239 documents found in 435ms

Data supplement to: Chemistry and Microbiology of the Critical Zone along a steep climate and vegetation gradient in the Chilean Coastal Cordillera
ISO19139(INSPIRE)
DIF
DataCite
Dataset

Oeser, Ralf A.
• Stroncik, Nicole
• Moskwa, Lisa-Marie
• Bernhard, Nadine
• Schaller, Mirjam
• (et. al.)

__Abstract:__The Chilean Coastal Cordillera features a spectacular climate and vegetation gradient, ranging from arid and unvegetated areas in the north to humid and forested areas in the south. The DFG Priority Program "EarthShape" (Earth Surface Shaping by Biota) uses this natural gradient to investigate how climate and biological processes shape the Earth's surface. We explored the critical zone, the Earth's uppermost layer, in four key sites located in desert, semidesert, mediterranean, and temperate climate zones of the Coastal Cordillera, with the focus on weathering of granitic rock. Here, we present first results from four ~2m-deep regolith profiles to document: (1) architecture of weathering zone; (2) degree and rate of rock weathering, thus the release of mineral-derived nutrients to the terrestrial ecosystems; (3) denudation rates; and (4) microbial abundances of bacteria and archaea in the saprolite. From north to south, denudation rates from cosmogenic nuclides are ~10 t km-2 yr-1 at the arid Pan de Azúcar site, ~20 t km-2 yr-1 at the semi-arid site of Santa Gracia, ~60 t km-2 yr-1 at the mediterranean climate site of La Campana, and ~30 t km-2 yr-1 at the humid site of Nahuelbuta. A and B horizons increase in thickness and elemental depletion or enrichment increases from north (~26 °S) to south (~38 °S) in these horizons. Differences in the degree of chemical weathering, quantified by the chemical depletion fraction (CDF), are significant only between the arid and sparsely vegetated site and the other three sites. Differences in the CDF between the sites, and elemental depletion within the sites are sometimes smaller than the variations induced by the bedrock heterogeneity. Microbial abundances (bacteria and archaea) in saprolite substantially increase from the arid to the semi-arid sites. With this study, we provide a comprehensive dataset characterizing the Critical Zone geochemistry in the Chilean Coastal Cordillera. This dataset confirms climatic controls on weathering and denudation rates and provides prerequisites to quantify the role of biota in future studies. The data are supplementary material to Oeser et al. (2018). All samples are assigned with International Geo Sample Numbers (IGSN), a globally unique and persistent Identifier for physical samples. The IGSNs are provided in the data tables and link to a comprehensive sample description in the internet. The content of the eight data tables is: Table S1: Catena properties of the four primary EarthShape study areas.Table S2: Major and selected trace element concentration for bedrock samples.Table S3 Normative modal abundance of rock-forming minerals.Table S4: Major and selected trace element concentration for regolith samples and dithionite and oxalate soluble pedogenic oxides.Table S5: Weathering indices CDF and CIA, and the mass transfer coefficients (τ) for major and trace elements along with volumetric strain (ɛ).Table S6: Chemical weathering and physical erosion ratesTable S7: Relative microbial abundances in saprolite of the four study areas.Table S8: Uncorrected major and trace element concentration. The data tables are provided as one Excel file with eight spreadsheets, as individual tables in .csv format in a zipped archive and as printable PDF versions in a zipped archive.

Dobslaw, Henryk
• Dill, Robert
• Dahle, Christoph

__Abstract:__Spherical harmonic coefficients that represent anomalous contributions of the non-tidal dynamic ocean to ocean bottom pressure during the specified timespan. The anomalous signals are relative to the mean field from 2003-2014.

Dobslaw, Henryk
• Dill, Robert
• Dahle, Christoph

__Abstract:__Spherical harmonic coefficients that represent the sum of the ATM (or GAA) and OCN (or GAB) coefficients during the specified timespan. These coefficients represent anomalous contributions of the non-tidal dynamic ocean to ocean bottom pressure, the non-tidal atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure, and the upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.

Dobslaw, Henryk
• Dill, Robert
• Dahle, Christoph

__Abstract:__Spherical harmonic coefficients that represent anomalous contributions of the non-tidal atmosphere to the Earth's mean gravity field during the specified timespan. This includes the contribution of atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure elsewhere, and the contribution of upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.

Dahle, Christoph
• Flechtner, Frank
• Murböck, Michael
• Michalak, Grzegorz
• Neumayer, Hans
• (et. al.)

__Abstract:__Spherical harmonic coefficients representing an estimate of Earth's mean gravity field during the specified timespan derived from GRACE mission measurements. These coefficients represent the full magnitude of land hydrology, ice, and solid Earth processes. Further, they represent atmospheric and oceanic processes not captured in the accompanying GAC product.

Dobslaw, Henryk
• Dill, Robert
• Dahle, Christoph

__Abstract:__Spherical harmonic coefficients that are zero over the continents, and provide the anomalous simulated ocean bottom pressure that includes non-tidal air and water contributions elsewhere during the specified timespan. These coefficients differ from GLO (or GAC) coefficients over the ocean domain by disregarding upper air density anomalies. The anomalous signals are relative to the mean field from 2003-2014.

Ring-shear test data of plastic sand, a new rock analogue material used for experimental Earth Science applications at Utrecht University, The Netherlands
[version 1]
ISO19139(INSPIRE)
DIF
DataCite
Dataset

Willingshofer, Ernst
• Sokoutis, Dimitrios
• Kleinhans, Maarten
• Beekmann, Fred
• Schönebeck, Jan-Michael
• (et. al.)

__Abstract:__This dataset provides friction data from ring-shear test (RST) on a plastic (polyester) sand material that has been used in flume experiments (Marra et al., 2014; Kleinhans et al., 2017) and is now used in the Tectonic Laboratory (TecLab) at Utrecht University (NL) as an analogue for brittle layers in the crust or lithosphere. Detailed information about the data, methodology and a list of files and formats is given in the data description and list of files that are included in the zip folder and also available via the DOI landing page. The material has been characterized by means of internal friction coefficient and cohesion as a remote service by GFZ Potsdam for TecLab (Utrecht University). According to our analysis the material behaves as a Mohr-Coulomb material characterized by a linear failure envelope and peak, dynamic and reactivation friction coefficients of 0.76, 0.60, and 0.66, respectively. Cohesions are in the order of few tens of Pa. A minor rate-weakening of 3% per ten-fold rate change is evident.

A GOCE only gravity model GOSG01S based on the SGG and SST observations
ISO19139(INSPIRE)
DIF
DataCite
Dataset

Xu, Xinyu

GOSG01S is a static gravity field model complete to spherical harmonic degree of 220 derived by using the Satellite Gravity Gradiometry (SGG) data and the Satellite-to-Satellite Tracking (SST) observations along the GOCE orbit based on least-squares analysis. Input data:-- GOCE SGG data: EGG_NOM_2 (GGT: Vxx, Vyy, Vzz) in GRF (1/11/2009-31/5/2012)-- GOCE SST data: SST_PKI_2, SST_PCV_2, SST_PRD_2 (1/11/2009-5/7/2010)-- Attitude: EGG_NOM_2 (IAQ), SST_PRM_2 (PRM)-- Non-conservative force: Common mode ACC (GG_CCD_1i)-- Background model: tidal model (solid etc.), third-body acceleration, relativistic corrections, ...-- GOSG01S is a GOCE only satellite gravity model, since no priori gravity information was used in modelling procedure. Data progress strategies: Data preprocessing:- Gross outlier elimination and interpolation (only for the data gaps less than 40s).- Splitting data into subsections for gaps > 40s The normal equation from SST data:- Point-wise acceleration approach (PAA)- Extended Differentiation Filter (low-pass)- Max degree: up to 130- Data: PKI, PCV, CCD The normal equation from SGG data:- Space-Wise LS method- Max degree: up to 220- Data: GGT, PRD, IAQ, PRM- Band-pass filter: used to deal with colored-noise of GGT observations (pass band 0.005-0.041Hz )- Forming the normal equations according to subsections- Spherical harmonic base function transformation instead of transforming GGT from GRF to LNRF Combination of SGG and SST:- Max degree: up to 220- The VCE technique is used to estimate the relative weights for Vxx, Vyy, Vzz- Tikhonov Regularization Technique (TRT) is only applied to near (zonal) terms (m<20)- Strictly inverse the normal matrix based on MPI

__Abstract:__We compile the GOCE-only satellite model GOSG01S complete to spherical harmonic degree of 220 using Satellite Gravity Gradiometry (SGG) data and the Satellite-to-Satellite Tracking (SST) observations along the GOCE orbit based on applying a least-squares analysis. The diagonal components (Vxx, Vyy, Vzz) of the gravitational gradient tensor are used to form the system of observation equations with the band-pass ARMA filter. The point-wise acceleration observations (ax, ay, az) along the orbit are used to form the system of observation equations up to the maximum spherical harmonic degree/order 130. The GOCE related satellite gravity models GOSG01S, GOTIM05S, GODIR05S, GOTIM04S, GODIR04S, GOSPW04S, JYY_GOCE02S, EIGEN-6C2 and EGM2008 are also validated by using GPS-leveling data in China and USA. According to the truncation at degree 200, the statistic results show that all GGMs have very similar differences at GPS-leveling points in USA, and all GOCE related gravity models have better performance than EGM2008 in China. This new model was developed by School of Geodesy and Geomatics (SGG) of Wuhan University (WHU) and Institute of Geodesy of University of Stuttgart. More details about the gravity field model GOSG01S is given in our paper “A GOCE only gravity model GOSG01S and the validation of GOCE related satellite gravity models ” (Xu X, Zhao Y, Reubelt T, et al. Geodesy and Geodynamics. 2017, 8(4): 260-272. http://dx.doi.org/10.1016/j.geog.2017.03.013). This work is supported by the National Key Basic Research Program of China (973 program, grant no.: 2013CB733301), the Major International (Regional) Joint Research Project (grant no.: 41210006).

GOSG01S is a static gravity field model complete to spherical harmonic degree of 220 derived by using the Satellite Gravity Gradiometry (SGG) data and the Satellite-to-Satellite Tracking (SST) observations along the GOCE orbit based on least-squares analysis. Input data:-- GOCE SGG data: EGG_NOM_2 (GGT: Vxx, Vyy, Vzz) in GRF (1/11/2009-31/5/2012)-- GOCE SST data: SST_PKI_2, SST_PCV_2, SST_PRD_2 (1/11/2009-5/7/2010)-- Attitude: EGG_NOM_2 (IAQ), SST_PRM_2 (PRM)-- Non-conservative force: Common mode ACC (GG_CCD_1i)-- Background model: tidal model (solid etc.), third-body acceleration, relativistic corrections, ...-- GOSG01S is a GOCE only satellite gravity model, since no priori gravity information was used in modelling procedure. Data progress strategies: Data preprocessing:- Gross outlier elimination and interpolation (only for the data gaps less than 40s).- Splitting data into subsections for gaps > 40s The normal equation from SST data:- Point-wise acceleration approach (PAA)- Extended Differentiation Filter (low-pass)- Max degree: up to 130- Data: PKI, PCV, CCD The normal equation from SGG data:- Space-Wise LS method- Max degree: up to 220- Data: GGT, PRD, IAQ, PRM- Band-pass filter: used to deal with colored-noise of GGT observations (pass band 0.005-0.041Hz )- Forming the normal equations according to subsections- Spherical harmonic base function transformation instead of transforming GGT from GRF to LNRF Combination of SGG and SST:- Max degree: up to 220- The VCE technique is used to estimate the relative weights for Vxx, Vyy, Vzz- Tikhonov Regularization Technique (TRT) is only applied to near (zonal) terms (m<20)- Strictly inverse the normal matrix based on MPI

Stream2segment: a tool to download, process and visualize event-based seismic waveform data
[version 2.7.3]
ISO19139(INSPIRE)
DIF
DataCite
Software

Zaccarelli, Riccardo

__Abstract:__The task of downloading comprehensive datasets of event-based seismic waveforms has been made easier through the development of standardised web services, but is still highly non-trivial, as the likelihood of temporary network failures or even worse subtle data errors naturally increase when the amount of requested data is in the order of millions of relatively short segments. This is even more challenging as the typical workflow is not restricted to a single massive download but consists of fetching all possible available input data (e.g., with several repeated download executions) for a processing stage producing any desired user-defined output. Here, we present stream2segment, a highly customisable Python 2+3 package helping the user through the whole workflow of downloading, inspecting and processing event-based seismic data by means of a relational database management system as archiving storage, which has clear performance and usability advantages. Stream2segment provides an integrated processing implementation able to produce any kind of user-defined output based on a configuration file and a user-defined Python function. Stream2segment can also produce diagnostic maps or user-defined plots which, unlike existing tools, do not require external software dependencies and are not static images but interactive browser-based applications ideally suited for data inspection or annotation tasks.

Continuous national Gross Domestic Product (GDP) time series for 195 countries: past observations (1850-2005) harmonized with future projections according the Shared Socio-economic Pathways (2006-2100)
[version 2.0]
ISO19139(INSPIRE)
DIF
DataCite
Dataset

Geiger, Tobias
• Frieler, Katja

__Abstract:__Version history:This data are a new version of Geiger et al (2017, http:doi.org/10.5880/PIK.2017.003). Please use this updated version of this dataset which contains the following correction of errors in the original dataset: The linear interpolation in GDP per capita for Aruba (ABW) between observations in 2005 and SSP2 projections in 2010 was replaced by observed GDP per capita values for the years 2006-2009, as the SSP2 projection for Aruba turned out to be incorrect. As a result of this, the national GDP per capita and GDP timeseries for Aruba between 2006 and 2009 is different from the previous version. We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries. All data, including the data description are included in a zip folder (2018-010_GDP_1850-2009_Data_v2.zip): (1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv. (2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. (3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv). We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (2018-010_Data-Description-GDP_1850-2009_v2.pdf).

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