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The DFG Priority Program 1803 "EarthShape - Earth Surface Shaping by Biota” (www.earthshape.net) installed three meteorological stations at an elevational gradient in the National Park La Campana, Chile, in the sector Ocoa, within one catchment, that is one of the four EarthShape core research sites. They are located at a valley position, at the slope and the crest of the catchment. For reference, the valley station is neighbouring a weather station (Campbell Scientific) that the EarthShape project has installed earlier, in 2016 (Übernickel et al., 2020). The other two weather stations are installed on higher elevations. The weather stations are intended to provide baseline meteorological data along the elevational gradient within the La Campana catchment. Each station is configured to include sensors that record air temperature, relative humidity, barometric pressure as well as total solar radiation at 2 m height; precipitation at 1 m height. The data recording started in March 2019. This publication provides raw data as downloaded from the three stations, appended to one single *.xlsx file per station. The data is measured in 30 minutes intervals. The full description of the data and methods is provided in the data description file.
The DFG Priority Program 1803 "EarthShape - Earth Surface Shaping by Biota” (www.earthshape.net, short description of the project below) installed a meteorological station network consisting of four stations between ~26 °S to ~38 °S in the Coastal Cordillera of Chile, South America. The stations are intended to provide baseline meteorological data along the climate and ecological gradient investigated in the EarthShape program. The stations are located in the EarthShape study areas, encompassing desert, semi-desert, mediterranean, and temperate climate zones. Each station is configured to include sensors that record precipitation at ground level, radiation at 2.8 m height, wind at 3 m height, 25 cm depth soil temperature, soil water content and bulk electrical conductivity, 2 m air temperature and relative humidity, and barometric pressure at 30-minute intervals. The data recording started in March/April 2016. The EarthShape project runs until December 2021. Data collection will continue until that date, and potentially longer depending on available funds. This publication provides two sets of data: raw data and processed data. The raw data contains 2 file types per meteorological station: (1) all measured parameters of the whole dataset measured in 30 minutes intervals as downloaded from the station. Furthermore, we provide (2) one table per station of high-resolution precipitation events, measured in 5 min. intervals that were triggered during rain events at each station. The processed data consists of a continuous timeseries of observations since the activation of each station. The processing consists of the exclusion of erroneous data, caused by maintenance of the weather-stations and sporadic malfunction of sensors detected during data screening. The excluded data is communicated in a logfile (excel table), comments from data screening, solar eclipse and others are summarized in history files (ASCII ). the full description of the data and methods is provided in the data description file (Data description file). ----------------------- Version history: 16 January 2023 (Version 1.1): Alexander Beer included as additional author, addition of new data from 2020-04-14 bis 2022-10-10. All files of the first version are moved to the "previous-versions" folder. 09 October 2023 (Version 1.2): Addition of new time series data to 2023-07-31. Detailed changelog information can be found in the “History” files in the respective subfolders for each site.
The dataset contains an urban weather record from the hydro-meteorological monitoring station of the Institute of Geographical Sciences at the Freie Universitaet Berlin (working group Applied Geography, Environmental Hydrology and Resource Management; Geo Campus Lankwitz, Malteserstraße 74-100, 12249 Berlin). The station is located 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 (spring to fall) to ensure reference evaporation conditions.The field is equipped with a range of redundant devices that record weather information. In this summary we focus on a description of the devices from which data were included in the published dataset. A actual list of all devices is available at the Website of the Hydrometeorological monitoring station "Berlin-Lankwitz, FU Geo Campus" (https://www.geo.fu-berlin.de/en/geog/fachrichtungen/angeog/Messfeld-auf-dem-Campus/index.html).The dataset contains rainfall, air temperature, humidity, dew point temperature, air pressure, solar radiation as well as wind speed and direction, each measured in intervals of 15 min. It starts in January 2017 and is updated annually. Rainfall is collected with a Davis VantagePro tipping bucket which is part of the ISS (Integrated Sensor Suite, DAV-6323EU, manufactured before 2007) and mounted 2 m above ground. The collector diameter is 16.3 cm resulting in a collecting area of 210 cm². The measuring resolution of the tipping bucket is 0.25 mm (0.01 inch). During winter the DAVIS rain gauge is heated using the DAV-7720EU heating system. The begin of the heating period in each year is determined by the air temperature and starts before the daily minimum drops below 0°C. In addition a stainless steel Hellmann gauge with standard diameter of 16 cm (area: 200 m²) is installed on the monitoring field 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. Paired data from the Hellman and DAVIS collector to assess accuracy are published separately (Reinhardt-Imjela et al. 2018). Temperature (°C), humidity (%) and air pressure (hPa) are measured 2 m above ground with the DAVIS ISS and the dew point is generated automatically from the data. Temperature includes mean, minimum and maximum of each 15 minute interval. Wind speed and direction are recorded by a Vaisale Weather Transmitter WXT520 2 m above ground. For solar radiation (W/m²) a Kipp & Zonen CMP3 Pyranometer is mounted also 2 m above ground.The data are provided as a tab-separated ASCII file with column names in the first line. The first column contains the date and time (date format: DD/MM/YYYY hh:mm). In the following columns all measured parameters are listed (units are included in the column name). Measuring errors or missing values are marked with “N/A”. Empty fields for the wind direction indicate intervals without measurable wind speed.
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 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.
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.
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.
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