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Soil moisture response, stable water isotope profiles, and trinary images of vertical subsurface flow paths across the evolution of siliceous and calcareous soil in proglacial areas

The data set was collected to identify hydrological processes and their evolution over it time. It consists of several individual files in tabstop delimeted text format. The data set contains the data obtained from deuterium and brilliant blue tracer experiments at two chronosequence studies in the glacier forefield of the Stone Glacier and the Griessfirn in the central Alps, Switzerland. Each chronosequence consisted of four moraines of different ages (from 30 to 13500 years). At each forefield sprinkling experiments with deuterium and dye tracer experiments with blue dye (Brilliant Blue) were conducted on three plots per moraine. The moraines at the forefield of the Stone Glacier developed from siliceous parent material and at the forefield of the Griessfirn from calcareous parent material. Data from the siliceous forefield are marked with (S) and data from the calcareous forefield are marked with (C). The data set consist of soil moisture time series and soil water isotope profiles of the sprinkling experiments with deuterium, as well as trinary images of stained vertical subsurface flow paths from the dye tracer experiment. The individual plots per moraine are distinguished via their position relative to one another on the moraine (left, middle, and right, looking upslope). The plots used for the sprinkling experiments were located in close vicinity to the plots used for the dye tracer experiments. For the sprinkling experiments with deuterium each plot (4m x 6m) per age class was equipped with 6 soil moisture sensors. Three of these sensors were installed as a sensor profile at one side of the plot about one meter downslope from the upper plot boundary. The sensors were installed at 10, 30, and 50 cm soil depth. On the other side of the plot, two sensors were placed in 10 cm depth, one opposite to the sensor profile and the second sensor one meter upslope from the lower plot boundary. The sixth sensor was placed at 10 cm depth in the center of the plot. The plots were irrigated on three consecutive days with three different irrigation intensities and deuterium concentrations. Per forefield, the soil moisture data are listed in one file per age class. The file contains for each plot, the time stamp and the soil moisture values of the 6 sensors.

Spatiotemporal variability in infiltration through biopores: earthworms, macropores and infiltration patterns

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.

Soil chemical, physical and hydrological characteristics in two agroforestry systems in Malawi

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.

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