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.
This dataset contains large-scale groundwater storage anomalies for the Sesan and Srepok catchments in the Central Highlands of Vietnam. The anomalies were derived from in-situ groundwater well water level time series and hydrogeological information. A detailed description of the datasets and methods can be found in Sayyadi et al 2025.
The dataset is comprised of three files: insitu_groundater_storage_anomalies.csv, GW_wells.csv and a shape file with the Thiessen polygones indicating the extent of the area for which the storage calculations were performed.
insitu_groundater_storage_anomalies.csv contains the groundwater storage variations, with the following columns:
1. gws_mm (Groundwater Storage in millimeters): The gws_mm data represent groundwater storage anomalies derived from in-situ well measurements. Groundwater levels were recorded monthly from a network of observation wells across the study area. Specific yield values were used to convert the groundwater level variations into storage anomalies, measured in millimeters of water equivalent.
2. seasonal_adjusted: The seasonal_adjusted data were obtained by removing the seasonal component from the raw groundwater storage time series (gws_mm). This was done by calculating the mean monthly anomalies and subtracting them from the original data to isolate non-seasonal variations.
3. trend: The trend data represent the linear trend component of the groundwater storage anomalies. The trend was calculated using a linear regression model applied to the seasonal-adjusted data, highlighting long-term groundwater storage changes over the study period.
4. detrended: The detrended data were created by removing both the seasonal and long-term trend components from the gws_mm data. This dataset captures short-term fluctuations and anomalies, free from the regular seasonal and trend influences.
GW_wells.csv contains a list of the griundwater wells used in the study, with information about their location and lithology, as well as the range of associated specific yields (sy).
Thiessen_polygones_GW_wells_2S.shp is a georeferenced shape sile containing the Thiessen polygones for the wells in GW_wells.csv.
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.