Other language confidence: 0.8707385379264615
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.
Real-time fluid monitoring began in late 2020 in the East Eifel and currently includes 12 sites, such as abandoned CO₂ wells, mofettes, CO₂-rich springs, CO₂-rich soil, and a cold-water geyser in the West Eifel. For the first time, fluid data are being recorded continuously with a high temporal resolution of up to 1 Hz. Depending on the local site conditions, the following parameters are being monitored: instrument temperature and battery voltage; barometric pressure and temperature; meteorological parameters; water level, wellhead pressure, water temperature; radon in free gas phase; CO2 concentration and CO2 flux in soil gas. Data are transmitted hourly via FTP to GFZ. While we generally observe small seasonal variations, short-term transients related to heavy rain or local and distant earthquakes are indicated. Over longer periods, we observe trend changes in helium isotope ratios, radon concentration, and water temperature. For example, two sites exhibited significant helium isotope changes from 2021 to 2025, which appear to correlate with earthquake swarms at depth. These examples demonstrate the necessity of jointly interpreting meteorological, hydrogeological, geophysical, and geodetic data.
This data set was taken within the Perturbations of Earth Surface Processes by Large Earthquakes PRESSurE Project (https://www.gfz-potsdam.de/en/section/geomorphology/projects/pressure/) of the GFZ Potsdam. This project aims to better understand the role of earthquakes on earth surface processes. Strong earthquakes cause transient perturbations of the near Earth’s surface system. These include the widespread landsliding and subsequent mass movement and the loading of rivers with sediments. In addition, rock mass is shattered during the event, forming cracks that affect rock strength and hydrological conductivity. Often overlooked in the immediate aftermath of an earthquake, these perturbations can represent a major part of the overall disaster with an impact that can last for years before restoring to background conditions. Thus, the relaxation phase is part of the seismically induced change by an earthquake and needs to be monitored in order to understand the full impact of earthquakes on the Earth system. Early June 2015, shortly after the April 2015 Mw7.9 Gorkha earthquake, 6 automatic compact weather station were installed in the upper Bhotekoshi catchment covering an area ~50km2. The weather station network is centered around the Kahule Khola catchment, a small headwater catchment and is part of a wider data acquisition strategy including hydrological monitoring, seismometers, geophones and high resolution optical (RapidEye) as well as radar imagery (TanDEM TerraSAR-X). https://www.gfz-potsdam.de/sektion/geomorphologie/projekte/pressure/
The Black Forest Observatory Data collection compiles digital data recorded at Black Forest Observatory (BFO) in Germany and provided through several international data centers. BFO aims to observe the entire geodynamic spectrum. It strives to ensure continuous, uninterrupted operation and is internationally recognized for high signal quality and sensitivity. Observed quantities cover three components of acceleration (including ground motion, gravity and tilt), strain, magnetic field, and others (see description of instruments below). The set of instruments and data recorders in operation provides a significant level of redundancy, which allows to distinguish natural phenomena from possible instrumental artefacts. The Black Forest Observatory (BFO) is a joint research facility of the Karlsruhe Institute of Technology (KIT) and the University of Stuttgart (Duffner et al., 2018; Gottschämmer et al. 2014). Since 1971 it is operated in cooperation of the geophysical and geodetic institutes of both universities (Zürn, 2014). BFO is staffed with two scientists and one technician. Main activities of the observatory fall into four categories, which are (1) observation and publication of a continuously recorded multi-parameter geodynamic data set, (2) research, (3) hosting of guest-experiments, and (4) teaching. The location of the observatory (48.3301 °N, 8.3296 °E) in the middle of the Black Forest was carefully selected at large distances to potential anthropogenic sources of noise. The instruments are deployed in a former silver mine in competent granite rock at a depth of up to 170 m below the surface and at up to 700 m distance from the entrance of the mine. This provides a thermally very stable environment. Two air-locks provide additional protection against air-pressure variations and ensure thermal stability. Because of these favorable conditions and the excellent high precision instruments operated at BFO the observatory is internationally well known as one of the most sensitive sites for long period observations, providing international standards for the scientific community, e.g. for recordings of Earth's free oscillations. The Black Forest Observatory operates broad-band seismometers (STS-1 and STS-2), gravimeters (superconducting gravimeter SG056, LaCoste Romberg earth-tide gravimeter ET-19), tiltmeters (Askania borehole tiltmeter, Horsfall fluid tiltmeter), an array of three invar-wire strainmeters, magnetometers (a scalar GSM-90 Overhauser magnetometer and a three component Rasmussen fluxgate magnetometers) and a permanent GPS-station. These are supplemented by regularly repeated magnetic base-line measurements and observations of absolute gravity as well as the recording of several environmental parameters (air-pressure, infrasound, humidity, wind speed, precipitation and temperature). Some of the latter are used to correct geodynamic recordings for remaining disturbances. The data are published in near-real-time through international data centers (IRIS DMC at Seattle, SZO at the BGR in Hannover, INTERMAGNET, GNSS Data Center at the BKG in Frankfurt, IGETS Database at GFZ Potsdam). Data are made available free of charge to scientific projects as well as to the general public with attribution as defined in the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). An extended review of research at BFO is given by Zürn (2014) and Duffner et al. (2018, in German). Both provide references to published BFO research.
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.
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