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451 results for “Catchments”
Stormwater Nitrogen in Arizona (SNAZ): runoff and stormwater-mediated export from urbanized catchments within the greater Phoenix metropolitan area, Arizona, USA (2010-2012)
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Data and expertise garnered by stormwater monitoring near the outflow of the IBW helped pave the way for a more expansive stormwater research effort facilitated by a leveraged grant from the National Science Foundation (DEB-0918457, NSF Eco
LAGOS - Chlorophyll, TP, and water color summer epilimnetic concentrations and lake and catchment data for inland lakes in WI, MI, NY, and ME – a subset of lake data from LAGOSLimno v.1.040.1
This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majo
Climate data for saddle catchment sensor network, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45 hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley.
Mercury in soil, vegetation, and organisms across Niwot Ridge, Saddle Catchment, and Green Lakes Valley, 2020 - 2023.
This dataset includes soil, vegetation, water, atmospheric deposition, litterfall, incubation, and organism data from the Niwot Ridge, Saddle Catchment, and Green Lakes Valley collected during 2020 and 2021 to investigate the storage, transformation, and mobilization of mercury in the Colorado Rocky Mountains. During Summer 2020, we collected soil cores (10cm x 3cm) across vegetation plant functional groups in wet meadows, moist meadows, dry meadows, krummholz, subalpine forest, shrub areas, as well as at the inlet and outlet of the Green Lakes in Green Lakes Valley. At each of these sites, we collected leaves from forbs, graminoids, and shrubs, as well as litter (and moss if present). For organisms, we sampled pika hairs from nine different pika trapped on the West Knoll, in addition to caddisfly pupae found in wet meadows in the Saddle Catchment. We analyzed hairs from weasel specimens at the CU Boulder Natural History Museum that were trapped either on, or near, Niwot Ridge. Finally, we analyzed dust samples collected by Dr. Ruth Heindel in 2018 and 2019 on Niwot Ridge. We analyzed soil samples for organic matter; pH; water content; percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed vegetation samples for percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed organism and dust samples for total mercury and methylmercury. During Spring 2021, we collected composite snow cores from 4 sites in the Saddle region and 3 sites in the subalpine forest. We measured snow depth and density to calculate snow water equivalent and then analyzed these samples for sulfate, nitrate, chloride, dissolved organic carbon, dissolved organic nitrogen, total mercury, and methylmercury concentrations. During Summer 2021, we collected soil cores (10cm x 3cm) every other week from June through September from a solifluction lobe, alpine wet
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>
Hydrogeological data of groundwater and precipitation monitored in the Vögelsberg landslide catchment
<p>Data contains hydrogeological data of precipitation and groundwater within the catchment of the Vögelsberg landslide (Tyrol, Austria) monitored between 2017-11-22 and 2021-07-05. The dataset provides time series of discharge, temperature, electrical conductivity and stable isotope ratios in groundwater and precipitation. Dataset is associated to following preprint: “Pfeiffer, J.; Zieher, T.; Schmieder, J.; Bogaard, T.; Rutzinger, M. and Spötl, C. (2021) Spatial assessment of probable recharge areas - Investigating the hydrogeological controls of an active deep-seated gravitational slope deformation, Natural Hazards and Earth System Sciences Discussions, Vol. 2021, p. 1-29, <a href="https://doi.org/10.5194/nhess-2021-388">https://doi.org/10.5194/nhess-2021-388</a>”. Accompanying readme file gives a detailed description of data fields contained in the published data.</p>
Land cover in the Purapel fluvial catchment
<p>The dataset contains 6 Land Cover maps at a 30m/pixel spatial resolution for the Purapel river catchment located in South-Central Chile. They were generated for the summer periods of 1986, 2000, 2005, 2010, 2015 and 2017.</p> <p>Maps of 1986-2015 were generated using atmospherically corrected Landsat CDR Scenes (<em>images courtesy of the U.S. Geological Survey</em>) including VNIR and SWIR bands from the TM5, ETM+ and OLI sensors and vegetation indices as auxiliary bands to highlight phenological differences among covers. Specifically the Normalized Difference Vegetation Index (NDVI) (Rouse et al,. 1974), the Green NDVI (Gitelson et al., 1996) and NDVI winter-summer Difference Index (ΔNDVI).</p> <p>Training and validation points were defined from field trips to the area in 2014-2015, various mid resolution satellite imagery sources and high-resolution Google Earth imagery (Map data ©2015 Google) when available. A topographic correction was applied using the C-Correction method (Teillet et al 1982), as proposed by Hantson and Chuvieco (2011), and the SRTM v3 DEM to account for the effect of local relief in the scene’s lighting.</p> <p>Accuracy assessment resulted in Overall Accuracy (OA), ranging from 82% to 92% (table 1).</p> <p>Table 1. Overall Accuracies for Land Cover maps from 1986 to 2017</p> <table> <tbody> <tr> <td> <p>Year</p> </td> <td> <p>OA</p> </td> </tr> <tr> <td> <p>1986</p> </td> <td> <p>89.7</p> </td> </tr> <tr> <td> <p>2000</p> </td> <td> <p>92.2</p> </td> </tr> <tr> <td> <p>2005</p> </td> <td> <p>91.5</p> </td> </tr> <tr> <td> <p>2010</p> </td> <td> <p>89.8</p> </td> </tr> <tr> <td> <p>2015</p> </td> <td> <p>82.7</p> </td> </tr> <tr> <td> <p>2017</p> </td> <td> <p>0.98</p> </td> </tr> </tbody> </table> <p> </p> <p>The 2017 map was generated using Random Forest classifier using several SI from Sentinel 2, Sentinel 1 C-band radar data (imagery from European Space Agency courtesy of the U.S. Geological Survey) and hydro-geomorphic indices obtained from 2009 LiDAR DTM data (Tolorza et al., 2022). Ninety polygons were used for training and thirty polygons and the classification of Zhao et al. (2016) were used for validation, obtaining an overall accuracy 0.98 (table 1).</p> <p> </p> <p>The 7 land cover classes defined following these codes and land use / covers:</p> <ul> <li>0 = Unclassified</li> <li>1 = Others (mainly crops and natural prairies in riverbeds)</li> <li>2 = Native Forest (mainly secondary-growth deciduous Nothofagus sp. Stands)</li> <li>3 = Shrubland (highly degraded formation of xerophytic and sclerophyllous shrubs such as <em>Acacia</em> <em>caven</em>, <em>Quillaja</em> <em>saponaria</em> and <em>Lithraea</em> <em>caustica</em>, among others).</li> <li>4 =Tree Plantations (industrial monocultures of <em>Pinus</em> <em>radiata</em> and <em>Eucalyptus</em> spp. of various age and development)</li> <li>5 = Seasonal grassland (annual pastures which wither in summer and urban areas)</li> <li>6 = Clear cuts (bare lands within industrial forestry surface)</li> </ul> <p>Codes 7 to 9 are specific to 2015 y 2017 because of the occurrence of two large (>5,000 hectares) fire events, and represent different Fire Severity levels based on the dNBR index (López and Caselles, 1991) according to Key and Benson (2006). They represent the following cases:</p> <ul> <li>7= Low Severity fire</li> <li>8 = Moderate severity fire</li> <li>9 = High severity fire</li> </ul> <p> </p> <p>Sources:</p> <p>Hantson, S. Chuvieco, E. 2011. Evaluation of different topographic correction methods for Landsat imagery. International Journal of Applied Earth Observation and Geoinformation 13:691-700.</p> <p>Rouse, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with erts. Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351, compiled and edited by S.C. Freden, E.P. Mercanti, and M.A. Becker. Washington, DC: National Aeronautics and Space Administration</p> <p>Gitelson, A., Y. Kaufman, and M. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298.</p> <p>Teillet, P., B. Guindon, and D. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8:84–106.</p> <p>Key, C. Benson, N. 2006. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. Pp: 1-51.</p> <p>López, MJ. Caselles, V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International (1) 1991: 31- 37.</p> <p>Tolorza, V. Poblete-Caballero, D. Banda, D. Little, C. Galleguillos, M. 2022. An operational method for mapping the composition of post-fire litter. Remote Sensing letters (13) 2022: 511-521. 10.1080/2150704X.2022.2040752</p> <p> Zhao, Y. D. Feng, L. Yu, X. Wang, Y. Chen, Y. Bai, H. Hernández, et al. 2016. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data. Remote Sensing of Environment 183: 170–185. 10.1016/j.rse.2016.05.016.</p>
LTER-Italy site Saldur River Catchment figure
<p>Geographical representation of the LTER-Italy site Saldur River Catchment (LTER_EU_IT_099) - DEIMS-ID <a href="https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1">https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1</a></p>
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
Marcell Experimental Forest chemistry of surface water draining the S2 catchment, 1986 - ongoing
This data set is a record since 1986 of chemistry for surface water draining the S2 catchment at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. Unfiltered water is usually collected every one or two weeks as part of the long-term monitoring program of the S2 catchment. Some samples were collected more often for various other studies and are included in this data set. Samples are routinely measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, manganese, strontium), silicon, nutrients (ammonium, nitrate+nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon. Occasionally, stable water and mercury isotopes as well as concentrations of dissolved organic carbon (DOC), bacterial respiration of dissolved organic matter, biodegradable DOC (BDOC), ferrous and ferric iron, total mercury (filtered or unfiltered), methylmercury (filtered or unfiltered), and lead were measured. Ultraviolet (UV) absorbance, a measure of water color or dissolved organic matter optical properties, was also measured for some samples. More solutes and values will be added as additional metadata are documented (pre-1986 to 1992), water samples are collected and analyzed (concentrations and isotopes), or archived water samples are analyzed for stable water isotopes. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station.
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Hågaån Catchment Continuous Sensor Data, Uppsala, Sweden, 2018-2023
This data package includes continuous sensor data used to model stream metabolism and carbon dioxide emissions from the Hågaån stream catchment in Uppsala, Sweden. Data is in 30 minute increments from 2018 to 2023 and includes discharge, water temperature, turbidity, specific conductivity, temperature, dissolved carbon dioxide, and dissolved oxygen. The data package is complete.
Eddy flux measurements during 2015 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts.
We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for all July to October 2015. This site was established in 2015.
Eddy flux measurements during 2016 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.
We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for all July to October 2016. This site was established in 2015.
Eddy flux measurements during 2017 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.
We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for all July to October 2017. This site was established in 2015.
A 2-minute rainfall (12 locations) and discharge time series at the Vallon de Nant catchment, Switzerland, for 2018 summer seasons
<p>The data set contains rainfall time series within the experimental 13.4 km² Vallon de Nant catchment, Switzerland (Michelon et al., 2020), from June 30th to September 23rd 2018 at 12 locations. A network of <em>Pluvimate</em> drop-counting raingauges (www.driptych.com) measured continuously the rainfall intensity at a 2-minute resolution. Operation and characteristics of the raingauges are detailed in Benoit et al. (2018) and Michelon et al. (2020), and the rating curve is described by Ceperley et al. (2018).</p> <p>Description of the files:</p> <ul> <li><em><strong>data.csv</strong></em> contain the rainfall intensities for the observation period, along with the main river discharge measured at the <a href="https://map.geo.admin.ch/?lang=fr&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.zeitreihen,ch.bfs.gebaeude_wohnungs_register,ch.bav.haltestellen-oev,ch.swisstopo.swisstlm3d-wanderwege,KML%7C%7Chttps:%2F%2Fpublic.geo.admin.ch%2FaLKDanGXRPGMpB_D51f2Tg&layers_visibility=false,false,false,false,true&layers_timestamp=18641231,,,,&E=2574619.27&N=1122462.26&zoom=8">outlet</a> over the same 2-minutes time step as the rainfall intensity. We also provide areal rainfall intensity aggregated over the whole catchment:<br> Columns: <ul> <li>year [-]</li> <li>month [-]</li> <li>day [-]</li> <li>hour [-]</li> <li>minute [-]</li> <li>specific discharge 95% inf. [mm/day]: inferior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge 95% sup. [mm/day]: superior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge mean [mm/day]: mean value of the specific discharge over 2 minutes</li> <li>specific discharge median [mm/day]: median value of the specific discharge over 2 minutes</li> <li>P St. #X [mm]: rainfall amount measured at the station X over 2 minutes</li> <li>P stochastic mean [mm/h]: rainfall amount interpolated over the whole catchment over 2 minutes</li> <li>P stochastic std [mm/h]: standard deviation of the stochastic rainfall interpolation, over 2 minutes</li> </ul> </li> <li><strong><em>stations.csv</em></strong> describes the raingauge locations.<br> Columns: <ul> <li>Station ID [-]</li> <li>lon [WGS84]: decimal longitude of the station into WGS84</li> <li>lat [WGS84]: decimal latitude of the station into WGS84</li> <li>E [CH1903]: east coordinate into Swiss Coordinate System</li> <li>N [CH1903]: north coordinate into Swiss Coordinate System</li> <li>elevation [m asl]: altitude of the station in meters above the sea level</li> <li>data in 2017 [-]: flag if the station was working over the 2017 observation period</li> <li>data in 2018 [-]: flag if the station was working over the 2018 observation period</li> </ul> </li> <li><em><strong>rainfall_viewer.m</strong></em> is a <em>MatLab</em> script (created with <em>MatLab 2017b</em>) which allows the joint visualization of the rainfall intensities and river discharge. It produces a composite figure with the following plots: <ul> <li>On top the general hydrograph over the whole observation period [mm/day]. The red dashed lines mark out period that the other plots are focus on. The shaded orange areas correspond to when the river stage data was not available.</li> <li>Below, the zoomed hydrogram show a detailed view of the river discharge (and uncertainty). In case a river reaction is associated, the discharge event is marked out by red dashed lines. Between these vertical lines is drawn a line joining the initial and final baseflow, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff part.</li> <li>In the middle a zoomed magnification of the hydrograph that shows a detailed view of the discharge in the river [mm/day]. When a river response is associated, the discharge event is marked with dashed red lines. Between these vertical lines a line joining the initial and final baseflow is drawn, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff.</li> <li>At the bottom are shown the rainfall recorded by each of the 12 rain gauges (the y-axis scale between 2 stations is about 20 mm/h). The rainfall event is marked out by green dashed lines.</li> <li>Above is shown the rainfall amount (and uncertainty) interpolated over the catchment using the stochastic method. The rainfall event is marked out by green dashed lines.</li> <li>On the left, a map with the 12 raingauge locations show the total amount of rainfall recorded by each station during the event (a red cross shows missing data).<br> <br> It is possible to zoom in the plots by clicking with the left and right mouse buttons to define respectively the starting and ending of the visualization window. The middle button defines a third time reference used to identify rainfall intensity peaks or discharge peaks. Statistics concerning the visualization period are displayed on the MatLab console.<br> Pressing [enter] will save the figure into a PNG file named with the starting and ending dates of the visualization window.</li> </ul> </li> <li><strong><em>Q_stats.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong><em>print_figure.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong>data.mat</strong> is a MatLab data file with all data required by the main code rainfall_viewer.m</li> </ul>
Monthly time series of rainfall, potential evapotranspiration and streamflow for 201 catchments in South-East Australia
<p>The data set contains data for 201 catchments located in South-Eastern Australia. The data was extracted from the datasets collated by Lerat, Thyer et al. (2020) including rainfall and potential-evapotranspiration data obtained from the Bureau of Meteorology Australian Water Outlook website (Frost, Ramchurn et al. 2016) and streamflow data obtained from the Bureau of Meteorology Water Data Online website (Bureau of Meteorology 2019). The data was collected over the period from 1980 to 2018, split into the two sub-periods 1980-1999 (Period 1) and 1999-2018 (Period 2).</p><p> </p><p>Bureau of Meteorology. (2019). "Water Data Online." from <a href="http://www.bom.gov.au/waterdata">http://www.bom.gov.au/waterdata</a>.</p><p>Frost, A. J., A. Ramchurn and A. Smith (2016). "The bureau's operational AWRA landscape (AWRA-L) Model." Bureau of Meteorology Technical Report.</p><p>Lerat, J., M. Thyer, D. McInerney, D. Kavetski, F. Woldemeskel, C. Pickett-Heaps, D. Shin and P. Feikema (2020). "A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data." Journal of Hydrology<strong>591</strong>: 125129.</p>
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </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.