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739 results for “groundwater”
Composition and biodegradability of dissolved organic matter in supra-permafrost groundwater and surface waters near Simpson Lagoon, Alaska
Supra-permafrost groundwater (SPGW) is an important source of terrestrial dissolved organic matter (DOM) to the Arctic Ocean, yet few studies have investigated the quality or characteristics of this DOM. We sampled fresh SPGW, run-off, and rivers near Simpson Lagoon, Alaska during spring ice break-up (mid-June), summer open water (late July), and fall freeze-up (late September - early October). We measured dissolved organic carbon (DOC) concentrations in these samples and analyzed the composition of DOM using high-resolution mass spectrometry (Fourier transform ion cyclotron resonance mass spectrometry; FT-ICR MS). To measure biodegradable dissolved organic carbon (BDOC), we conducted an aerobic incubation experiment following the methods suggested by Vonk et al. (2015). Briefly, water samples were incubated at 20C for 28 days to measure DOC loss due to remineralization by in-situ microbial communities.
Daily summer riverine and groundwater pCO2, dissolved oxygen, and ecosystem metabolism from sites along the Alaska Beaufort Sea coast, 2019-2022
Three riverine systems near Utqiaġvik, Alaska, are sampled regularly by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program in order to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Sampling includes one intermediate river, Avak Creek, and two smaller streams: the Mayoeak River (thermokarst stream) and a beaded stream. In 2019, 2021, and 2022, we performed additional sampling at these core program sites, with continuous monitoring of the streams between late June and August. Continuous measurements included riverine discharge, pCO₂, dissolved oxygen (DO), and ecosystem production, along with periodic grab samples of dissolved organic carbon (DOC). In 2022, there was also monitoring of groundwater wells at the two streams, capturing continuous hydraulic head and groundwater pCO₂, in addition to grab samples for DOC. Daily averages of continuous data and DOC laboratory results are presented for surface water and groundwater. This dataset is not part of the BLE Core Program and is not currently planned as an ongoing effort.
Ramped Pyrolysis Oxidation (RPO) coupled radiocarbon (14C-DOC) and stable carbon (13C-DOC), high-resolution molecular composition (FT-ICR MS), and biodegradable dissolved organic carbon (BDOC) of groundwater, river water, and lagoon water in northeast Alaska, 2017
Supra-permafrost groundwater (SPGW), river water, and lagoon water were sampled near Kaktovik, AK to assess the reactivity and origin of dissolved organic matter (DOM) across interconnected hydrologic systems during late summer. Water samples were collected on August 17th 2017 from SPGW along the beach of Jago Lagoon (Jago GW), surface water from the Jago River’s main channel above tidal influence (Jago R), and from the water column of Kaktovik Lagoon at 2–3 m depth (KA LW). Measurements were made from grab samples for river and lagoon water, and from a composite sample for SPGW gathered from 10 individual shoreline locations. Data include dissolved organic carbon concentration (DOC, mg C L-1), Ramped Pyrolysis Oxidation (RPO) derived fraction compositions of 13C-DOC (δ13C ‰), 14C-DOC (in fraction modern), and method/instrumental error in the 14C and 13C results, and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) molecular composition and summarized compound classes. Biodegradable DOC (BDOC) bottle experiments were performed using all three sample types, where DOC concentration was subsequently measured at 2, 7, 14, and 28 days. FT-ICR MS composition was subsequently measured at the 28-day timepoint to track changes in molecular formulae and compound class relative abundance following biodegradation. Data from RPO serial thermal oxidation include temperature and normalized CO2 profiles for each background sample. Thermal-oxidation profiles of CO2 were transformed into non-parametric activation energy (E) distributions using an inverse model. Model output includes C mass of oxidized CO2 (µg C), Tmax (K), Emax (kJ mol-1), Emean (kJ mol-1), Estd (kJ mol-1), and p(0,E)max of user-defined sample fractions. FT-ICR MS results include a summary table of the relative abundance of compound classes (e.g., unsaturated phenolic, polyphenolic, aliphatic, condensed aromatics, peptide-like) and elemental groupings (e.g., CHO-type, CHON-type, CHOS-type, CHON
North Temperate Lakes LTER Estimated winter inputs of stream water and groundwater to primary study lakes 1982 - 2014
This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day. Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5. Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate... Hunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2. Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.
Soil dissolved organic matter and greenhouse gas fluxes from intact Delmarva Bay wetland soil cores during laboratory simulation of groundwater level rise
Wetlands in low-relief landscapes have dynamic terrestrial-aquatic interfaces as surface water and groundwater levels fluctuate seasonally and these variable water levels influence wetland carbon cycling. Seasonal changes in groundwater levels determine which soils are hydrologically connected to the wetland surface water-groundwater continuum and therefore which soils act as carbon sources. To quantify groundwater-mediated soil dissolved organic matter (DOM) and greenhouse gas (CO2 and CH4) fluxes, we performed a laboratory simulation of groundwater rise on intact soil cores. Soil cores were collected from four Delmarva Bay wetlands located in the low-relief landscape of the Delmarva Peninsula in the Mid-Atlantic United States. At each wetland, two cores (length = 60 cm, diameter = 10.2 cm) were collected: one from within the wetland basin and the second from the transitional zone near the edge of the wetland basin (total number of cores collected = 8). To characterize seasonal hydrologic conditions at each wetland site where intact soil cores were collected (e.g., mean water level, number of saturation events, duration of saturation), we used high frequency water level data collected in wetland center and upland groundwater monitoring wells. In the lab, cores were re-saturated with groundwater over 15 days and after cores were fully re-saturated, core water levels were maintained for an additional 25 days. Water levels in each head tank and soil core were manually recorded on sub-daily timesteps. Rhizon soil porewater samplers were installed at 8, 20, and 40 cm below the soil surface reflecting the expected depths of the O, A, and B soil horizons across the wetland sites. Source groundwater, soil porewater, and exfiltrated surface water samples were collected daily from the soil cores and analyzed for pH, ORP, and DOM concentration (dissolved organic carbon) and DOM composition (absorbance and fluorescence metrics). Discrete measurements of CO2 and CH4 fluxes were
Groundwater levels and temperature in coastal tundra adjacent to Simpson Lagoon, Alaska, 2022
The coastal tundra adjacent to Simpson Lagoon, along Alaska's Beaufort Sea coast, was visited in July and September/October 2022 to assess coastal groundwater dynamics along the lagoon. Groundwater levels were monitored in five piezometers aligned in a transect oriented perpendicular to Simpson Lagoon, as well as surface water levels in the lagoon. Loggers were deployed on July 22, 2022, and collected on September 29, 2022. Data include surface water and groundwater elevation data from July 22, 2022, to September 29, 2022, with the elevation relative to a local datum. All levels have been corrected for barometric pressure (see methods). Also included is groundwater temperature data from the well closest to the lagoon (Well 5).
North Temperate Lakes LTER: Groundwater Chemistry 1984 - current
Water chemistry is measured annually in 11 monitoring wells to characterize regional groundwater chemistry in the Trout Lake area. The chemical parameters measured include pH, conductivity, total alkalinity, disolved inorganic and organic carbon, total nitrogen, nitrae, ammonia, total phosphorus, calcium, magnesium, soium, potassium, chloride, sulfate, iron, manganese, total silica and dissolved reactive silica. Chemical data areavailable at a quarterly sampling frequency for some years. In addition (see related data set - Groundwater Level), water levels in 37 monitoring wells are measured several times per year. The wells are scattered throughout the Trout Lake hydrological basin and the data are used to calibrate and test regional groundwater flow models. Sampling Frequency: annually - with some earlier data from quarterly sampling Number of sites: 11
North Temperate Lakes LTER: Groundwater Levels 1984 - current
Water levels in monitoring wells are measured several times throughout the year. The number of monitored wells has ranged over the study period from 19 to 44 wells. Currently, 37 wells are being monitored 4 - 5 times per year. The wells are scattered throughout the Trout Lake hydrological basin and the data are used to calibrate and test regional groundwater flow models. In addition (see related data set - Groundwater Chemistry), water chemistry is measured annually in a subset of 11 of these wells to characterize regional groundwater chemistry in the Trout Lake area. Sampling Frequency: varies - generally from 4 - 9 times a year Number of sites: 44
Groundwater Levels on Hog Island, VA. 2007-2021
This dataset contains hourly measurements of groundwater levels on Hog Island, VA - off the Atlantic Coast of the Delmarva Peninsula. It continues measurements of groundwater levels that were initiated in by Frank Day in July 1990 (dataset VCR99066) and by Mark Brinson in October 1997 (dataset VCR05130). Refer to these datasets for previous measurements. The groundwater measurements use the same wells and locations, but differ in sensor technology and frequency of measurement. In August 2007, a system of wirelessly-connected data loggers designed by Thomas Williams was installed with help from Arthur Schwarzschild, Chris Buck, John Hayward and John Porter. The system was decomissioned December 31, 2021
Groundwater, soil moisture, light and weather data in Brownsville forest, Nassawadox, VA, 2019-2023
This dataset includes groundwater, soil moisture, weather and light data collected in eight study sites in the Brownsville forested area (VA). In particular, sites H5 and H7 characterize the high forest where healthy Pinus taeda dominate, sites L1 and L6 characterize the low forest, where barren or dead Pinus taeda are present, sites M1 and M2 characterize the medium forest, representing transition between high and low forest, transition site where dead trees and saltmarsh vegetation live together and marsh site dominated by saltmarsh vegetation. Data collection, started in January 2019, is done for VCR-LTER and CCZN (Coastal Critical Zone Network) long term projects. CTD-Diver are used to measure groundwater pressure, specific conductance and temperature. They hang from a cable in six wells, one for each study site. Water pressure is compensated using barometric pressure data collected by the weather station nearby. Water levels are georeferenced to NAVD 88. Two soil moisture sensors for each site are placed 7-10 cm and 30 cm below the ground surface to detect water content, specific conductance and temperature of the first soil layer. Two weather stations are always installed in different sites. Their position is periodically changed to cover all sites. This material is based upon work supported by the National Science Foundation under Grant No. 2012322, Collaborative Research: Network Cluster: The Coastal Critical Zone: Processes that transform landscapes and fluxes between land and sea.
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>
Dissolved CO2, CH4, and ions in groundwater from five sites in the NEON network (CARI, COMO, KING, MART, WALK), U.S., 2021-2024.
This package contains groundwater chemistry measurements collected from groundwater wells between June 2021 – May 2024 at five sites in the U.S. National Ecological Observatory Network (NEON): CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN. The dataset includes concentrations of dissolved gases (CO2 and CH4), ions (F, Cl, NO2, Br, NO3, PO4, SO4, Na, NH4, K, Mg, Ca), silica (Si), and nutrients measured by colorimetric methods (NO3, NH4, PO4). When available, we also report measurements of water temperature, specific conductivity (SpC), pH, dissolved O2, and barometric pressure. Sample collection and analysis was conducted across three labs with additional assistance from the NEON Research Support Services program. Whenever possible, we matched our field sampling methods to NEON’s protocols for groundwater sampling to ensure samples would be comparable to preexisting data from these sites. Any deviations from these protocols are described in the methods.
Regional E-Atlas of the Greater Phoenix Region: Areas of significant agricultural and residential groundwater use, 1996-2000
Spatial distribution of well water usage (groundwater) for the period 1996 - 2000. These data present significant areas of agricultural and residential groundwater use during this period. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Database of Geographic Information: Change in groundwater level, Central Arizona-Phoenix, 1985-2000
Change in Level of Groundwater in central Arizona-Phoenix, 1985-2000. This file shows spatial changes in groundwater levels for two separate time periods: 1985-1989 and 1996-2000. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Continuous groundwater well temperature, salinity and water level measurements at the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx) site from May 2014 to February 2018
The Georgia Coastal Ecosystems LTER Seawater Addition Long-Term Experiment (SALTEx) is a large-scale field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. In order to characterize groundwater salinity, temperature, and plot flooding following experimental manipulation, unvented water pressure, temperature and conductivity were continuously measured in a PVC groundwater well installed at the SALTEx site. Measurements were made at the bottom of the well using a submerged Schlumberger CTD-Diver logger every 15 minutes from 30-May-2014 to 14-Feb-2018. In February 2016 a second CTD-Diver was deployed near the top of the well. Data were downloaded from the loggers using Diver Office communication software, then imported into MATLAB for post-processing, quality control and documentation. Raw, unvented pressure readings were corrected for atmospheric pressure and sensor height from the bottom of the well to generate corrected pressure readings, then water level, salinity and density were calculated from the measured variables using UNESCO algorithms. These data were collected as part of the Georgia Coastal Ecosystems LTER SALTEx project (http://gce-lter.marsci.uga.edu/public/app/send_project_eml.asp?id=73), and will be updated annually.
AGW03 Konza Prairie Long-term high frequency groundwater level and temperature from wells on N04d
The objectives of this project are to quantify the seasonably variable timing among meteoric precipitation, groundwater recharge, and groundwater temperature. Hypotheses are: 1. Because of the karst-like characteristics of the aquifers in N04d (and by extension, the entire region), recharge will be rapid during moderately large precipitation events where fractures are enlarged by dissolution and therefore highly conductive, except during the most active part of the growing season. 2. The recharge efficiency of the aquifers will be spatially variable, with highest hydraulic conductivity (because of solution enlargement of fractures) near the riparian zone. 3. Groundwater temperature will vary continuously over the year and also demonstrate abrupt changes after recharge-effective precipitation events when the precipitation temperature is different from the groundwater temperature. Because the aquifers are merokarst and recharge is rapid, data are recorded at high frequency (5 minute intervals).
shallowgroundwater: estimated zones with shallow groundwater in the Flemish region of Belgium
<p><strong>General</strong></p> <p>The data source <code>shallowgroundwater</code> is a geospatial dataset of multipolygons that represent the estimated areas, in the Flemish region of Belgium, where the mean lowest groundwater level (MLW; Knotters & Van Walsum, 1997; Van Heesen, 1970) is less than approximately 2 m below soil surface (hence, “shallow” groundwater). We expect groundwater dependent species and communities to be present within these areas. Outside these areas we assume they are groundwater independent. We combined several data sources in order to estimate these areas.</p> <p><strong>Compilation of the data source</strong></p> <p>We compiled the dataset through an iterative process of adding specific data sources (referred in the description of attributes below), followed by validation steps based on both the actual presence of groundwater dependent habitat types or regionally important biotopes (Natura 2000 habitat map) and in situ measurements of groundwater levels (<a href="http://data.inbo.be/watina/Pages/Common/Default.aspx">Watina+</a> database). The coverage of these validation data by <code>shallowgroundwater</code> was 96.9% and 98.6% respectively.</p> <p>Most steps to compile the data source were done manually using QGIS. Final steps were done in R; see R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/1b004e1/src/update_shallowgroundwater">'n2khab-preprocessing' at commit 1b004e1</a>.</p> <p><strong>Detailed properties</strong></p> <p>The data source is a GeoPackage with a single spatial multipolygon layer <code>shallowgroundwater</code> in the ‘Belge 1972 / Belgian Lambert 72’ coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>).</p> <p>All attributes are boolean (true/false), each indicating if a polygon was selected from the corresponding data source by applying a set of criteria. Multiple attributes can be true for a given polygon. In order to reduce file size, polygons were dissolved by each unique combination of the values of all attributes. Hence the dataset consists of multipolygons (also multipart polygons) rather than single (part) polygons. The different attributes of this dataset reveal for each polygon (1) the data source(s) we relied on and (2) the selection criteria we applied to judge if the mean lowest groundwater level is less than approximately 2 m below soil surface. As far as possible, we reference each used data source in the description of attributes below. If one is interested in the original polygons of each datasource, selections can be made by consulting the referenced data sources and applying the specified criteria.</p> <p>These are the attributes:</p> <ul> <li> <p><code>geomorph_wcoast</code>:</p> <ul> <li> <p>source: Cosyns et al. (2019)</p> </li> <li> <p>description: polygon belonging to geomorphological entities that are expected to harbour groundwater dependent types, and thus to exhibit shallow groundwater levels. It mainly concerns dune slacks, mud flats and salt marshes.</p> </li> <li> <p>selection: <code>"Code" IN ('ms', 'msl', 'sm', 'ss', 'ys', 'ysl', 'yfs') OR ("Code" = 't' and "Subtype" like '%vlakte%')</code> with ms = medium old dune slack, msl = leveled medium old dune slack, sm = mud flat, ss = salt marsh, ys = young dune slack, ysl = leveled young dune slack, yfs = young frontal dune (intruded), (<code>"Code" = 't' AND "Subtype" like '%vlakte%'</code>) = fossil beach</p> </li> </ul> </li> <li> <p><code>anthrop_gwdep:</code></p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: zones located within a 100 m buffer around (almost) everywhere groundwater dependent habitat types (or regionally important biotopes) ánd situated within zones classified as “anthropogenic” areas within the soil map. Within the zones of the soil map that are designated as “anthropogenic”, we lack information on soil characteristics. However, (almost) everywhere groundwater dependent types are present in these zones according to the Natura 2000 habitat map of Flanders, implying shallow groundwater levels. By including 100 m buffer zones around these types, restricted to the anthropogenic zones of the soil map, we consider the combined areas to have (potentially) shallow groundwater levels. So in practice, we first select the habitatmap polygons with (almost) everywhere groundwater dependent types that intersect the anthropogenic soil polygons, we buffer them, and then clip the result by the anthropogenic soil polygons.</p> </li> <li> <p>selection: see <a href="https://github.com/inbo/n2khab-preprocessing/pull/61">https://github.com/inbo/n2khab-preprocessing/pull/61</a> for the adopted workflow</p> <ul> <li> <p>from <code>soilmap_simple</code>: <code>bsm_mo_soilunitype starts with 'O'</code></p> </li> <li> <p>from <code>habitatmap_terr</code>: (almost) everywhere groundwater dependent types only; list of this category of types is available through <a href="https://inbo.github.io/n2khab">n2khab</a> R-package.</p> </li> </ul> </li> </ul> </li> <li> <p><code>narrowanthrop_gwdep</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: narrow zones classified as “anthropogenic” areas within the soil map that include (almost) everywhere groundwater dependent habitat types (or regionally important biotopes). Regarding the anthropogenic soil type polygons, it appears that the narrow ones containing (almost) everywhere groundwater dependent types are interesting to include as a whole as zones with supposed shallow groundwater levels. Hence we select them as a whole instead of selecting buffers around the polygons with (almost) everywhere groundwater dependent types (cfr. <code>anthrop_gwdep</code> and <code>dunes_gwdep</code>). An appropriate algorithm selects meaningful polygons, based on a “thinness” criterion and the fraction of (almost) everywhere groundwater dependent types that are present within the polygons.</p> </li> <li> <p>selection: see https://github.com/inbo/n2khab-preprocessing/pull/61</p> </li> </ul> </li> <li> <p><code>drainage</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: Drainage classification is based on a combination of groundwater depth, soil permeability, presence of impermeable layers, soil depth and topography (see Van Ranst & Sys, 2000). </p> </li> <li> <p>selection: <code>bsm_mo_drain in ('c-d', 'd', 'e', 'f', 'g', 'h', 'i', 'e-f', 'e-i', 'h-i')</code>; these are soils that are at least moderately gleyic or wet. </p> </li> </ul> </li> <li> <p><code>dunes_gwdep</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: zones located within a 100 m buffer around (almost) everywhere groundwater dependent habitat types (or regionally important biotopes) ánd situated within zones classified as “dunes” areas within the soil map. Within the zones of the Belgian soil map that are designated as “dunes”, we lack information on soil characteristics. However, (almost) everywhere groundwater dependent types are present in these zones according to the Natura 2000 habitat map of Flanders, implying shallow groundwater levels. By including 100 m buffer zones around these types, restricted to the “dunes” of the soil map, we consider the combined areas to have shallow groundwater levels. So in practice, we first select the habitatmap polygons with (almost) everywhere groundwater dependent types that intersect the “dunes” polygons, we buffer them, and then clip the result by the “dunes” polygons.</p> </li> <li> <p>selection: see https://github.com/inbo/n2khab-preprocessing/pull/61</p> <ul> <li> <p>from <code>soilmap_simple</code>: <code>bsm_mo_soilunitype = 'X'</code></p> </li> <li> <p>from <code>habitatmap_terr</code>: (almost) everywhere groundwater dependent types only; list of this category of types available through <a href="https://inbo.github.io/n2khab">n2khab</a> R-package.</p> </li> </ul> </li> </ul> </li> <li> <p><code>peat_profile</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: variant of the soil profile indicates a superficial peaty cover, mostly on gleyic or permanently water saturated soil with or without profile development (‘(v)’), eventually combined with strong anthropogenic influence (‘(o)’)</p> </li> <li> <p>selection: <code>bsm_mo_profvar in ('(o)(v)', '(v)')</code></p> </li> </ul> </li> <li> <p><code>peat_substr</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: soil substrate (layer underlying superficial layer, and lithologically diverging from it) consists of peat material starting at small (less than 75 cm; ‘v’) or moderate depths (75-125 cm; ‘(v)’), or a combination of the previous (‘v-’)</p> </li> <li> <p>selection: <code>bsm_mo_substr in ('(v)', 'v', 'v-')</code></p> </li> </ul> </li> <li> <p><code>peat_parentmat</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: parent material contains a mixture of at least 30% of peaty material</p> </li> <li> <p>selection: <code>bsm_mo_parentmat = 'v'</code></p> </li> </ul> </li> <li> <p><code>peat_texture</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: soil consists of plain peat material </p> </li> <li> <p>selection: <code>bsm_mo_tex in ('V-E', 'V')</code></p> </li> </ul> </li> <li> <p><code>phys_system</code>:</p> <ul> <li> <p>source: Fysische systeemkaart - Gegeneraliseerde bodemkaart voor Vlaanderen (<a href="https://www.geopunt.be/catalogus/datasetfolder/80a496cc-aa90-4ff9-8eb8-c89418f2407d">here</a> available at geopunt.be). Lhermitte & Honnay (1994).</p> </li> <li> <p>description: polygons designated as seepage areas where groundwater is supposed to gather after having infiltrated elsewhere (infiltration areas) and being transported through the landscape (passage areas). The classification of infiltration/passage/seepage areas is entirely based on the soil map polygons of Flanders (<a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>). Each polygon is attributed to one of these classes by considering soil, topographical and hydrological characteristics. In the seepage areas, we consider the groundwater to be shallow below topography.</p> </li> <li> <p>selection: <code>INKWEL = 3</code></p> </li> </ul> </li> <li> <p><code>zwin</code>:</p> <ul> <li> <p>source: Open Street Map, consulted 2021-10-06 by QGIS plugin QuickOSM</p> </li> <li> <p>description: the contour of the Zwin Nature Reserve in the most eastern part of the Flemish coastal area, as mapped in the Open Streep Map on 2021-10-06</p> </li> <li> <p>selection: <code>leisure = nature_reserve</code></p> </li> </ul> </li> <li> <p><code>habitat_1130</code>:</p> <ul> <li> <p>source: Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: polygons located within estuaries (habitat type <code>1130</code>); these areas are located outside dike infrastructure and harbour several types that rely on tidal dynamics or brackish water, and at the same time are considered (almost) everywhere groundwater dependent, a.o. brackish pioneer habitats, salt marshes and meadows (habitat types <code>1310</code>, <code>1320</code>, <code>1330</code> resp.), reed beds (<code>6430_mr</code>, <code>rbbmr</code>) and riverine Salix forests and shrubs (<code>91E0_sf</code>, <code>rbbsf</code>).</p> </li> <li> <p>selection: <code>Hab1 = '1130' or Hab2 = '1130' or Hab3 = '1130' or Hab4 = '1130' or Hab5 = '1130'</code></p> </li> </ul> </li> <li> <p><code>gwdepth_coast</code>:</p> <ul> <li> <p>source: interpolated groundwater level of point measurements along the Flemish coast, taken from the <a href="http://data.inbo.be/watina/Pages/Common/Default.aspx">WATINA database</a> of the Research Institute for Nature and Forest (<a href="https://www.vlaanderen.be/inbo/home/">INBO</a>)</p> </li> <li> <p>description: locations with estimated average lowest groundwater table less than 2.5 m below soil surface (shallow groundwater), based on interpolation of measured groundwater levels in areas along the Flemish coast with sufficient gauge densities; in these coarse sandy areas, groundwater levels are supposed to behave independently of topography, allowing simple interpolation among point measurements</p> </li> <li> <p>selection:</p> <ul> <li> <p>calculation of mean yearly spring groundwater level at each gauge (GVG; i.e. average of three groundwater head measurements in meter Ostend height, <a href="https://epsg.io/5710">EPSG:5710</a>, closest to 1st April, but with a minimum time lapse of 14 days, averaged over years)</p> </li> <li> <p>calculation of mean yearly amplitude of groundwater level at each gauge (i.e. difference between min and max water level per hydrological year, averaged over years)</p> </li> <li> <p>inverse distance weighted interpolation of both these datasets (IDW; factor 3, resolution 5 m) for areas with sufficient gauge densities</p> </li> <li> <p>calculation of relative GVG (depth below soil surface) by subtracting soil surface level (<a href="https://www.geopunt.be/catalogus/datasetfolder/f52b1a13-86bc-4b64-8256-88cc0d1a8735">Flemish DTM</a>) from GVG, both in meter Ostend height (EPSG:5710)</p> </li> <li> <p>subtract average amplitude from relative GVG as an estimate of lowest groundwater level (GLG)</p> </li> <li> <p>select locations with estimated <code>GLG >= -2.5</code> m located within the coastal dunes ecodistrict (see <a href="https://www.geopunt.be/catalogus/datasetfolder/58279e22-b9c0-4bcc-a774-95eb76208e7e">Flemish ecoregions and ecodistricts</a>)</p> </li> </ul> </li> </ul> </li> <li> <p><code>gwdepth_local</code>:</p> <ul> <li> <p>source: Batelaan et al. (2012)</p> </li> <li> <p>description: mean lowest groundwater level less than 2 m below soil surface (MLW) for the present day situation. The modelled area is a large military training site for which no information is available in the soil map of Flanders.</p> </li> <li> <p>selection: <code>GLG <= 2</code> m</p> </li> </ul> </li> <li> <p><code>seepage</code>:</p> <ul> <li> <p>source: Batelaan & De Smedt (1994)</p> </li> <li> <p>description: area with modelled seepage fluxes of at least 0.8 mm/day. In these areas we inherently consider the groundwater level to be shallow. The extent of the regional groundwater model encompasses the valleys of the rivers Demer, Dijle en Nete in the central part of eastern Flanders</p> </li> <li> <p>selection: <code>gridcode >= 3</code></p> </li> </ul> </li> <li> <p><code>peat_survey</code>:</p> <ul> <li> <p>source: peat pricks executed or compiled by the Research Institute for Nature and Forest (INBO)</p> </li> <li> <p>layer with interpreted presence of peaty zones evaluated by simple measurements of the depth of plain and superficial peaty soil layers at regular intervals (about 20 m), sampled by manually stabbing the peaty layers with a simple stick until the underlying mineral layer is reached (i.e. resistance is felt). These measurements originate from localised inventories and are thus sporadically available only.</p> </li> </ul> </li> <li> <p><code>duneslack</code>:</p> <ul> <li> <p>source: Provoost et al. (2020)</p> </li> <li> <p>description: polygons with dune slack vegetations along the Flemish coast that typically imply shallow groundwater levels. </p> </li> <li> <p>selection: see source</p> </li> </ul> </li> </ul> <p><br> A reading function to return <code>shallowgroundwater</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p>
Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021
<p><strong>Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>9 piezometers consisting of fully screened plastic tubes were installed at an averaged depth of 1.5 to 2m in the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). They cover four transects perpendicular to the stream from downstream (A) to upstream to (D).</p> <p>Water table elevation and temperature were recorded in each well at a 10 minute interval using SparkFun MS5803-14BA pressure sensors. Sensor resolution is 1 mm and 0.01 °C, sensor accuracy is ± 2 cm and ± 0.8°C. Sensor bias was verified and corrected by bi-monthly manual groundwater stage measurements. Water temperature was not manually corrected and may be subject to some unidentified bias.</p> <p>Piezometer location can be visualized in<strong><em> overview_piezo.jpg</em> </strong></p> <p>Piezometer coordinates are available in<strong> </strong>shapefile <strong><em>GPS_piezometers.zip</em></strong> (coordinate system: swiss LV95 (EPSG:2056))</p> <p>Piezometers name matches the labelling used in Müller et al. 2022 (A1,A2 to D1,D2). Two additionnal piezometers (BinjUp & BinjDown) used for a specific salt tracing analysis (see <em>ERT_timelapse_salt_tracer.gif </em>at <a href="https://zenodo.org/record/6342767#.YjBRmTXjJlh">https://zenodo.org/record/6342767#.YjBRmTXjJlh</a>) are also available. Finally an additional piezometer (B1-2) located between B1 and B2 is also available although it was not used in Müller et al. 2022.</p> <p><strong>Data Structure</strong></p> <p><strong>df_piezometers.csv</strong> is formated as a tidy dataframe with 10 minute interval and following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> w_elevation: Water table elevation [m. asl]<br> w_temperature : Groundwater temperature [°C]<br> - <em>name </em>: name of piezometer (see coordinates in GPS_piezometers.zip)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Data can be quickly vizualized in<strong> plot_piezo.png</strong> or interactively in a browser using <strong>plot_piezo_interactive.html</strong></p>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Indicative distribution map for Ecosystem Functional Group SF1.2 Groundwater ecosystems
<p>This archive contains indicative distribution maps and profiles for <strong>SF1.2 Groundwater ecosystems</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.