Other language confidence: 0.9977712303320049
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 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 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.
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.
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).
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.
The Regional Research Network „Water in Central Asia“ (CAWa) funded by the German Federal Foreign Office consists of 18 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. 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. 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. 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). The data are accessible via ftp and organised as subfolders for each station. The top folder additionally provides: (1) a station overview with metadata for each station (name, partners, starting date, coordinates, sensors; 2020-002_Zech_ROMPS-station_overview.pdf) (2) the manuals of the sensors (2020-002_Zech_ROMPS_Manuals.zip), and (3) the datasheets with technical specification of the sensors (2020-002_Zech_ROMPS_Datasheets.zip). 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.
Davis Vantage Pro Rainfall Collectors are used in a wide range of projects worldwide even in remote and inaccessible regions (cf. Krois et al. 2013). However in remote areas or if a large number of collectors is used parallel, measurement errors for each device can hardly be quantified. To provide a dataset that allows an estimation of errors that occur in observations with Davis Vantage Pro collectors, comparative rainfall data were obtained at the hydro-meteorological monitoring station in Berlin-Lankwitz. The station is located at Geo Campus Lankwitz (Freie Universität Berlin, Department of Earth Sciences) at an elevation of 45 m a.s.l. and consists of a 7.5 x 7.5 m wide fenced measuring field covered by short grass which is cut in weekly intervals. The field is equipped with a range of rainfall measuring devices including a Hellmann rain gauge and a Vantage Pro collector. A comparison of both time series allows a general estimation of potential measuring errors of the Davis Vantage Pro data, assuming that the Hellmann data are less affected by random measuring errors. The data are intended to support the interpretation of rainfall records of Davis Vantage Pro stations in studies without control instruments and to enable users to apply their own statistical analysis to the data. However the dataset does not contain a continuous weather record. The detailed time series are published separately. The Hellmann gauge installed on the monitoring field has a standard diameter of 16 cm (area: 200 m²), is made of stainless steel and mounted 1 m above ground. Rain water is collected in a steel can, which is emptied manually every morning from Monday to Friday using a DIN58667 measuring glass. Between December and February accumulated snow and ice is thawed. Monday's observations representing a three day period (weekend) are excluded from the data set to avoid errors caused by evaporation and sublimation from the collector. The Davis VantagePro tipping bucket is part of a DAVIS Vantage Pro ISS (Integrated Sensor Suite, DAV-6323EU) with a collector diameter of 16.3 cm and a collecting area of 210 cm² respectively. The system was manufactured before 2007. The top of the plastic rain collector is mounted 2 m above ground. From December to February the collector is heated using the DAV-7720EU heating system. The measuring resolution of the tipping bucket is 0.25 mm (0.01 inch). Rainfall is logged in 15-minute intervals and for the error analysis data are aggregated according to the reading intervals of the Hellmann gauge. The dataset contains a total of 72 paired daily rainfall observations obtained in 2017. The difference between Hellmann gauge and Vantage Pro (referred to as measurement error) ranges from -1.6 (Vantage Pro underestimates) to 3 mm (Vantage Pro overestimates) with a mean absolute error of 0.6 mm (median: 0.4 mm). 75 % of the absolute error values are below 0.7 mm. If a linear regression model is applied the absolute error does not increase significantly with increasing daily precipitation (R²: 0.25). However, the low R² is caused by the fact that for the June 2017 extreme rainfall event (29/30 June: 95.7 mm in 24 hours) one of the lowest errors in the data set (-0.2 mm) was calculated. Removing of this outlier results in an increased R² of 0.75. However, it should be noted that as measurements continue after 2017 the sample size will increase year by year. The data are provided as a tab-separated TXT file with column names in the first line. The first and second column contain the beginning ("From_Date") and end ("Until_Date") of each reading interval (date format: DD.MM.YYYY hh:mm). In the third column (“DavisVantagePro”) rainfall of the Vantage Pro tipping bucket is listed and the last column (“Hellmann”) contains the rainfall measured with the Hellmann collector (in mm).
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