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2,582 results for “nitrogen”
Global dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
Soil nitrogen availability vs. acidification: effects on soil respiration, heterotrophic respiration, and soil physicochemical properties in mixed temperate forests in central New York, USA (2019-2022)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains soil physicochemical properties (soil pH, resin available nitrogen), soil temperature, in situ soil respiration, and heterotrophic respiration measured from laboratory incubations of soils collected from experimental plots. Soil pH was measured both pre-treatment (2009-2010) and after 8 and 11 years of experimental treatment. All other properties were measured between 9 and 12 years after treatment initiation.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen fertilized simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen and phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen and phosphorus fertilization conditions.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus in wet sedge tundra, 1994, Arctic LTER, Toolik Lake, Alaska.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus were measured in three wet sedge tundra experimental sites. Treatments at each site included factorial NxP and at the Toolik sites greenhouse and shade house. Treatments started in 1985 (Sag site) and in 1988 (Toolik sites).
Hubbard Brook Nitrogen Oligotrophication (HBNO): Foliar resorption, leaf mass per area, and leaf chemistry of sugar maple and American beech, 2020-2022
We quantified nitrogen (N) resorption of the two dominant tree species of northern hardwood forests along an elevation gradient using 14 sites at Hubbard Brook Experimental Forest, NH. For these calculations, we also quantified the leaf mass per area for both species, sugar maple and American beech. The original data before averaging for combining with chemistry data is available in an earlier revision of this dataset. Foliar N of sugar maple increased, and N resorption proficiency (NRP) decreased with increasing elevation. In contrast, foliar N and NRP of American beech did not vary significantly with elevation, suggesting that the mechanisms driving patterns of N resorption were distinct between these co-occurring species. While both species exhibited strong correlations between resorption efficiency of C and N, resorption of both elements was much greater for beech than maple. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Short-Term Effects on Vegetation in Soil Warming Plus Nitrogen Addition Experiment at Harvard Forest 2006
The purpose of this experiment is to examine the interactive affects of warming and N additions on plant diversity. The experiment consists of four treatments (control, heated +N, heated -N, +N only) with six replicates per treatment in a completely randomized design. Average soil temperature in the heated plots will be elevated 5 deg C above ambient by the use of buried heating cables placed at 10 cm depth in the soil and spaced 20 cm apart. The heating cables will be controlled by a data logger that monitors thermistors (5 per plot) every 10 min. Plots will automatically turn on and off to maintain a 5 deg C temperature difference between the heated and control plots. The N addition plots (heated +N, +N only) will be fertilized following the protocol of the Chronic Nitrogen Addition Study. An aqueous solution of NH4NO3 will be applied at a rate equivalent to the low N plots at the chronic N study (5 g m-2 yr-1). Fertilizer will be applied in equal monthly doses during the growing season (Apr-Oct). The control plots and unfertilized, heated plots (heated -N) will receive water only.
Umbilicaria Mammulata and Nitrogen Deposition at Harvard Forest 2006
Atmospheric N has quadrupled due to anthropogenic inputs and is projected to double again by 2050. The lichen Umbilicaria mammulata relies primarily on atmospheric deposition for nutrients, including N. Much of this species’ geographic distribution coincides with the N deposition gradient in the eastern US. We were interested in the potential impacts of N deposition on this species under realistic field conditions. Lichens were collected at six sites, ranging from Maine (low deposition) to West Virginia (high deposition). Thalli were analyzed for %C, %N, C:N, specific thallus weight (a measure of thickness), chlorophyll content, and chlorophyll fluorescence. Using stepwise regression we identified the most predictive model(s) using N deposition, environmental variables, and climate to predict differences in lichen ecophysiology and biochemistry across the gradient. While N deposition was important in predicting some responses, the model that best predicted %N in lichen only included mean minimum temperature, mean annual precipitation, and substrate slope. In controlled experiments at Grandfather Mountain (Linville NC) lichens were fertilized in situ with N to mimic deposition projected for 2050. There were no significant differences between control and fertilized thalli for any response variables, but differences did exist among sites. The results of this study highlight the complexities associated with sampling along a gradient of one factor in a multivariate environment.
Inorganic Nitrogen Pools and Tree Composition in Hemlock Removal Experiment at Harvard Forest 2007-2009
This study assessed how the change in leaf litter input and environmental conditions, from that of hemlock stands to earlier successional deciduous stands, will affect inorganic nitrogen availability at the interface of the mineral soil and LFH horizons. In early October 2007 we exchanged 0.5 x 0.5 m "loaves" of LFH horizons from hemlock and deciduous stands between forest types, while keeping control plots in the original forest stands. Inorganic nitrogen outputs from the litter loaves were monitored for 20 months using ion-exchange resins, removing and replacing the resins three times at 5 - 8 month intervals. Results indicated that there were differences between the control hemlock and deciduous loaves - NO3 and NH4 availability were higher in deciduous stands. However, the transfer of litter loaves from one stand type to another did not reveal changes in nutrient availability due to a shift in habitat. Rather, the methodology of moving litter loaves to a new site caused greater changes in nutrient availability, particularly over the summer months.
Nitrogen Deposition and Pitcher Plant Morphology in Massachusetts and Vermont 1998-1999
Atmospheric transport and deposition of nutrients, especially nitrogen, is a global environmental problem with well-documented consequences for ecosystem dynamics. However, monitoring nitrogen deposition is relatively expensive, monitoring stations are widely spaced, and estimates and predicted impacts of nitrogen deposition are currently derived from spatial modeling and interpolation of limited data. Bogs are nutrient-poor ecosystems that are especially sensitive to increasing nutrient input, and carnivorous plants, which are characteristic of these widespread ecosystem types, may be especially sensitive indicators of N deposition. Botanical carnivory is thought to have evolved in nutrient-poor and well-lit habitats such as bogs because the marginal benefits accruing from carnivory exceed the marginal photosynthetic costs associated with the maintenance of carnivorous organs. However, the production of carnivorous organs can be a phenotypically plastic trait. The northern pitcher plant, Sarracenia purpurea, produces leaves specialized for prey capture and nutrient uptake (pitchers) and leaves that are more efficient at photosynthesis (phyllodia). We hypothesized that relative allocation to these two types of leaves reflects ambient nitrogen availability. We manipulated nutrient availability to plants with leaf enrichment and whole-plot fertilization experiments. Increased nitrogen, but not phosphorus, reduced production of pitchers relative to phyllodia; this result provided empirical support for the cost–benefit model of the evolution of botanical carnivory. Because this phenotypic shift in leaf production occurs in ecological time, our results suggest that S. purpurea could be a reliable and inexpensive biological indicator of nitrogen deposition rates. This suggestion is supported by field observations across a geographic gradient of nitrogen deposition.
Soil Carbon Dioxide and Oxygen at the Soil Warming Plus Nitrogen Experiment at Harvard Forest since 2018
This dataset includes soil air CO2 and O2 concentrations measured from the organic/mineral horizon interface and 10, 30, and 50 cm depths of the mineral soil at SWaN using permanently installed stainless steel gas wells. Measurements were made 4-8 times throughout the year in 2018, 2019, and 2020.
Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at field collection time, 1989
This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted the following day. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week F (field) of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.
Nitrogen mineralization potential in soils collected from the Jornada Basin LTER-I transect and extracted at incubation time 0, 1989
This data package contains nitrogen mineralization data from soils collected along the Jornada Basin LTER (LTER-I) transects in southern New Mexico, USA. These transects are located in a livestock exclosure established in 1982 in the Chihuahuan Desert Rangeland Research Center (CDRRC) and run from the middle of the College Playa up to the foot of Mt. Summerford (2.7 km in length). Prior to the exclosure, the study site was moderately to heavily grazed for the past 100 years. The Treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Along each transect, 91 stations, each with a plant intercept line, are spaced at 30 meter intervals. For this dataset, 60 soil samples (total) were collected along the control and fertilized treatment transects and mixed with potassium chloride solution (KCl) on Nov 27, 1989, then filter extracted four days later to give a time = 0 incubation value. The dataset contains a soil moisture correction factor, sample weights, total inorganic nitrogen (NO3+NO2-N), and nitrogen in ammonium (NH4-N) for Week 0 of nitrogen mineralization potentials. The soil mineralization data complements the biomass harvest measurements that occurred in September 1989 (dataset knb-lter-jrn.210015001). This study is complete.
Soil and foliar carbon and nitrogen content and stable isotope ratios from rainfall manipulation experiments at the Jornada Basin LTER, 2011-2020
As rainfall extremes are expected to increase in novel magnitude and frequency, especially in dryland regions, we asked how prolonged and directional shifts to water availability may affect ecosystem carbon and nitrogen dynamics. This data set includes foliar and soil carbon and nitrogen stable isotope and concentration data collected from multiple long-term rainfall manipulation experiments at the Jornada Basin LTER. Datasets also include rainfall data adjusted to rainfall manipulation intensities. Collection dates range from 5 to 14 years since the onset of experimental treatments. The primary plant species targeted for this study were the dominant grass, Bouteloua eriopoda, and the dominant shrub, Prosopis glandulosa.
Dataset and analyses for publication entitled: “Acclimation of the nitrogen cycle to changes in precipitation”
This dataset contains data and analysis code for the paper entitled “Acclimation of the nitrogen cycle to changes in precipitation" by Currier et al. As the frequency of precipitation extremes are expected to increase, especially in arid regions, we asked how prolonged shifts in water availability facilitate acclimation of the N cycle in a semiarid grassland. Using natural abundances of stable nitrogen isotopes for dominant plants and soils and rainfall manipulation experiments, we tested the hypothesis that N cycling will interact with water availability further amplifying the openness of the N cycle through time. For the dominant plant species, we found the relationship for N availability vs. ambient annual precipitation to be significantly positive, contrary to global spatial models. We also considered the temporal dynamics of our experiments, which imposed directional rainfall manipulations in duration ranging from 5 to 14 years. The slopes of these relationships decreased (became less positive) with more time since the onset of the directional precipitation extremes. These data and metadata supplement long-term foliar and soil isotope data from the Jornada LTER (Dataset ID: knb-lter-jrn.210586001) with a large spatial dataset from NEON data package DP1.10026.001 and Craine et al. 2018 (https://doi.org/10.5061/dryad.v2k2607).
LAGOS - Lake nitrogen, phosphorus, stoichiometry, and geospatial data for a 17-state region of the U.S.
This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes greater than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).
Sediment Carbon and Nitrogen of Seagrass Restoration in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment carbon and nitrogen content in restored Z. marina meadows in Hog Island Bay and South Bay, VA. Sediments were sampled annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Carbon and Nitrogen in Seagrass Tissue from Virginia Coastal Bays, 2010-2021
This dataset contains measurements of carbon and nitrogen content of Z. marina tissue sampled in plots in the restored seagrass meadows in Hog Island Bay and South Bay, VA. Samples were collected annually during late June-early July. GPS locations of sampling plots are available in the companion data set VCR11180.
Particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition of seawater sampled during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition (delta 13C and delta 15N) sampled during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. Water samples were collected from the underway seawater supply every 3 hours, filtered onto pre-combusted glass fibre filters, acidified to remove inorganic compounds and analysed for both elements on the same filter using an elemental analyser. These samples provide an estimate of the organic carbon and organic nitrogen concentration and carbon and nitrogen stable isotope composition of living and detrital particles > 0.7 micrometres in size.</p> <p><strong>Dataset contents</strong></p> <ul> <li>README.txt, metatdata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_uw_poc_pon_blanks_20200512CURRSGCMR.csv, data file, comma-separated values</li> <li>ace_uw_poc_pon_20200512CURRSGCMR.csv, data file, comma-separated values</li> </ul>
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>· Carbon and nutrients data for 5470 continental French catchments</p> <p>· Modelled discharge for 5128 of catchments out of 5470</p> <p>· Geopackages with catchment delineations and outlets</p> <p>· DEM conditioned to delimit additional catchments</p> <p>· 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. Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.</p> <p><strong>Data sources & 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 (DEM) conditioned by the hydrographic network and elementary catchments’ 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 & Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of “obvious outliers”, 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 & Andréassian, 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&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 | 4 992| 658 147| 14.3| 9.0|</code></p> <p><code>|DSi | 3 299| 333 866| 12.9| 8.3|</code></p> <p><code>|NH4 | 5 318| 907 343| 19.3| 8.7|</code></p> <p><code>|NO2 | 5 264| 891 886| 19.2| 8.6|</code></p> <p><code>|NO3 | 5 465| 939 279| 19.0| 9.0|</code></p> <p><code>|SRP | 5 361| 910 107| 19.1| 8.7|</code></p> <p><code>|TOC | 935| 111 993| 13.6| 9.6|</code></p> <p><code>|TP | 5 199| 802 841| 17.1| 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 “fraction” 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 (< 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 > 1000 km<sup>2</sup>. The criteria retained for a direct interpolation is 0.8*area_discharge_station < area_quality < 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 & processing and File descriptions.</p> <p><strong> </strong></p> <p><strong>File and attributes descriptions: </strong></p> <p>The key “sta_code” is present across all files. For time varying records, “date” can be a secondary key. </p> <p><strong>Description of CNPSi.csv data attributes</strong></p> <p>Each line is a couple measurement/parameter/station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· fraction: "water_filtrated" or "water_raw"</p> <p>· date: date of sampling</p> <p>· hour: hour of sampling</p> <p>· value: analytical result (concentration)</p> <p>· provider: provider of the data</p> <p>· producer: producer of the data</p> <p>· from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un système d’information sur les observations environnementales. Présentation Séminaire UMR Métis)</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· unit: unit of concentration</p> <p>· element: "C" "N" "P" or "Si"</p> <p>· year: year of observation</p> <p>· month: month of observation</p> <p>· day: day of observation</p> <p>· julian_day: julian day observation (1-366)</p> <p>· decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")</p> <p> </p> <p> </p> <p><strong>Description of CNPSi_stats.csv data attributes</strong></p> <p>Each line is a couple parameter / station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· start_year: year of first observation for a given parameter / station</p> <p>· end_year: year of last observation for a given parameter / station</p> <p>· duration_y_tot: total duration of observation in years for a given parameter / station</p> <p>· duration_y_tot: duration of observation in years for a given parameter / station for years with at least 1 meas</p> <p>· mean_nmeas_per_y_tot: mean number of observations per year considering total duration</p> <p>· mean_nmeas_per_y_meas: mean number of observations per year considering years with measurements</p> <p>· is_fully_continuous: TRUE if at least one measurement per year for a given parameter / station</p> <p>· start_cont_seq: year in which starts the longest continuous sequence for a given parameter / station</p> <p>· end_cont_seq: year in which ends the longest continuous sequence for a given parameter / station</p> <p>· duration_y_cont_seq: duration in years for the longest continuous sequence for a given parameter / station</p> <p>· nmeas_cont_seq: number of measurements for the longest continuous sequence for a given parameter / station</p> <p>· mean_nmeas_per_y_cont_seq: mean number of observations per year for the longest continuous sequence for a given parameter / station</p> <p>· mean: mean value (concentration) for a given parameter / station</p> <p>· median: median value (concentration) for a given parameter / station</p> <p>· sd: standard deviation (concentration) for a given parameter / station</p> <p>· cv: coeficient of variation (concentration) for a given parameter / station</p> <p>· c05,c25,c50,c75,c95: centiles 5, 25, 50, 75 & 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>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· watercourse: Name of the water course (from spatial join on IGN BD Topo)</p> <p>· mun_name: Name of the municipality of the outlet (from spatial join on IGN BD Admin Express)</p> <p>· ccm_wso_id: Seaoutlet id from CCM v2.1 database</p> <p>· ccm_wso1_id: Elementary catchment id from CCM v2.1 database</p> <p>· ccm_strahler: Strahler order of the catchment from CCM v2.1 database</p> <p>· 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>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· date: Date in format yyyy-mm-dd</p> <p>· flow_mm: Discharge expressed in mm.d-1</p> <p>· 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>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· period: 1990-2019</p> <p>· source: EMO-1 (pr) & EMO-5 (tmin, tmax)</p> <p>· pr: mean yearly precipitation (mm)</p> <p>· tmin: mean daily minimal temperature (°C)</p> <p>· tmin: mean daily maximal temperature (°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>· clc_44class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o clc_name: Description of land cover class in CLC product</p> <p>o clc_code: Code for land cover class in CLC product</p> <p>o percent_cover: Percent cover by CLC class in the catchment (0-100)</p> <p>· clc_8class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o code_clc_8class: Code for land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o percent_cover: Percent cover by aggregated CLC class in the catchment (0-100)</p> <p>· clc_44class_to_8class.csv</p> <p>o code_clc: Code for land cover class in CLC product (44 classes)</p> <p>o code_clc_8class: Code for land cover class in aggregated CLC product (8classes)</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p> </p> <p> </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 Andréassian 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> </p> <p><strong>References</strong></p> <p>de Lavenne, A., Skøien, J. O., Cudennec, C., Curie, F., & 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), 5555–5576. https://doi.org/10.1002/2016WR018716</p> <p>de Lavenne, A., Loree, T., Squividant, H., & Cudennec, C. (2023). The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology. Environmental Modelling & 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 édition 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., & Andréassian, V. (2020). Hydrograph separation: An impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences, 24(3), 1171–1187. <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). Complétion d'hydrogrammes avec le modèle GR4J - Note méthodologique. INRAE, UR HYCAR.</p> <p>Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, 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, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022</p> <p>Thieu, V., Silvestre, M., 2015. DoNuts : un système d'information sur les observations environnementales. Présentation Séminaire UMR Métis</p> <p>Vogt, J., A. de Jager, E. Rimaviciute, W. Mehl, S. Foisneau, K. Bódis, J. Dusart, M.L. Paracchini, P. Haastrup, & 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>
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