Other language confidence: 0.9977712303320049
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.
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.
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 Alt Plestlin climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The agrometeorological station Alt Plestlin was installed in 2013. It is located within a large field, next to a water area, which is about 1m below the station and within a shallow depression. Smaller bushes are within the closer surrounding of the station. The station is equipped with sensor for measuring the following variables: AdconTR1_Temperature, AdconTR1_RelativeHumidity, AdconRainGauge_Precipitation, AdconWindspeed_Windspeed, AdconWinddirection_Winddirection, AdconBP1_BarometricPressure, KuZCMP3_PyranometerIncoming, KuZCMP3_PyranometerOutgoing, KuZCGR3_PyrgeometerOutgoing, UMSTH3_Soiltemperature005cm, UMSTH3_Soiltemperature010cm, UMSTH3_Soiltemperature020cm, UMSTH3_Soiltemperature030cm, UMSTH3_Soiltemperature050cm, UMSTH3_Soiltemperature100cm, AdconSM1_Soiltemperature015cm, AdconSM1_Soiltemperature045cm, AdconSM1_Soilmoisture010cm, AdconSM1_Soilmoisture020cm, AdconSM1_Soilmoisture030cm, AdconSM1_Soilmoisture040cm, AdconSM1_Soilmoisture050cm, AdconSM1_Soilmoisture060cm, AdconWET_LeafWetness and KuZCGR3_PyrgeometerIncoming. The current version of this dataset is 2.5. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. New authors were added for this new version: Alice Künzel (GFZ Potsdam), Christian Budach (GFZ Potsdam), Nils Brinckmann (GFZ Potsdam), Max Wegener (DLR Neustrelitz) and Klemens Schmidt (DLR Neustrelitz).A detailed overview on all changes is provided in the station description file. Older versions are available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.283 containing the measured data only. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in data processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. During October 2020 a Bug in the published data was detected and a new version of the datasets was released from beginning until mid 2020. Data processing was done using DMRP version: 1.8.4. Metadataprocessing was done using DMETA version: 1.2.0.
The Leppin climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The agrometeorological station Leppin was installed in 2013. It is located within a field, close to a collection of huge stones (~1.5m height) and small shrubs. The station is equipped with sensor for measuring the following variables: AdconTR1_Temperature, AdconTR1_RelativeHumidity, AdconRainGauge_Precipitation, AdconWindspeed_Windspeed, AdconWinddirection_Winddirection, AdconBP1_BarometricPressure, KuZCMP3_PyranometerIncoming, KuZCMP3_PyranometerOutgoing, KuZCGR3_PyrgeometerOutgoing, UMSTH3_Soiltemperature005cm, UMSTH3_Soiltemperature010cm, UMSTH3_Soiltemperature020cm, UMSTH3_Soiltemperature030cm, UMSTH3_Soiltemperature050cm, UMSTH3_Soiltemperature100cm, AdconSM1_Soiltemperature015cm, AdconSM1_Soiltemperature045cm, AdconSM1_Soilmoisture010cm, AdconSM1_Soilmoisture020cm, AdconSM1_Soilmoisture030cm, AdconSM1_Soilmoisture040cm, AdconSM1_Soilmoisture050cm, AdconSM1_Soilmoisture060cm, AdconWET_LeafWetness and KuZCGR3_PyrgeometerIncoming. The current version of this dataset is 2.5. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. New authors were added for this new version: Alice Künzel (GFZ Potsdam), Christian Budach (GFZ Potsdam), Nils Brinckmann (GFZ Potsdam), Max Wegener (DLR Neustrelitz) and Klemens Schmidt (DLR Neustrelitz).A detailed overview on all changes is provided in the station description file. Older versions are available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.268 containing the measured data only. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in data processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. During October 2020 a Bug in the published data was detected and a new version of the datasets was released from beginning until mid 2020. Data processing was done using DMRP version: 1.8.4. Metadataprocessing was done using DMETA version: 1.2.0.
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.
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