API src

Found 1459 results.

Related terms

LegacyVegetation: Asian reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka

This data set presents the reconstructed vegetation cover for 446 Asian sites based on harmonized pollen data from the data set LegacyPollen 2.0. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by accounting for taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were calculated using the aforementioned taxon-specific parameters and a Gaussian plume model for deposition and dispersal. Values for relative pollen productivity and fall speed from the synthesis from Wiezcorek and Herzschuh (2010) were updated with recent studies used to reconstruct vegetation cover. The average values from all Northern Hemisphere values were used where taxon-specific continental values were unavailable. As REVEALS was conceived to reconstruct vegetation from large lakes, only records originating from large lakes (>= 50h) are marked as "valid as site" in the dataset. Reconstructions from other records can be used when spatially averaging several together. An example script to do so is provided on Zenodo (https://doi.org/10.5281/zenodo.12800290). Reconstructed tree cover was validated using modern Landsat remote sensing forest cover. Reconstructed tree cover has much lower errors than the original arboreal pollen percentages. Reconstructions of individual taxa are more uncertain. We present tables with reconstructed vegetation cover for all continents with original parameters. As further details, we list a table with the taxon-specific parameters used, metadata for all records, and a list of parameters adjusted in the default version of REVEALSinR.

REVEALS reconstruction of past vegetation cover with optimized RPP values for Asian samples

This data set presents the reconstructed vegetation cover for 706 Asian sites based on harmonized pollen data from the data set LegacyPollen 2.0 and optimized RPP values. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal and forest cover was reconstructed. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Sexton et al. 2013) for Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for records in Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.

Global optimized REVEALS reconstruction of past vegetation cover for taxonomically harmonized pollen data sets

This data set presents the reconstructed vegetation cover for 3083 sites based on harmonized pollen data from the data set LegacyPollen 2.0 (https://doi.pangaea.de/10.1594/PANGAEA.965907) and optimized RPP values. 1115 sites are located in North America, 1435 in Europe, and 533 in Asia. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Townshend 2016) for North America, Europe, and Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for all Europe, North America and Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.

Dissolved major element (Ca, Sr, Mg, K, Li) concentration data of the western Atlantic Ocean meridional section GA02 (GEOTRACES) and Arctic Ocean cruise JR271 (BODC)

This dataset presents salinity-normalized dissolved major element (Ca, Mg, K, Sr, Li) concentrations in the western Atlantic Ocean and the Arctic Ocean. Atlantic samples were collected along the western meridional GEOTRACES section GA02 comprised of cruises JR057 (Punta Arenas (Chile) 02-03-2011 to Las Palmas (Spain) 06-04-2011 ), PE321 (Bermuda 11-06-2010 to Fortaleza (Brazil) 08-07-2010), PE319 (Scrabster 28-04-2010 to Bermuda 25-05-2010), and PE358 (Reykjavik (Iceland) 29-07-2012 to Texel (Netherlands) 19-08-2012). Samples for dissolved major ions were sub-sampled from trace metal sample collection stored at the Royal Netherlands Institute for Sea Research (NIOZ). Samples for the Arctic Ocean were collected on BODC cruise JR271 (Immingham 01-06-2012 to Reykjavik 02-07-2012). Samples were analysed for Na, Ca, Mg, K, Li and Sr using a Varian-720 ES ICP-OES. Samples were diluted by a factor of 78-82 in 0.12 M HCl to the same final salinity. Multiple spectral lines were selected for each element, and samples were corrected for instrumental drift by sample-standard bracketing with IAPSO P157 diluted to the same final salinity. Calibration was performed on 7 dilutions of IAPSO P157. Element-to-sodium ratios were calculated for all combinations of spectral lines. Assuming a constant Na-to-salinity (PSU)=35 ratio, the element/Na ratios were multiplied by 0.46847 µmol kg-1 to obtain the salinity (PSU)-normalized element concentration, and by the ratio of practical to absolute salinity (TEOS-10). The TEOS-10 absolute salinities were calculated from EOS-80 values using the Gibb's Oceanographic Toolbox using the R package 'gsw' (v 1.1-1).

Pollen-based climate reconstructions and syntheses in Europe

A fossil pollen dataset distributed across Europe (10° W - 43° E, 33° - 71° N) comprising 520 records was extracted from the LegacyPollen 1.0 database (Herzschuh et al., 2022) to reconstruct climatic variables including Annual temperature (TANN), Annual precipitation (PANN), Winter Temperature (December, January, February; TDJF), Summer Temperature (June, July, August; TJJA). Short records not reaching beyond 1 ka BP were also excluded to keep the dataset refined, as the syntheses aim to cover the entire Holocene (i.e., 11-1 ka BP). The modern pollen training dataset was integrated from Legacy Climate 1.0 (Herzschuh et al., 2023) and the EMPD2 (Davis et al., 2020). Two different approaches were applied in parallel to reconstruct climate variables from fossil pollen assemblages, namely Modern Analogue Technique (MAT) and Weighted Averaging Partial Least Squares (WAPLS). Reconstruction uncertainties were provided as Root Mean Squared Errors of Prediction (RMSEPs). All the reconstructions and tests were conducted using the rioja and analogue packages in R (R Core Team, 2019). The synthesized results were interpolated from all reconstructed climate records. The mean value of reconstructed climatic variables with the same ages was calculated before any interpolations. Due to the different chronological resolution of the time series, the sequences were then interpolated to equidistant time series of 50-year intervals. Two different interpolation methods were applied in R. The first is to use the interp.dataset function from rioja package with loess regression to interpolate the dataset as a whole. The second is to interpolate each complete record that can cover the Holocene (i.e., 11-1 ka) and has a mean resolution of less than 1ka separately using the corit package with linear regression and then calculate the mean of these records. To perform the latter interpolation, a total of 214 records covering the entire period between 11-1 ka BP were used. The Root Mean Squared Errors (RMSEs) were calculated for the synthesis results.

Reservoir-modelling and parametrization of a potential reservoir structure (Pilot area B) in the German North Sea

As part of the CDRmare joint project GEOSTOR (https://geostor.cdrmare.de/), the BGR created detailed static geological 3D models for two potential CO2 storage structures in the Middle Buntsandstein in the Exclusive Economic Zone (EEZ) of the German North Sea and supplemented them with petrophysical parameters (e.g. porosities, permeabilities). The 3D geological model (Pilot area B; ~560 km2) is located in the north-western part of the German North Sea sector, the so-called “Entenschnabel”, an approximately 150 kilometer long and 30 kilometer wide area between the offshore sectors of the Netherlands, Denmark and Great Britain (pilot region B). The model in the Ducks Beak is based on several high-resolution 3D seismic data and geophysical/geological information from four exploration wells. It includes 20 generalized faults and the following 16 horizon surfaces: 1) Sea Floor, 2) Mid Miocene Unconformity, 3) Base Tertiary, 4) Base Upper Cretaceous, 5) Base Lower Cretaceous, 6) Base Upper Jurassic, 7) Base Lower Jurassic, 8) Base Muschelkalk, 9) Base Röt, 10) Base Solling Formation, 11) Base Detfurth Formation, 12) Base Volpriehausen Wechselfolge, 13) Base Volpriehausen Formation, 14) Base Triassic, 15) Base Zechstein, 16) Top Basement. The reservoir formed by sandstones of the Middle Buntsandstein is located within the Mads Graben, which is bounded to the west by the extensive Mads Fault (normal fault). Marine mudstones of the Upper Jurassic and Lower Cretaceous serve as the main seal formations. Petrophysical analyses of all considered well data were conducted and reservoir properties (including porosity and permeability) were calculated to determine the static reservoir capacity for these potential CO2 storage structures. The model parameterized and can be used for further dynamic simulations of storage capacity, geo-risk, and infrastructure analyses, in order to develop a comprehensive feasibility study for potential CO2 storage within the project framework. The 3D models were created by the BGR between 2021 and 2024. SKUA-GOCAD was used as the modeling software. We would like to thank AspenTech for providing licenses for their SSE software package as part of the Academic Program (https://www.aspentech.com/en/academic-program).

Reservoir-modelling and parametrization of a potential reservoir structure (Pilot area A) in the German North Sea

As part of the CDRmare joint project GEOSTOR (https://geostor.cdrmare.de/), the BGR created detailed static geological 3D models for two potential CO2 storage structures in the Middle Buntsandstein in the Exclusive Economic Zone (EEZ) of the German North Sea and supplemented them with petrophysical parameters (e.g. porosities, permeabilities). The 3D geological model (Pilot area A; ~1300 km2) is located on the West Schleswig Block in the area of the Henni salt pillow (pilot region A). It is based on 2D seismic data from various surveys and geophysical/geological information from four exploration wells. The model comprises 14 generalized faults and the following 14 horizon surfaces: 1) Sea Floor, 2) Mid Miocene Unconformity, 3) Base Rupelian, 4) Base Tertiary, 5) Base Upper Cretaceous, 6) Base Lower Cretaceous, 7) Base Muschelkalk, 8) Base Röt (Pelite), 9) Base Röt (Salinar), 10) Base Solling Formation, 11) Base Detfurth Formation, 12) Base Volpriehausen Formation, 13) Base Triassic, 14) Base Zechstein. The selected potential reservoir structure in the Middle Buntsandstein is formed by an anticline created by the uplift of the underlying Henni salt pillow. The primary reservoir unit is the 40-50 m thick Lower Volpriehausen Sandstone, the main sealing units are the Röt and the Lower Cretaceous. Petrophysical analyses of all considered well data were conducted and reservoir properties (including porosity and permeability) were calculated to determine the static reservoir capacity for these potential CO2 storage structures. Both models were parameterized and can be used for further dynamic simulations of storage capacity, geo-risk, and infrastructure analyses, in order to develop a comprehensive feasibility study for potential CO2 storage within the project framework. The 3D models were created by the BGR between 2021 and 2024. SKUA-GOCAD was used as the modeling software. We would like to thank AspenTech for providing licenses for their SSE software package as part of the Academic Program (https://www.aspentech.com/en/academic-program).

LegacyVegetation: European reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka

This data set presents the reconstructed vegetation cover for 1287 European sites based on harmonized pollen data from the data set LegacyPollen 2.0. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by accounting for taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were calculated using the aforementioned taxon-specific parameters and a Gaussian plume model for deposition and dispersal. Values for relative pollen productivity and fall speed from the synthesis from Wiezcorek and Herzschuh (2010) were updated with recent studies used to reconstruct vegetation cover. The average values from all Northern Hemisphere values were used where taxon-specific continental values were unavailable. As REVEALS was conceived to reconstruct vegetation from large lakes, only records originating from large lakes (>= 50h) are marked as "valid as site" in the dataset. Reconstructions from other records can be used when spatially averaging several together. An example script to do so is provided on Zenodo (https://doi.org/10.5281/zenodo.12800290). Reconstructed tree cover was validated using modern Landsat remote sensing forest cover. Reconstructed tree cover has much lower errors than the original arboreal pollen percentages. Reconstructions of individual taxa are more uncertain. We present tables with reconstructed vegetation cover for all continents with original parameters. As further details, we list a table with the taxon-specific parameters used, metadata for all records, and a list of parameters adjusted in the default version of REVEALSinR.

LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka

This data set presents the reconstructed vegetation cover for 2773 sites based on harmonized pollen data from the data set LegacyPollen 2.0 (https://doi.pangaea.de/10.1594/PANGAEA.965907). 1040 sites are located in North America, 1287 in Europe, and 446 in Asia. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by accounting for taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were calculated using the aforementioned taxon-specific parameters and a Gaussian plume model for deposition and dispersal. Values for relative pollen productivity and fall speed from the synthesis from Wiezcorek and Herzschuh (2010) were updated with recent studies used to reconstruct vegetation cover. The average values from all Northern Hemisphere values were used where taxon-specific continental values were unavailable. As REVEALS was conceived to reconstruct vegetation from large lakes, only records originating from large lakes (>= 50h) are marked as "valid as site" in the dataset. Reconstructions from other records can be used when spatially averaging several together. An example script to do so is provided on Zenodo (https://doi.org/10.5281/zenodo.12800290). Reconstructed tree cover was validated using modern Landsat remote sensing forest cover. Reconstructed tree cover has much lower errors than the original arboreal pollen percentages. Reconstructions of individual taxa are more uncertain. We present tables with reconstructed vegetation cover for all continents with original parameters. As further details, we list a table with the taxon-specific parameters used, metadata for all records, and a list of parameters adjusted in the default version of REVEALSinR.

SoilSuite – Sentinel-2 – Europe, 5 year composite (2018-2022)

The SoilSuite contains a collection of different image data products that provide information about the spectral and statistical properties of European soils and other bare surfaces such as rocks. It is created using DLR's Soil Composite Mapping Processor (ScMAP), which utilises the Sentinel-2 data archive. SCMaP is a specialised processing chain for detecting and analysing bare soils/surfaces on a large (continental) scale. Bare surface and soil pixels are selected using a combined NDVI and NBR index (PVIR2) that optimises the exclusion of photosynthetically active and non-active vegetation. The index is calculated and applied for each individual pixel. All SoilSuite products are calculated based on the available Sentinel-2 scenes recorded between January 2018 and December 2022 in Europe. The data package excludes all scenes with a cloud cover of > 80 % and a sun elevation of < 20°. The spectral composite products are calculated from the mean value after extensive removal of clouds, haze and snow effects at both scene and pixel level. The spectral data products are available at a pixel size of 20 m and contain 10 Sentinel-2 bands (B02, B03, B04, B05, B06, B07, B08, B08a, B11, B12). The SoilSuite comprises: (a) “Bare Surface Reflectance Composite – Mean” that provides the spectral properties of soils that vary due to different soil organic carbon (SOC) content, soil moisture and soil minerology. This product is often used for spectral and digital soil mapping approaches, (b) “Bare Surface Reflectance Composite - Standard deviation” informing about the spectral dynamic of bare surfaces and soils, (c) “Bare Surface Reflectance Composite – 95% Confidence” contains information about the reliability of the spectral information due to the number of valid observations per pixel, (d) “Bare Surface Statistics Product” provides the number of bare soil occurrences over the total number of valid observations (Band 1), the number of bare soil occurrences (Band 2) and the total number of valid observations (Band 3), (e) “Mask” is a product that aggregates simple landcover classes that occur during the time period between 2018 - 2022 (Sentinel-2). The three-class Mask contains bare surface occurrences (1), permanent vegetation (2) and other surfaces such as water bodies, urban areas, roads (3). Additionally, the SoilSuite provides (f) “Reflectance Composite – Mean” that represents the mean reflectance of all valid Sentinel-2 observations between 2018 – 2022 including vegetation, bare and other surfaces, and (g) “Reflectance Composite – Standard deviation”, which contains the standard deviation per band for all valid Sentinel-2 observations between 2018 – 2022.

1 2 3 4 5 … 144 145 146