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This dataset comprises synthetic weather data generated for historical (“control” present, 1985-2014) and two future periods (near future: 2031-2060 (period1) and far future: 2071-2100 (period2)) across a domain encompassing Germany and its neighboring riparian countries. The dataset was produced through the following key steps: (1) Classifying Weather Circulation Patterns for the Observed/Present Period: Weather circulation patterns (CPs) were classified for a European domain (35°N – 70°N, 15°W – 30°E), and regional average temperatures at 2 m height (t2m) were calculated for the German domain (45.125°N – 55.125°N, 5.125°E – 19.125°E). This classification used mean sea level pressure (psl) and mean temperature (tas) data from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al., 2020). (2) Training Non-Stationary Climate-Informed Weather Generator (nsRWG): The nsRWG (Nguyen et al., 2024), conditioned on the classified CPs and using tas as a covariate, was set up and trained for the German domain using the E-OBS dataset, version 25.0e (Cornes et al., 2018). This training dataset includes 540 grid cells of mean daily temperature and precipitation totals for the period 1950–2021, with a spatial resolution of 0.5° x 0.5°. (3) Generating Data for the Present Period: Long-term synthetic data for the present period is generated using the trained nsRWG. (4) Assigning Circulation Patterns for Future Periods: The classified CPs from the present period were assumed to remain stable in the future. These CPs were assigned to future periods based on mean sea level pressure data from nine selected general circulation models (GCMs) from CMIP6 (Eyring et al., 2020) for the two future periods and two shared socio-economic pathways: SSP245 and SSP585 (IPCC, 2023). In total, CPs were derived for 36 scenarios, and regional average temperatures were also computed. (5) Downscaling Data for Future Scenarios: The nsRWG was used to statistically downscale long-term synthetic weather data for all 36 future scenarios. (6) Final dataset: The dataset includes synthetic weather data generated for the present period (Step 3) and future scenarios (Step 5). This dataset is expected to offer a key benefit for hydrological impact studies by providing long-term (thousands of years) consistent synthetic weather data, which is indispensable for the robust estimation of probability changes of hydrologic extremes such as floods.
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 presented dataset forms the basis for investigating present and future coupled effects of rising surface temperatures and temporal trends in groundwater recharge on subsurface pressure and temperature (PT) conditions in the North German Basin beneath the Federal States of Brandenburg and Berlin (NE Germany), for the period 1955-2100. The study relies on a stochastic weather generator, a distributed hydrologic model, and a 3D thermo-hydraulic groundwater model to evaluate spatio-temporal subsurface feedback to two shared socioeconomic pathways (SSP) for seven general circulation models (GCM). The results demonstrate a regional variability in both the intensity and maximum depths of projected groundwater warming, driven by hydraulic gradients and the underlying geological structure. The magnitude of groundwater warming primarily depends on the surface temperature scenario. Projected changes in recharge are not sufficient to reverse this trend, although recharge is still a key factor controlling groundwater dynamics within aquifers lying above the Rupelian Clay aquitard. The dataset can be further utilized for assessing shallow geothermal potential and groundwater storage availability in the Berlin-Brandenburg region under climate change.
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 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).
The Regional Research Network „Water in Central Asia“ (CAWa) funded by the German Federal Foreign Office consists of 19 remotely operated multi-parameter stations (ROMPS) in Central Asia. These stations were installed by the German Research Centre for Geosciences (GFZ) in Potsdam, Germany in close cooperation with the Central-Asian Institute for Applied Geosciences (CAIAG) in Bishkek, Kyrgyzstan, the national hydrometeorological services in Uzbekistan and Tajikistan, the Ulugh Beg Astronomical Institute in Tashkent, Uzbekistan, and the Kabul Polytechnic University, Afghanistan. The primary objective of these stations is to support the establishment of a reliable data basis of meteorological and hydrological data especially in remote areas with extreme climate conditions in Central Asia for applications in climate and water monitoring. Up to now, ten years of data are provided for an area of scarce station distribution and with limited open access data which can be used for a wide range of scientific or engineering applications. This dataset provides different types of raw hydrometeorological data such as air temperature, relative humidity, air pressure, wind speed and direction, precipitation, solar radiation, soil moisture and soil temperature as well as snow parameters and river discharge information for selected sites. The data has not undergone any quality control mechanism and should, therefore, be seen as raw data. A visual inspection of the data set has been made and some errors and quality degradation are listed in Zech et al. (2020) but does not claim to be complete. A quality control is strongly recommended by the authors before using the data. Each station data has its own storage directory at the data dissemination server named with the abbreviation (4-letter code) of the station. The data is sampled with a 5-minute interval and stored in hourly files separated by the type of data. These files are then archived as monthly files named with the station abbreviation, type of data, year and month. After one year, these monthly files are further archived to a yearly file. A detailed description for the stations is provided by the Station Exposure Descriptions. Further information about the dataset can be found in Zech et al. (2020). All data is compiled as ASCII data in two different formats which are explained in the documents GITW-SSP-FMT-GFZ-003.pdf (for the stations ALAI, ALA6, and SARY) and CAWA-SSP-FMT-GFZ-006.pdf (for all other stations). Monthly, the data will be dynamically extended as long as data can be acquired from the stations. Additionally, the near real-time data can be displayed and downloaded without any registration from the Sensor Data Storage System (SDSS) hosted at the Central-Asian Institute for Applied Geosciences (CAIAG) in Bishkek, Kyrgyzstan.
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 data publication contains the datasets generated in a study aiming at reconstructing paleoclimatic conditions during the late Holocene in northern Philippines. The data come from samples taken from sediment lakes retrieved from Bulusan Lake on the Luzon Island, Philippines. On these samples we measured the stable-hydrogen-isotopic composition of terrestrial-lipid biomarkers to reconstruct ENSO dynamics and past hydrological conditions, pollen data to reconstruct past vegetation, and magnetic susceptibility measurements of the sediment cores to reconstruct past erosion rates. This is complemented with isoGSM2 data to constrain modern hydrological conditions. The data was generated between 2013-04 and 2020-9. The data files are provided in Excel and tab-delimited text versions.
The described dataset resulted from a joint multidisciplinary measurement campaign in an agroforestry system in the Western Cape region in South Africa. Five participating institutions measured a range of environmental variables to characterise the influence of windbreak trees onto water fluxes, nutrient distribution and microclimate in the adjacent blackberry field. The dataset contains spatially collected soil characteristics, a soil profile description, time series of meteorological measurements as well as soil moisture and matric potential, information on soil hydraulic properties of the soil determined in the laboratory and windbreak characteristics and shape from a point cloud derived from terrestrial LiDAR scanning.
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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