No active filters. Use the sidebar to filter search results.
6717 documents found in 345ms
# 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
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.
# 4
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.
# 5
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.
# 6
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.
# 7
von Bloh, Werner • Schaphoff, Sibyll • Müller, Christoph • Rolinski, Susanne • Waha, Katharina • (et. al.)
Abstract: LPJmL5 is a dynamical global vegetation model that simulates climate and land-use change impacts on the terrestrial biosphere, the water, carbon and nitrogen cycle and on agricultural production. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included into the model. A comprehensive description of the model is given by von Bloh et al. (2017,http://doi.org/10.5194/gmd-2017-228). We here present the LPJmL5 model code described and used by the publications in GMD: Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5) (von Bloh et al., 2017) The model code of LPJmL5 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL5 is given in the INSTALL file. Additionally to the publication a html documentation and manual pages are provided. The LPJmL5 version is based on LPJmL3.5 that is not publicly available. The LPJmL4 version without nitrogen cycle but with an updated phenology scheme can be found on github (https://github.com/PIK-LPJmL/LPJmL).
# 8
Schaphoff (Ed.), Sibyll • von Bloh, Werner • Thonicke, Kirsten • Biemans, Hester • Forkel, Matthias • (et. al.)
Abstract: LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
# 9
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)
# 10
Kind, Rainer
Abstract: Durchleuchtung der Litosphäre und des oberen Mantels mit Hilfe aktiver und passiver Seismologie (hier nur passiver Teil). Receiver Funktionen und SKS-Anisotropie Methoden sollen angewandt werden. Ziel ist die Rolle eines Mantelplumes in einem aktiven Kontinentalrand zu untersuchen. Waveform data are available from the GEOFON data centre, under network code XC under CC-BY 4.0 license.
spinning wheel Loading next page