136 documents found in 455ms
# 1
Raab, Tobias • Reinsch, Thomas • Aldaz Cifuentes, Santiago • Henninges, Jan
Abstract: The data set is a supplement to the publication Raab, T., Reinsch, T., Aldaz Cifuentes, S. R., and Henninges, J. (2019). Real-Time Well Integrity Monitoring using Fiber-Optic Distributed Acoustic Sensing. SPE Journal. http://doi.org/10.2118/195678-PA. The data set contains fiber-optic and conventional logging data recorded for integrity investigations during different drilling stages of Well RN-34, Iceland.
AcknowledgementData was acquired within the framework of project IMAGE (Integrated Methods for Advanced Geothermal Exploration), funded by the EC Seventh Framework Programme under grant agreement No. 608553. This study has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 654497 (GeoWell project), 676564 (EPOS IP), and 691728 (DESTRESS). We would like to thank our partners from the GeoWell project for the excellent collaboration, constant support during data acquisition and analysis as well as the fruitful discussions over the past years. We are especially grateful to Árni Ragnarsson, Ingólfur Örn Þorbjornsson and Gunnar Skúlason Kaldal and their colleagues from ÍSOR as well as Guðmundur Ómar Friðleifsson and Ómar Sigurðsson and their colleagues from HS ORKA.We would like to thank Andi Clarke and his colleagues from Silixa Ltd. for their effort during data acquisition and analysis. At GFZ, we would like to thank David Bruhn, Ernst Huenges, Philippe Jousset, Christian Cunow, Jörg Schrötter, and Ronny Giese as well as all colleagues in section 4.8 Geoenergy who contributed to this project in one form or the other.
# 2
Dahle, Christoph • Flechtner, Frank • Murböck, Michael • Michalak, Grzegorz • Neumayer, Karl Hans • (et. al.)
Abstract: Spherical harmonic coefficients representing an estimate of Earth's mean gravity field during the specified timespan derived from GRACE-FO 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.
# 3
Gütschow, Johannes
Abstract: The PRIMAP-hist Socio-Eco dataset combines several published datasets to create a comprehensive set of population and Gross domestic product (GDP) pathways for every country covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data has no sector resolution. List of datasets included in this data publication: (1) PMHSOCIOECO21_GDP_26-Jul-2019.csv: contains the GDP data for all countries(2) PMHSOCIOECO21_Population_26-Jul-2019.csv: contains the population data for all countries(3) PRIMAP-hist_SocioEco_data_description.pdf: including CHANGELOG(all files are also included in the .zip folder) When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset. Please consider also citing the relevant original sources when using the PRIMAP-hist Socio-Eco dataset. See the full citations in the References section further below. A data description article is in preparation. Until it is published we refer to the description article of the PRIMAP-hist emissions time series for the methodology used. SOURCES: - UN World Population Prospects 2019 (UN2019)- World Bank World Development Indicators 2019 (July) (WDI2019B). We use the *NY.GDP.MKTP.PP.KD* variable for GDP.- Penn World Table version 9.1 (PWT91). We use the *cgdpe* variable for GDP (Robert and Feenstra, 2019; Feenstra et al., 2015)- Maddison Project Database 2018 (MPD2018). We use the *cgdppc* variable for GDP (Bolt et al,, 2018)- Anthropogenic land use estimates for the Holocene – HYDE 3.2 (HYDE32)(Klein Goldewijk, 2017)- Continuous national gross domestic product (GDP) time series for 195 countries: past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100) (Geiger2018, Geiger and Frieler, 2018)Full references are available in the data description document.
Methods:Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.
# 4
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert • Günther, Annika
Abstract: This is an updated version of Gütschow et al. (2019, http://doi.org/10.5880/pik.2019.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.1 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.1_09-Nov-2019.csv: With numerical extrapolation of all time series to 2017. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.1_09-Nov-2019.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.1_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.1_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v4: Andrew (2019)- BP Statistical Review of World Energy: BP (2019)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2019)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2019)- UNFCCC Biennal Update Reports: UNFCCC (2019)- UNFCCC Common Reporting Format (CRF): UNFCCC (2018), UNFCCC (2019), Jeffery et al. (2018) Full references are available in the data description document.
Country resolved data are combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). They are supplemented with growth rates from regionally resolved sources and numerical extrapolations.
# 5
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert
Abstract: This is an updated version of Gütschow et al. (2018, http://doi.org/10.5880/pik.2018.003). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2016, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.0 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.0_11-Dec-2018.csv: With numerical extrapolation of all time series to 2016. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.0_11-Dec-2018.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.0_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.0_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v2: Andrew (2018)- BP Statistical Review of World Energy: BP (2018)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2018)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2018)- UNFCCC Biennal Update Reports: UNFCCC (2018)- UNFCCC Common Reporting Format (CRF): UNFCCC (2017), UNFCCC (2018), Jeffery et al. (2018) Full references are available in the data description document.
Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.
# 6
Ziegler, Moritz O.
Abstract: In geosciences 3D geomechanical-numerical models are used to estimate the in-situ stress state. In such a model each geological unit is populated with the rock properties Young’s module, Poisson ratio, and density. Usually, each unit is assigned a single set of homogeneous properties. However, variable rock properties are observed and expected within the same geological unit. Even in small volumes large variabilities may. The Python script HIPSTER (Homogeneous to Inhomogeneous rock Properties for Stress TEnsor Research) provides an algorithm to include inhomogeneities in geomechanical-numerical models that use the solver Abaqus®. The user specifies the mean values for the rock properties Young's module, Poisson ratio and density, and their variability for each geological unit. The variability of the material properties is individually defined for each of the three rock properties in each geological layer. For each unit HIPSTER generates a normal or uniform distribution for each rock property. From these distri-butions for each single element HIPSTER draws individual rock properties and writes them to a separate material file. This file defines different material properties for each element. The file is included in the geomechanical-numerical analysis solver deck and the numerical model is solved as usual. HIPSTER is fully documented in the associated data report (Ziegler, 2019, http://doi.org/10.2312/WSM.2019.003) and can also be accessed at Github (http://github.com/MorZieg/hipster)
# 7
Metzger, Sabrina • Ischuk, Anatoly • Akhmedov, Akram • Ilyasova, Zukhrah • Moreno, Marcos • (et. al.)
Abstract: We have installed 20 new Global Positioning System (GPS) markers in the West Pamir and the Tajik Depression and measured 25 markers once a year between 2013 and 2016 in survey mode. The stations are positioned along two dense NW-SE oriented profiles with an average spacing of 5-10 km. The profiles cross the Darvaz and the Vakhsh/Ilyak fault and thus monitor the recent slip of these two profiles, which are expected to accommodate the gravity-driven westward extrusion of the West Pamir into the Tajik Depression. Some of the stations include millimeter to centimeter offsets potentially caused by the 2015 Mw7.2 Sarez, Pamir, earthquake.
The markers are 100 mm long stainless steel rods of 8 mm diameter drilled and glued into the ground. Marker positions were measured for nearly 48 hrs per measurement at a sampling rate of 30 s. The data were always acquired in autumn (September to November) to minimize seasonal signal contributions. We used a Trimble R7 receiver and a Trimble Geodetic Zephyr Model 1 (TRM41249.00) antenna on a leveled spike mount with a fixed height of 12.2 cm. The antenna cable plug was oriented towards North whenever possible. Metadata regarding the measurement conditions were archived on paper log sheets. Trimble's proprietary data was converted to ASCII-files using the Trimble software "runpkr00", and then into exchangeable RINEX data using the software "TEQC" (https://doi.org/10.1007/PL00012778), which can be downloaded from the UNAVCO webpage. Finally, mandatory metadata - e.g. antenna and receiver types, marker names, antenna offsets - were added to the header information of the RINEX files. The resulting data presented herein include daily observations in RINEX format. These are organized in yearly and daily folders ("2019-007_Metzger-et-al_data/daily/YYYY/DDD"). Further documentation is found in the folder "2019-007_Metzger-et-al_documentation" and includes the technical reports ("TechnicalReport20YY.pdf") with additional details regarding the installation and remeasurement of the network, waypoint descriptions ("WaypointDescriptions.pdf"), technical aspects of the GPS antenna ("antenna_TRM41249_00.gif"), logsheets documenting additional data acquisition information ("logsheets") as well as example pictures taken during data acquistion ("photo_examples").
# 8
Ganguli, Poulomi • Paprotny, Dominik • Hasan, Mehedi • Güntner, Andreas • Merz, Bruno
Abstract: This dataset comprises time series of 6-hourly surges and the daily streamflow records simulated from hydrodynamic-hydrologic modelling to quantify the compound effects of surges and peak river discharge over northwestern Europe. We simulate the surge height (m) and river discharge (m3/s) at the vicinity of the coast in the reference (1981–2005) and projected (2040–2069) periods using time series of high-resolution (0.11⁰, which is about 12 km) regional dynamically downscaled meteorological forcings from the World Climate Research Program CORDEX (COordinated Regional Climate Downscaling EXperiment) framework (Nikulin et al., 2011) (https://esg-dn1.nsc.liu.se/search/esgf-liu/) for Europe, forced by five host (or parent)-GCMs from the CMIP5 project. Given data availability, we use meteorological forcing dataset from SHMI’s Rossby Centre regional atmospheric model (RCA4; Strandberg et al., 2015) driven by five host GCMs participating in CMIP5, i.e., CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR, MOHC-HadGEM2-ES, and MPI-M-MPI-ESM-LR. For each host GCM, the first ensemble member (r1i1p1) of climate realization has been used except the ICHEC-EC-EARTH model, r12i1p1 realization has been used. All simulations have the same physical version (p1) and initialization method (i1) but differ in initial states (i.e., r1 and r12). After 2005, the future scenarios diverge, and we investigate projected change in compound flood climatology during 2040 – 2069 using business as usual RCP8.5 scenario to cover extremes. While we simulate surge at 33 tide gauges using hydrodynamic model Delft3D (Delft3D-FLOW, 2014), the simulation of discharge from 39 stream gauges is performed using the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014). Since we are mostly interested in the meteorological phenomena that drive the compound flood mechanism, we focus on modeling of surges and do not simulate tides. The individual datasets of the surge and discharge time series for each host GCMs in the GCM-RCM chains are available in the sub-folders ‘Discharge’ and ‘Surge’ under the zip-file ‘CF_drivers’.
To simulate surge from meteorological forcing, we use hydrodynamic model Delft3D (Delft3D-FLOW, 2014) that uses depth-averaged shallow water equations. The model was previously calibrated and validated against observed skew surges for the Euro-CORDEX domain in Paprotny et al. (2016). Details of hydrodynamic model parameters (such as wind drag coefficients and channel roughness) and boundary conditions are discussed in Paprotny et al. (2016). The simulation is driven by 6-hourly resolution sea level pressure and winds (See Data processing section for details) available at 0.11⁰ resolution. To accurately simulate extreme storm surges (for example, annual maxima), the time step of calculations is kept as 30-minutes. We simulate the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014) to simulate current and future runoff at daily time steps from each river basin. WaterGAP simulates runoff, groundwater recharge and water use with a spatial resolution of 0.5⁰ (approximately 55 km) for all land areas except Antarctica. The WaterGAP is calibrated (Müller Schmied et al., 2014) using daily reanalysis-based WFDEI-GPCC (Watch Forcing Data based on ERA-Interim) (Weedon et al., 2014) meteorological forcing. WaterGAP is tuned based on observed river discharge at stations around the world individually and for each ‘first-order’ sub-basin using a tuning parameter, runoff coefficient. For simulating river discharge using WaterGAP, we select locations of stream gauges from medium to large-sized basins with a catchment area between 1000 and 1,05,000 km2 located at a radial distance of within 200 km distance from the tide gauges (Ganguli & Merz, 2019b, 2019a). For more information, please consult the data description document.
# 9
Blanchet, Cécile L.
Abstract: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (e.g. Blanchet, 2018b, 2018a). The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments”, “rocks” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores_V2.txt"). A first version of the database was published in September 2018 and presented data for the African sector. A second version was published in April 2019, in which the dataset has been extended to reach a global extent. The dataset will be further updated bi-annually to increase the geographical resolution and/or add other type of samples. This dataset consists of two tab separated tables: "Dataset_Nd_Sr_isotopes_V2.txt" and "Age_determination_Sediment_Cores_V2.txt". "Dataset_Nd_Sr_isotopes_V2.txt" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores_V2.txt" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A full reference list is provided in the file “References_Database_Nd_Sr_isotopes_V2.rtf”. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (Blanchet, 2018b, 2018a).
# 10
Reyer, Christopher • Silveyra Gonzalez, Ramiro • Dolos, Klara • Hartig, Florian • Hauf, Ylva • (et. al.)
Abstract: Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite, ProfoundData_ASCII.zip) and documented by the following three documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
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