Depth profiles of stable water isotopes in the soil provide important information on flow and transport processes in the subsurface. We sampled depth profiles of stable water isotopes (2H and 18O) in the pore waters on two occasions at 46 sites in the Attert catchment, Luxembourg and are partly located in mixed deciduous forest and partly on grassland. These sites correspond to the sensor cluster sites of the DFG research unit CAOS. Sampling took place once between February 2012 and October 2013 and once in June 2014. Sampling procedure: We took 1-3 soil cores of 8 cm diameter in close proximity with a percussion drill (Atlas Copco Cobra, Stockholm, Sweden) at each study site within a radius of 5 m from the soil moisture sensor profiles. We drilled as deep as possible and divided the extracted soil cores into subsamples of 5 to 10 cm length and sealed the material in air tight bags (Weber Packaging, Güglingen, Germany). The soil sample depths were corrected for compaction during the drilling pro-cess and are provided as the mean depth of 5 or 10 cm soil core subsamples. For isotope analyses of the pore water, we used the direct equilibration method (Wassenaar et al., 2008). Analyses were carried out at the Chair of Hydrology, University of Freiburg. We provide detailed information about the laboratory analyses in Sprenger et al. (2015) and Sprenger et al. (2016) and the data description associated with the data.
This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, including pedo-hydrological, geophysical, and remote sensing data. The objective of this experiment is to monitor soil moisture dynamics during a well-defined infiltration process with a combination of direct and non-invasive techniques.Overall, the quantification of soil water dynamics and, in particular, its spatial distributions is essential for the understanding of land-atmosphere interactions. However, the precise measurement of soil water dynamics and its spatial distribution in a continuous manner is a challenging task. Pedo-hydrological monitoring techniques rely on direct, point-based measurement with buried probes for soil water content and matric potential. Non-invasive remote sensing (RS) and geophysical measurement techniques allow for spatially continuous measurements on different spatial scales and extents. This experiment provides a basis for the analyses of signal coherence between the measurement techniques and disciplines. It contributes to forthcoming developments of monitoring setups and modeling approaches to landscape-water dynamics.For direct monitoring, an array of time-domain reflectometry (TDR) probes and tensiometers was used. As non-invasive techniques, we applied a ground-penetrating radar (GPR), a hyperspectral snapshot sensor, a long-wave infrared (LWIR) sensor, and a hyperspectral field spectroradiometer. We provide the data in nearly raw format, including information about the site properties and calibration references. The data are organized along with the different sensors and disciplines. Thus, the distinct sensor data can also be used independently of each other. In addition, exemplary scripts for reading and processing the data are included.
The dataset consists of measured soil moisture, sap velocity, precipitation and solar radiation time series, additionally calculated time series of root water uptake (RWU) and sap flow, including a python script for calculating RWU. The measurements were collected at two sites in mixed beech stands (Fagus sylvatica L.) in contrasting geological settings (sandstone and slate). Both sites are situated within the Attert catchment in western Luxembourg and were part of the monitoring network of the CAOS project (DFG research unit “From Catchments as Organised Systems to Models Based on Functional Units”; Zehe et al. 2014). In the vegetation period of 2017, we measured soil moisture across two profiles in the trees’ rhizosphere. These time series are compared to sap flow measurements in nearby trees. Moreover, we include precipitation and solar radiation data for the study period. For conversion of soil moisture to soil matric potential, we provide van Genuchten parameters (van Genuchten, 1980) for soil water retention at both sites, based on a previous study (Jackisch, 2015).1. Soil moistureSoil moisture was monitored using TDR tube probes (Pico Profile T3PN, Imko GmbH), which allow for installation with minimal disturbance using an acrylic glass access liner (diameter 48 mm). The liner tube was installed in the rhizosphere of the trees without any excavation using a percussion drill. For optimal contact of the liner with the surrounding soil, the drill diameter was 40 mm and the tube was installed more than one year prior to the recorded data set. Each TDR probe segment integrates the soil moisture measurement over its length of 0.2 m. The signal penetrates the soil about 0.05 m which results in an integral volume of approx. 0,001 m-3. The probes can be stacked directly on top of each other, permitting spatially continuous monitoring over the soil moisture profile. At the sandstone site, we were able to install a sequence of 12 probes reaching a depth of 2.4 m. At the slate site, percussion drilling was inhibited by the weathered bedrock. There, we installed a sequence of 9 probes reaching a depth of 1.8 m. Soil moisture is recorded in 15 min intervals and aggregated to 30 min means.2. Sap flowSap velocities were monitored in four beech trees in the direct vicinity of the soil moisture profile (as part of the CAOS research unit). At the sandy site, the reference sap velocity time series could be obtained from the exact tree where the TDR sensors were installed. It had a diameter at breast height (DBH) of 64 cm. At the slate site, the sap velocity sensor of the intended tree failed 3 weeks after leaf out. There, we refer to a neighbouring beech tree with a DBH of 48 cm about 9 m from the TDR measurements The sap flow sensors (East30 Sensors) are based on the heat ratio method and measure simultaneously at 5, 18 and 30 mm depth within the sapwood. Installation and calculation of sap velocities followed the description in Hassler et al. (2018). The sensors were installed before leaf out of the vegetation period in 2017. The data is recorded in 30 min intervals. We provide both the measured sap velocities and the upscaled sap flows. We assume the two outer measurement points in the sapwood to be representative for the radial area between 0–11 mm and 11–24 mm. Both are the mid points between the sensor positions. The inner sensor is representing a flow field, which has been shown to follow a Weibull distribution (Gebauer et al., 2008) in the active sapwood. To estimate the sap velocity distribution at each time step, we fit the Weibull function with the beech-parameters of (Gebauer et al., 2008) to the observed measurements at the mid and inner point via a scaling factor. For a correct position reference, the bark thickness is removed after Rössler (2008). As an inner limit, the 95% percentile is used to mark the transition to the inactive sapwood (Gebauer et al., 2008) (“zero” sap velocity limit). The resulting time series is now reporting sap flow in L h-1 and is aggregated to daily values.3. Meteorological dataAs further reference for the drivers of temporal dynamics in soil moisture and sap velocity we use 10 min solar radiation records (Apogee Pyranometer SP110) subsampled to the time stamps of the precipitation data. Corrected hourly radar stand precipitation at canopy level is obtained from combined data from DWD (Deutscher Wetterdienst, Germany), ASTA (Administration des Services techniques de l'agriculture, Luxembourg) and KNMI (Koninklijk Nederlands Meteorologisch Instituut, Netherlands) after Neuper and Ehret (2019).4. Soil water retention propertiesSoil water retention properties of the sites are given for two layers. The data was assessed in a previous study using the free evaporation method of the HYPROP apparatus and the chilled mirror method in the WP4C (both Meter AG) with 250 mL undisturbed soil samples from the sites (Jackisch, 2015). Following this method, the matric potential is divided into bins (0.05 pF). All retention data of the reference soil samples is bin-wise averaged to form the basis for the fitting of a van Genuchten retention curve. We have aggregated the results of 44 and 41 soil samples in the subbasins of the sand and slate site.
Version history17. July 2019: release of Version 2.0. This version includes additionally the catchment boundaries provided as subfolder of geodata.zip. The version 1.0 is available in the "previous-versions" subfolder via the Data Download link. The time series did not change and are not included in the V1.0 zip folder. Data descriptionWe used different sensing techniques including time-lapse imagery, electric conductivity and stage measurements to generate a combined dataset of presence and absence of streamflow within a large number of nested sub-catchments in the Attert Catchment, Luxembourg. The first sites of observation were established in 2013 and successively extended to a total number of 182 in 2016 as part of the project “Catchments As Organized Systems” (CAOS, Zehe et al., 2014). Setup for time-lapse imagery measurements was inspired by Gilmore et al. (2013) while the setup for EC-sensor was proposed by Chapin et al. (2014). Temporal resolution ranged from 5 to 15 minutes intervals. Each single dataset was carefully processed and quality controlled before the time interval was homogenized to 30 minutes. The dataset provides valuable information of the dynamics of a meso-scale stream network in space and time.The Attert basin is located in the border region of Luxembourg and Belgium and covers an area of 247 km². The elevation of the catchment ranges from 245 m a.s.l. in Useldange to 549 m a.s.l. in the Ar-dennes. Climate conditions across the catchment are rather similar in terms of temperature and pre-cipitation. Hydrological regimes are mainly driven by seasonal fluctuations in evapotranspiration caus-ing flow to cease in intermittent reaches during dry periods. The catchment covers three predominant geologies: Slate, Marls and Sandstone. The dataset features data from catchments covering all geologi-cal characteristics from single geology to mixed geology. It can be used to test and evaluate hydrologic models, but also for the assessment of the intermittent stream ecosystem in the Attert basin.
This dataset consists of spatially and temporally resolved data of dye-infiltration patterns, earthworms and macropores as well as supporting data, such as land use, soil moisture content, soil temperature, bulk density, and soil texture, in the Wollefsbach area of the Attert Catchment in Luxembourg (Pfister et al., 2005).The data was gathered in six measurement campaigns in the period from May 2015 to March 2016. During each measurement campaign we measured at five random sites on each of six chosen fields: three grasslands and three agricultural fields. At each measurement site a combination of measurements was performed: infiltration patterns of blue stained water, earthworm abundance (species level), macropore counts on horizontal soil profiles (in three depths, discriminating three size classes and stained or non-stained), soil temperature and moisture contents in three depths. Finally, undisturbed soil core samples were taken during one campaign for the determination of the texture and bulk density at different sampling sites. In the data table we also include GIS derived values of elevation, slope, aspect, heat load index, and topographical wetness index. Details on all the measurement methods, GIS-analysis methods and units of the data are given below.This data was gathered as part of the Joint Research Project “Catchments as Organised Systems” (CAOS, Zehe et al., 2014) funded by the German Research Foundation.---------------------------------------------------Version history:10 February 2020, release of Version 1.1.:The authors discovered that some rows in the data table “Earthworms_Macropores_Data.csv” for September Field 3 and Field 4 were accidentally exchanged. Compared to version 1.0, the data in rows 71 to 75 (Sept_3_1 to Sept_3_5) were exchanged with the data in rows 76 to 80 (Sept_4_1 to Sept_4_5). The authors apologise for this and ask everyone who downloaded the data of version 1.0 are advised to only use version 1.1, because there was an error which could lead to wrong results. Nevertheless, version 1.0 of the data table is available in the "previous-versions" subfolder via the Data Download link. The infiltration data included in “2019-022_vanSchaik-et-al_Infiltration_patterns.zip” remain unchanged.
This dataset provides half-hourly surface energy balance measurements for a temperate grassland site in Luxembourg. The data were obtained during a field campaign in June and July 2015. The observations comprise multiple variables measurements by an Eddy-Covariance station, a net radiometer, soil moisture, temperature and soil heat flux probes and meteorological standard measurements. For details please see the reference article Renner et al. (2019, HESS) with the general setup described in Wizemann et al., 2015. The data are complemented by half-hourly model output of sensible and latent heat fluxes that are published as individual data publication (Renner et al., 2018).The data is provided as comma-separated-values (csv) format in a long table format. Columns represent Date, Time, variable, value, source. The column “variable” sets the name of the variable (following CEOP standards) with an information of the measurement depth for soil measurements. Column “source” describes the data source with an acronym(Observations “ObsEC”).The data contributes to the Joint Research Group "Catchments As Organized Systems" (CAOS) funded by the German Research Foundation.Methods: Eddy Covariance, Surface energy balance observations
This dataset provides half-hourly model output of sensible and latent heat fluxes simulated by three structurally different evapotranspiration schemes for a temperate grassland site in Luxembourg. All models use surface energy and meteorological observations as input. The observational data were collected during a field campaign in June and July 2015 and are distributed as complementary dataset by Wizemann et al., 2018. Two models are based on a parameterization of the sensible heat flux (OSEB, TSEB; see Brenner et al., 2017) and one model (STIC 1.2, Mallick et al., 2016) is a modification of the Penman-Monteith formulation using skin temperature as additional input variable. For details please see the reference article Renner et al., 2019, HESS. The data is provided as comma-separated-values (csv) format in a long table format. Columns represent Date, Time, variable, value, source. The column “variable” sets the name of the variable (following CEOP standards, https://www.eol.ucar.edu/field_projects/ceop). Column “source” describes the data source with an acronym representing the models (OSEB, TSEB, STIC).The data contributes to the Joint Research Group "Catchments As Organized Systems" (CAOS) funded by the German Research Foundation.Methods: land-surface modelling, evapotranspiration schemes
The dataset consist of time series of hourly rain rates and mean radar reflectivity factor (herein after referred to as reflectivity) near the ground, 100 meter and 1500 meter above the ground at six locations in the Attert catchment in Luxembourg. The time series cover a time span of 4 years (from the 1st of October 2012 tor the 30th of September 2016). The dataset was derived from drop size measurements we conducted at six stations with six laser optical disdrometers and two micro rain radars (MRR) within the CAOS Project (DFG Research Group: From Catchments as Organized Systems to Models based on Functional Units (FOR 1598). The time series of rain rates and radar reflectivity factors (reflectivities) were calculated (derived) via the 3.5th and 6th statistical moments of the drop size distributions using the particular raw data of drop sizes and fall velocities. The primary reason for the measurements was to improve radar based quantitative precipitation estimation in general and the conversion of the reflectivity Z (measured by operational weather radar) to a rain rate R at the ground via the so-called Z-R relation within a mesoscale catchment.GENERAL CONVENTIONS:• Time extent: 1.10.2012 00:00 – 30.09.2016 23:00 (35064 values)• Time reference: UTC • Time stamp: end• Time resolution: 1h• Time series are equidistant and gapless• Missing values: NaN• Delimiter: ; (semicolon)• decimal separator: . (point)STATION LOCATIONS:Name; Abbreviation; Latitude (WGS-84); Longitude(WGS-84); height a.s.l; InstrumentationOberpallen;OPA; 49.73201°; 5.84712°;287 m; disdrometer Useldange;USL; 49.76738°; 5.96756°; 280 m; disdrometer and MRR Ell;ELL; 49.76558°; 5.84401°; 290 m; disdrometer Post;POS; 49.75394°; 5.75481°; 345 m; disdrometer Petit-Nobressart;PIN; 49.77938°; 5.80526°; 374 m; disdrometer and MRR Hostert-Folschette;HOF; 49.81267°; 5.87008°; 435 m; disdrometerHEADER – VARIABLES DESCRIPTION:Name - description:Date-UTC – Date as yyyy-mm-dd HH:MM (4 digit year-2 digit month – 2 digit day 2 digit hour: 2 digit minute)Time Zone: UTC. Decade – tenner day of the year (that is 1st to 10th of January = 1 ; 11th to 20th of January = 2 ; 21th to 30th of January = 3 ; … 21st to 31st of December = 36.Month – Month of the year (1: January, 2: February, 3:March,…, 12: December).dBZ0_DIS_ELL – reflectivity at ground level (in dBZ) at the station Ell derived from disdrometer measurements.dBZ0_DIS_HOF – reflectivity at ground level (in dBZ) at the station Hostert-Folschette derived from disdrometer measurements.dBZ0_DIS_OPA – reflectivity at ground level (in dBZ) at the station Oberpallen derived from disdrometer measurements.dBZ0_DIS_PIN – reflectivity at ground level (in dBZ) at the station Petit-Nobressart derived from disdrometer measurements.dBZ0_DIS_POS – reflectivity at ground level (in dBZ) at the station Post derived from disdrometer measurements.dBZ0_DIS_USL – reflectivity at ground level (in dBZ) at the station Useldange derived from disdrometer measurements.dBZ100_MRR_PIN – reflectivity 100 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ100_MRR_USL – reflectivity 100 m above ground (in dBZ) at the station Useldange derived from MRR measurements.dBZ1500_MRR_PIN – reflectivity 1500 m above ground (in dBZ) at the station Petit-Nobressart derived from MRR measurements.dBZ1500_MRR_USL – reflectivity 1500 m above ground (in dBZ) at the station Useldange derived from MRR measurements.RR0_DIS_ELL – rain rate at ground level (in mm/h) at the station Ell derived from disdrometer measurements.RR0_DIS_HOF – rain rate at ground level (in mm/h) at the station Hostert-Folschette derived from disdrometer measurements.RR0_DIS_OPA – rain rate at ground level (in mm/h) at the station Oberpallen derived from disdrometer measurements.RR0_DIS_PIN– rain rate at ground level (in mm/h) at the station Petit-Nobressart derived from disdrometer measurements.RR0_DIS_POS – rain rate at ground level (in mm/h) at the station Post derived from disdrometer measurements.RR0_DIS_USL – rain rate at ground level (in mm/h) at the station Useldange derived from disdrometer measurements.RR100_MRR_PIN – rain rate 100 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR100_MRR_USL – rain rate 100 m above ground (in mm/h) at the station Useldange derived from MRR measurements.RR1500_MRR_PIN – rain rate 1500 m above ground (in mm/h) at the station Petit-Nobressart derived from MRR measurements.RR1500_MRR_USL – rain rate 1500 m above ground (in mm/h) at the station Useldange derived from MRR measurements.The instruments were maintained and cleaned monthly. The data was quality checked. Cases with solid precipitation were excluded using the output form the Pasivel² present weather sensor software, which especially was needed since disdrometer data was contaminated by cobwebs. But since the present weather analyzer classified these (due to their slow movement within the wind) as snow, these then could easily be eliminated.
Cuttings were crushed in a tungsten carbide ball mill for 25 min; while core samples were crushed in a tungsten carbide jaw breaker and then processed in the same way as the chip material. The resulting powder samples (max 0.06 mm size) were dried at 105°C, 3 gr selected and mixed with 2.5% Moviol solution and finally pressed under 40 kN into alumina rings. These standardized pellets were used for both, XRD and XRF measurements. For the determination of major and trace elements a fully automated wavelenght-dispersive XRF device (SIEMENS SRS 303 AS) was used in the field laboratory. The standard measuring operation comprised 11 major elements (SiO2, TiO2, Al2O3, Fe2O3 total, MnO, MgO, CaO, Na2O, K2O, P2O5, S) and 12 traces (Rb, Sr, Y, Zr, Nb, Cr, Ni, Zn, V, Cu, Th, U). Element concentrations were calculated by setting up calibration curves computed with more than 40 international natural rock standards.
Cuttings were crushed in a tungsten carbide ball mill for 25 min; while core samples were crushed in a tungsten carbide jaw breaker and then processed in the same way as the chip material. The resulting powder samples (max 0.06 mm size) were dried at 105°C, 3 gr selected and mixed with 2.5% Moviol solution and finally pressed under 40 kN into alumina rings. These standardized pellets were used for both, XRD and XRF measurements. For the determination of major and trace elements a fully automated wavelenght-dispersive XRF device (SIEMENS SRS 303 AS) was used in the field laboratory. The standard measuring operation comprised 11 major elements (SiO2, TiO2, Al2O3, Fe2O3 total, MnO, MgO, CaO, Na2O, K2O, P2O5, S) and 12 traces (Rb, Sr, Y, Zr, Nb, Cr, Ni, Zn, V, Cu, Th, U). Element concentrations were calculated by setting up calibration curves computed with more than 40 international natural rock standards.
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