fr/">https://www.eau-seine-normandie.fr/</a>, <a href="https://eau-grandsudouest.fr/">https://eau-grandsudouest.fr/</a>, <a href="https://www.eaurmc.fr/">https://www.eaurmc.fr/</a>, <a href="https://www.eau-artois-picardie.fr/">https://www.eau-artois-picardie.fr/</a>, <a href="https://www.eau-rhin-meuse.fr/">https://www.eau-rhin-meuse.fr/</a> and <a href="https://agence.eau-loire-bretagne.fr/home.html">https://agence.eau-loire-bretagne.fr/home.html</a>), and pre-processed using a database management system relying on PostgreSQL with PostGIS extension (Thieu &amp; Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of &ldquo;obvious outliers&rdquo;, 2. Detection of baseline change and correction if possible (or removal of data) 3. Removal of outliers using a quantile based approach by element and temporal series.</p> <p>Hydrological time series are interpolation trough hydrograph transfer (de Lavenne et al., 2023) of 1664 time series of discharge completed with GR4J model (Pelletier &amp; Andr&eacute;assian, 2020; Pelletier 2021).</p> <p>Land cover data was extracted from Corine Land Cover dataset for years 2000, 2006, 2012, and 2018 (EEA, 2020). Raw CLC typology contains 44 classes. Results of percent cover per year per class were computed for each catchment. An aggregated typology of 8 classes is also proposed.</p> <p>Climatological data was extracted from daily reconstruction at 5 arcmin for temperatures and 1 arcmin for precipitation over Europe (Thiemig et al., 2022). Mean by catchment for min&amp;max daily temperature and precipitation were computed for each catchment for the 1990-2019 period.</p> <p><strong>Nuts-STeauRY dataset</strong></p> <p><strong>Carbon and nutrients time series</strong></p> <p>Time series of carbon and nutrients within the 1962-2019 period on 5470 stations: Dissolved Organic Carbon (DOC), Total Organic Carbon (TOC) Nitrates (NO3-), Nitrites (NO2-), Ammonia (NH4+), Soluble Reactive Phosphorus (SRP), Total Phosphorus (TP) and Dissolved Silica (DSi).</p> <p><code>|var | n_unique_station| n_total_meas| mean_duration_y| mean_frequency_y|</code></p> <p><code>|:---|----------------:|------------:|---------------:|----------------:|</code></p> <p><code>|DOC |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4 992|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 658 147|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 14.3|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9.0|</code></p> <p><code>|DSi |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3 299|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 333 866|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 12.9|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;8.3|</code></p> <p><code>|NH4 |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5 318|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 907 343|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19.3|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8.7|</code></p> <p><code>|NO2 |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5 264|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 891 886|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19.2|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8.6|</code></p> <p><code>|NO3 |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5 465|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 939 279|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19.0|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9.0|</code></p> <p><code>|SRP |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5 361|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 910 107|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19.1|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8.7|</code></p> <p><code>|TOC |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 935|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 111 993|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 13.6|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9.6|</code></p> <p><code>|TP&nbsp; |&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5 199|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 802 841|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17.1|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8.8|</code></p> <p>Note that some SRP and DSi measurements were declared as realized on raw water. A thorough analysis of time series show no evidence of difference on baselines. For more accuracy, it is advised to filter out those analyses using the &ldquo;fraction&rdquo; attribute of each measurement.</p> <p><strong>Discharge modelled daily time series</strong></p> <p>Modelled naturalized discharge through hydrograph transfer and interpolated measured discharges when available for the 1980-2019 period.</p> <p>A daily discharge was computed for 5128 catchments. For small catchments (&lt; 1000 km<sup>2</sup>, n = 4530), hydrograph transfer was used, while for big catchments, a direct interpolation of measured/completed discharges was performed. The direct interpolation was only possible for 598 catchments &gt; 1000 km<sup>2</sup>. The criteria retained for a direct interpolation is 0.8*area_discharge_station &lt; area_quality &lt; 1.2*area_discharge_station when discharge and quality stations were nested.</p> <p>Hydrological time series uncertainties varies a lot depending on: quality of data source, distance from pseudo-gauged outlets, land cover of the catchments, natural spatial and temporal variability of discharge, size of the catchment (de Lavenne et al., 2016). We advise a cautious use of those modelled discharges as uncertainties could not be computed.</p> <p><strong>Catchments, outlets and conditioned DEM</strong></p> <p>5470 catchments and outlets are delivered as geopackages (EPSG: 3035).</p> <p>The DEM, conditioned by CCM 2.1 is also delivered as a GeoTIFF (EPSG: 3035) as way to delimit new catchment for the area that are consistent with the dataset.</p> <p><strong>Catchments characteristics and climate</strong></p> <p>Refer to Data sources &amp; processing and File descriptions.</p> <p><strong>&nbsp;</strong></p> <p><strong>File and attributes descriptions: </strong></p> <p>The key &ldquo;sta_code&rdquo; is present across all files. For time varying records, &ldquo;date&rdquo; can be a secondary key. &nbsp;</p> <p><strong>Description of CNPSi.csv data attributes</strong></p> <p>Each line is a couple measurement/parameter/station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; var: Abbreviation of parameter name</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; fraction: "water_filtrated" or "water_raw"</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; date:&nbsp; date of sampling</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; hour: hour of sampling</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; value: analytical result (concentration)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; provider: provider of the data</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; producer: producer of the data</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un syst&egrave;me d&rsquo;information sur les observations environnementales. Pr&eacute;sentation S&eacute;minaire UMR M&eacute;tis)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; n_meas: number of observations for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; unit: unit of concentration</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; element: "C" "N" "P" or "Si"</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; year: year of observation</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; month: month of observation</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; day: day of observation</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; julian_day: julian day observation (1-366)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")</p> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>Description of CNPSi_stats.csv data attributes</strong></p> <p>Each line is a couple parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; var: Abbreviation of parameter name</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; n_meas: number of observations for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; start_year: year of first observation for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; end_year: year of last observation for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; duration_y_tot: total duration of observation in years for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; duration_y_tot: duration of observation in years for a given parameter / station for years with at least 1 meas</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mean_nmeas_per_y_tot: mean number of observations per year considering total duration</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mean_nmeas_per_y_meas: mean number of observations per year considering years with measurements</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; is_fully_continuous: TRUE if at least one measurement per year for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; start_cont_seq: year in which starts the longest continuous sequence for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; end_cont_seq: year in which ends the longest continuous sequence for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; duration_y_cont_seq: duration in years for the longest continuous sequence for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; nmeas_cont_seq:&nbsp; number of measurements for the longest continuous sequence for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mean_nmeas_per_y_cont_seq: mean number of observations per year for the longest continuous sequence for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mean: mean value (concentration) for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; median: median value (concentration) for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sd: standard deviation (concentration) for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; cv: coeficient of variation (concentration) for a given parameter / station</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; c05,c25,c50,c75,c95: centiles 5, 25, 50, 75 &amp; 95 for a given parameter / station</p> <p><strong>Description of catchments.gpkg and outlets.gpkg data attributes</strong></p> <p>Each line is a catchment or an outlet (sampling point)</p> <p>File is a .gpkg (EPSG = 3035)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; watercourse: Name of the water course (from spatial join on IGN BD Topo)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mun_name: Name of the municipality of the outlet (from spatial join on IGN BD Admin Express)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ccm_wso_id: Seaoutlet id from CCM v2.1 database</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ccm_wso1_id: Elementary catchment id from CCM v2.1 database</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ccm_strahler: Strahler order of the catchment from CCM v2.1 database</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; area_km2: Computed area in km2 of the catchment</p> <p><strong>Description of daily discharges data attributes</strong></p> <p>Each line corresponds to a daily modelled discharge at a quality station from 1980 to 2019</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; date: Date in format yyyy-mm-dd</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; flow_mm: Discharge expressed in mm.d-1</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; flow_m3s: Discharge expressed in m3.s-1</p> <p><strong>Description of climate data attributes</strong></p> <p>Each line in the pr_tmin_tmax_1990-2019_lt_mean.csv corresponds to a mean value within a catchment for the 1990-2019 period.</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; period: 1990-2019</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; source: EMO-1 (pr) &amp; EMO-5 (tmin, tmax)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; pr: mean yearly precipitation (mm)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; tmin: mean daily minimal temperature (&deg;C)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; tmin: mean daily maximal temperature (&deg;C)</p> <p><strong>Description of land cover data attributes</strong></p> <p>Each line in the clc_8class.csv and clc_44class.csv corresponds to Corine Land Cover (CLC) class for a year (1990, 2000, 2006, 2012, or 2018) and a catchment. Raw CLC typology describes 44 classes that were aggregated to 8 classes (see clc_44class_to_8class.csv).</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; clc_44class.csv</p> <p>o&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o&nbsp;&nbsp; year: Year as stated in CLC product</p> <p>o&nbsp;&nbsp; clc_name: Description of land cover class in CLC product</p> <p>o&nbsp;&nbsp; clc_code: Code for land cover class in CLC product</p> <p>o&nbsp;&nbsp; percent_cover: Percent cover by CLC class in the catchment (0-100)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; clc_8class.csv</p> <p>o&nbsp;&nbsp; sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o&nbsp;&nbsp; year: Year as stated in CLC product</p> <p>o&nbsp;&nbsp; label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o&nbsp;&nbsp; code_clc_8class: Code for land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o&nbsp;&nbsp; percent_cover: Percent cover by aggregated CLC class in the catchment (0-100)</p> <p>&middot;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; clc_44class_to_8class.csv</p> <p>o&nbsp;&nbsp; code_clc: Code for land cover class in CLC product (44 classes)</p> <p>o&nbsp;&nbsp; code_clc_8class: Code for land cover class in aggregated CLC product (8classes)</p> <p>o&nbsp;&nbsp; label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>Acknowledgement</strong></p> <p>This publication has been prepared using European Union's Copernicus Land Monitoring Service information; <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p>The authors thank Vasken Andr&eacute;assian for communicating the discharge data and discharge station data and Alban de Lavenne for its help in using the transfr package, both for INRAE UR HYCAR.</p> <p>&nbsp;</p> <p><strong>References</strong></p> <p>de Lavenne, A., Sk&oslash;ien, J. O., Cudennec, C., Curie, F., &amp; Moatar, F. (2016). Transferring measured discharge time series: Large-scale comparison of Top-kriging to geomorphology-based inverse modeling: transferring measured discharge time series. Water Resources Research, 52(7), 5555&ndash;5576. https://doi.org/10.1002/2016WR018716</p> <p>de Lavenne, A., Loree, T., Squividant, H., &amp; Cudennec, C. (2023). The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology. Environmental Modelling &amp; Software, 159, 105562. <a href="https://doi.org/10.1016/j.envsoft.2022.105562">https://doi.org/10.1016/j.envsoft.2022.105562</a></p> <p>EEA. (2020). Corine Land Cover &eacute;dition 2018. CLC 2018. <a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine</a></p> <p>Pelletier, A., &amp; Andr&eacute;assian, V. (2020). Hydrograph separation: An impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences, 24(3), 1171&ndash;1187. <a href="https://doi.org/10.5194/hess-24-1171-2020">https://doi.org/10.5194/hess-24-1171-2020</a></p> <p>Pelletier, A. (2021). Compl&eacute;tion d'hydrogrammes avec le mod&egrave;le GR4J - Note m&eacute;thodologique. INRAE, UR HYCAR.</p> <p>Thiemig, V., Gomes, G. N., Sk&oslash;ien, J. O., Ziese, M., Rauthe-Sch&ouml;ch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249&ndash;3272, https://doi.org/10.5194/essd-14-3249-2022, 2022</p> <p>Thieu, V., Silvestre, M., 2015. DoNuts : un syst&egrave;me d'information sur les observations environnementales. Pr&eacute;sentation S&eacute;minaire UMR M&eacute;tis</p> <p>Vogt, J., A. de Jager, E. Rimaviciute, W. Mehl, S. Foisneau, K. B&oacute;dis, J. Dusart, M.L. Paracchini, P. Haastrup, &amp; C. Bamps. (2007). A pan-European river and catchment database. (European Commission. Joint Research Centre. Institute for Environment and Sustainability.). Publications Office. https://data.europa.eu/doi/10.2788/35907</p>

opencc-by-4.0Mar 2024View details →
edi44/100

SBC LTER: Land: Catchment characteristics along the southern coast of Santa Barbara County in Geodatabase

This data package include GIS layers stored in Geodatabase. The layers describe the characteristics of the catchments along the southern coast of Santa Barbara County used in the article Aguilera, R., & Melack, J. M. (2018). Relationships among nutrient and sediment fluxes, hydrological variability, fire, and land cover in coastal California catchments. Journal of Geophysical Research: Biogeosciences, 123, 2568– 2589. https://doi.org/10.1029/2017JG004119. Catchment characteristics include: Land cover and land use based on hyperspectral imagery obtained by the Airborne Visible/Infrared Imaging Spectrometer; number of inhabitants based on population counts by block from the 2010 census spatial database; relief and slopes estimated from a 30 m digital elevation model; geological substrata obtained from geologic maps of California; soil textural types based on the Soil Survey Geographic data, and fire perimeters for the Gaviota, Gap, Tea and Jesusita fires.

openCC (other)Feb 2022View details →
zenodo40/100

A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1

<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation:&nbsp;<strong>&nbsp;</strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2023View details →
zenodo40/100

The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments

<p>Data used for the paper: The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments. Contains water quality and discharge data. See paper for more details.</p>

opencc-by-4.0Aug 2024View details →
zenodo32/100

Supplement to "Hydro-Meteorological Drivers of Event Runoff Characteristics Under Analogous Soil Moisture Patterns in Three Small-Scale Headwater Catchments"

<p>The dataset is a supplement to the manuscript "Hydro-Meteorological Drivers of Event Runoff Characteristics Under Analogous Soil Moisture Patterns in Three Small-Scale Headwater Catchments" and contains the processed time series data of the W&uuml;stebach, Rollesbroich, and Petzenkirchen catchments. Hydro-metorological variables include precipitation, runoff, soil moisture, and groundwater level.</p>

opencc-by-4.0Sep 2024View details →
zenodo28/100

A dataset for reservoir-catchment characteristics for 3254 Chinese reservoirs, i.e., Res-CN

<p>A total of 512 attributes are provied for 3254 Chinese reservoirs. --These&nbsp;attributes are provided at reservoir upstream catchment-level, containing 1) two types of reservoir upstream catchments (i.e., full catchment and intermediate catchment), 2) catchment&nbsp;topography, 3) anthropogenic activity, 4) climate, 5) land cover &amp; use, and 6) soil &amp;&nbsp;geology. -----&gt; we also generate time series of 15 meteorological variables. ----&gt; We also provide an extensive data set on reservoir storage anomaly (data available for 92% of the 3,254 reservoirs), water level ( 20%), --evaporation (98%)--, -- water area (99%)--&nbsp;from multisource satellites --&nbsp;radar/laser satellite altimeters + satellite&nbsp;images from Landsat + Sentinel-2. Validation_figures record all validations, time series of figures, and statistical metrics for water level, water area and storage variations. Currently, our manuscript is submitted to Earth System Science Data for review.&nbsp;https://doi.org/10.5194/essd-2022-422</p>

opencc-by-4.0Dec 2022View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record