Other language confidence: 0.9888593509953059
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.
gravityInf is a small R-package which aims at supporting the anaylsis of a sprinkling (infiltration) experiment in combination with simultaneous and continious gravity measurements, presented in the above mentioned paper. With this package you can easily walk through the necessary steps in order to set up an infiltration scenario, maybe based on your own sprinkling / irrigation experiment and carry out simple hydrological modelling of water distribution in 3D in the subsurface. An observed gravity time series is needed for the model in order to fit and thus identify the dominant infiltration process for your research area. A model functionality and limitations can be found in Reich et al. (2021), the associtated data was published by Reich et al. (2021, https://doi.org/10.5880/GFZ.4.4.2021.001).
A sprinkling experiment was conducted at the geodetic observatory Wettzell (Bavaria, Germany) with the intention to combine classical hydrological field observations of soil moisture with gravity data and electrical resistivity tomography (ERT). The setup consisted of 8 sprinkling units installed around a gravimeter in field enclosure. Artificial rainfall was applied for 6 hours. The sprinkling area of 15 x 15 m was equipped with 3 vertical soil moisture sensor profiles, 1 horizontal soil moisture transect, near-surface soil moisture sensors and 3 ERT profiles. The non-invasive gravity data and the ancillary monitoring data were used to infer water transport processes in the subsurface during the sprinkling experiment. To this end, the gravity data were used to identify the structure and the parameters of a subsurface flow model in an inverse modelling approach by optimizing the simulated gravity response with respect to the observations. The ancillary soil moisture and ERT data were used to evaluate the model outputs in terms of adequacy and dominant subsurface flow processes. Model data cover the following subtopics: • virtual experiments to show the theoretical relationships between subsurface water re-distribution processes and their corresponding gravity responses • an uncertainty analysis of the sprinkling experiment, e.g., with respect to water volumes and their spatial distribution, and the impact on the expected gravity response • inverse modelling to identify dominant subsurface water re-distribution processes • a synthetical model setup based on the ancillary datasets of soil moisture and ERT Monitoring and model output data used for this investigation is provided within this data repository. A detailed description and discussion can be found in Reich et al. (2021). The inverse modelling was carried out using the R-package gravityInf (Reich, 2021).
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 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.
The described dataset was the result of a field effort consisting of several campaigns to assess the influence of carbon increase as a result of agroforestry treatments on soil hydrological characteristics and water fluxes at two sites in Malawi. At the sites, two experimental trials have been established which differ in age and soil characteristics, while climatic conditions are roughly comparable. At both sites we focused on control plots of maize and agroforestry treatments including Gliricidia sepium (Jacq.) Walp. as the tree component. The dataset contains soil characteristics such as texture, porosity, carbon and nitrogen concentrations, carbon density fractions, dispersible clay proportions, soil hydraulic conductivity and water retention curves. To assess the differences in water fluxes between treatments and sites, we installed soil moisture and matric potential sensors and a small weather station at the sites and monitored the fluxes over the course of about three months. The resulting time series are also part of the dataset, as well as some measurements of maize heights. The file structure of the dataset as well as details on the sites, sampling procedures, measurements and methodology are included in the data description.
The dataset comprises a range of variables describing characteristics of flood events and river catchments for 480 gauging stations in Germany and Austria. The event characteristics are asscoiated with annual maximum flood events in the period from 1951 to 2010. They include variables on event precipitation, antecedent catchment state, event catchment response, event timing, and event types. The catchment characteristics include variables on catchment area, catchment wetness, tail heaviness of rainfall, nonlinearity of catchment response, and synchronicity of precipitation and catchment state. The variables were compiled as potential predictors of heavy tail behaviour of flood peak distributions. They are based on gauge observations of discharge, E-OBS meteorological data (Haylock et al. 2008), mHM hydrological model simulations (Samaniego et al., 2010), 4DAS climate reanalysis data (Primo et al., 2019), and the 25x25 m resolution EU-DEM v1.1. A short description of the data processing is included in the file inventory and more details can be found in Macdonald et al. (2022).
This dataset contains catchment average time series of five meteorological or hydrological parameters for 3872 hydrometric stations across Europe from 1960-2010. The parameters are: rainfall, soil moisture saturation, snowmelt, snow cover and convective conditions. All parameters have a daily resolution and were derived from a 0.11x0.11° reanalysis dataset. Daily averages were calculated from the pixels within each catchment, weighted by the fraction of pixel area that lies within the respective catchment. This dataset was originally created for the classification of floods by their generating process, but is also suitable for different hydrological studies.The dataset consists of two types of files:(1) The station metadata, which contains latitude, longitude, catchment area and an ID for each hydrometric station.(2) The five time series datasets, which contain one value for each station ID and each day from 1960-01-01 to 2010-12-31.
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.
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