Other language confidence: 0.9728649554188545
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.
The data set contains hydrological, meteorological and gravity time series collected at Argentine-German Geodetic Observatory (AGGO) in La Plata, Argentina. The hydrological series include soil moisture, temperature, electric conductivity, soil parameters, and groundwater variation. The meteorological time series comprise air temperature, humidity, pressure, wind speed, solar short- and long-waver radiation, and precipitation. The observed hydrometeorological parameters are extended by modelled value of evapotranspiration and water content variation in the zone between deepest soil moisture sensor and the groundwater level. Gravity products include large-scale hydrological, oceanic as well as atmospheric effects. These gravity effects are furthermore extended by local hydrological effects and gravity residuals suitable for comparison and evaluation of the model performance. Provided are directly observed values denoted as Level 1 product along with pre-processed series corrected for known issues (Level 2). Level 3 products are model outputs acquired using Level 2 data. The maximal temporal coverage of the data set ranges from May 2016 up to November 2018 with some exceptions for sensors and models set up in May 2017. The data set is organized in a database structure suitable for implementation in a relational database management system. All definitions and data tables are provided in separate text files allowing for traditional use without database installation.Software related to the data acquisition, processing, and modelling can be found in a separate publication describing scripts applied to the data set presented here. The software publication is available at https://doi.org/10.5880/GFZ.5.4.2018.002 (Mikolaj, 2018)
This software publication describes the data acquisition, processing and modelling of hydrological, meteorological and gravity time series prepared for the Argentine-German Geodetic Observatory (AGGO) in La Plata, Argentina. The corresponding output data set is available at http://doi.org/10.5880/GFZ.5.4.2018.001 (Mikolaj et al., 2018).Processed hydrological series include soil moisture, temperature, electric conductivity, and groundwater variation. The processed meteorological time series comprise air temperature, humidity, pressure, wind speed, solar short- and long-waver radiation, and precipitation. Modelling scripts include evapotranspiration, combined precipitation, and water content variation in the zone between deepest soil moisture sensor and groundwater. In addition, large-scale hydrological, oceanic as well as atmospheric effect are modelled along with the local hydrological effects. To allow for a comparison of the model outputs to observations, processing script of gravity residuals is provided as well.
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes. ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017). The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction). This entry refers to the ISIMIP2b simulation data from three agricultural models: GEPIC, LPJmL and PEPIC. ---------------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications). ----------------------------------------------------------------------------
This data publication is supplementary to a study on the climatic controls on leaf wax hydrogen isotopes, by Gaviria-Lugo et al. (2023). The dataset contains hydrogen isotope ratios from leaf wax n-alkanes (δ2Hwax) taken from soils, river sediments and marine surface sediments along a climatic gradient from hyperarid to humid in Chile. In addition, for each sampling site the hydrogen isotope ratios from precipitation (δ2Hpre) from the grids produced by the Online Isotopes in Precipitation Calculator (OIPC) (Bowen and Revenaugh, 2003). Furthermore, for each sampling site we report mean annual data of precipitation, actual evapotranspiration, relative humidity, and soil moisture, all derived from TerraClimate (Abatzoglou et al., 2018). Also provide data of mean annual temperature and the annual average of maximum daily temperature derived from WorldClim (Fick and Hijmans, 2017). As a final climatic parameter, we also derived data of aridity index from the Consultative Group of the International Agricultural Research Consortium for Spatial Information (CGIARCSI) (Trabucco and Zomer, 2022). In addition to climatic variables, for each site we include land cover fractions of trees, shrubs, grasses, crops, and barren land. These land cover fractions were obtained from Collection 2 of the Copernicus Global Land Cover layers (Buchhorn et al., 2020) via Google Earth Engine. For further comparison here we provide δ2Hwax compiled from 26 publications (see references) that reported both the n-C29 and n-C31 n-alkanes homologues from soils and lake sediments. For each sampling site of the global compilation, we provide δ2Hpre and the same climatic and land cover parameters as for the Chilean data (i.e., precipitation, actual evapotranspiration, relative humidity, soil moisture, aridity index, temperature, fraction of trees, fraction of grasses, etc.), using the same sources. The data is provided here as one single .xlsx file containing 9 data sheets, but also as 9 individual .csv files, to be accessed using the file format of preference. Additionally, 5 supplementary figures that accompany the publication Gaviria-Lugo et al. (2023) are provided in one single .pdf file. The samples taken for this study were assigned International Geo Sample Numbers (IGSNs), which are included in the provided tables S4, S5 and S6.
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
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
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