235 documents found in 356ms
# 1
Jeffery, Mairi Louise • Gütschow, Johannes • Gieseke, Robert • Gebel, Ronja
Abstract: PRIMAP-crf is a processed version of data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC) in the Common Reporting Format (CRF). The processing has three key aspects: 1) Data from individual countries and years are combined into one file. 2) Data is re-organised to follow the IPCC 2006 hierarchical categorisation. 3) ‘Baskets’ of gases are calculated according to different global warming potential estimates from each of the three most recent IPCC reports. All Annex I Parties to the United Nations Framework Convention on Climate Change (UNFCCC) are required to report domestic emissions on an annual basis in a 'Common Reporting Format' (CRF). In 2015, the CRF data reporting was updated to follow the more recent 2006 guidelines from the IPCC and the structure of the reporting tables was modified accordingly. However, the hierarchical categorisation of data in the IPCC 2006 guidelines is not readily extracted from the reporting tables. We present the PRIMAP-crf data as a re-constructed hierarchical dataset according to the IPCC 2006 guidelines. Furthermore, the data is organised in a series of tables containing all available countries and years for each GHG individual gas and category reported. In addition to single gases, the Kyoto basket of greenhouse gases (CO2, N2O, CH4, HFCs, PFCs, SF6, and NF3) is provided according to multiple global warming potentials. The dataset was produced using the PRIMAP emissions module. Key processing steps include; extracting data from submitted CRF excel spreadsheets, mapping CRF categories to IPCC 2006 categories, constructing missing categories from available data, and aggregating single gases to gas baskets. The processed data is available under an Creative Commons Attribution 4.0 International License (CC BY 4.0).
# 2
Rybacki, Erik • Herrmann, Johannes • Wirth, Richard • Dresen, Georg
Abstract: Experimentally determined data to examine the creep behavior of an immature carbonate-rich Posidonia shale (Dottenhausen), subjected to constant stress conditions at temperatures between 50 and 200 °C and confining pressures of 50–200 MPa, simulating elevated in-situ depth conditions. The data are described in and supplementary material to Rybacki et al. (2017; http://doi.org/10.1007/s00603-017-1295-y). The data refer to Figure 1 and Table 1 of Rybacki et al. (2017) and are povided in tab-separated ASCII-Format (.dat). The first column represents time in sec and second column the associated axial strain (decimal separator is a comma). An empty line separates data before and after achieving constant stress conditions (cf., Fig. 1 in Rybacki et al., 2017). The following files are included in Rybacki-et-al_2018-001_data.zip: <br> Sample / (confining) Pressure [MPa] / Temperature [°C] / (axial differential) Stress [MPa] / comments: <br>DOT01 / 50 / 100 / 166 <br>DOT02 / 50 / 100 / 150 <br> DOT03 / 50 / 100 / 157 <br>DOT04 / 50 / 100 / 148 / (missing stran values in the upper c. 22.5 sec)DOT09 / 100 / 100 / 169 DOT101 / 100 / 100 / 111 DOT103 / 100 / 100 / 152 DOT104 / 50 / 100 / 154 DOT105 / 150 / 100 / 154DOT107 / 100 / 200 / 113 DOT108 / 100 / 200 / 61 DOT110 / 100 / 50 / 110
# 3
Förster, Hans-Jürgen
Abstract: This data set compiles the results of electron-microprobe spot analyses of monazite-(Ce), xenotime-(Y) and zircon from the two-mica granite massif of Bergen. This massif is composed of compositionally and texturally distinct sub-intrusions, which occasionally contain dark microgranular enclaves and are cross-cut by aplitic dikes. These late-Variscan (c. 325 Ma) granites are evolved, Si-rich (70.6−76.3 wt% SiO2), of transitional I−S-type affinity, and spatially associated with minor W−Mo mineralization. Data indicate that the composition of monazite-(Ce) and zircon changes with fractionation-driven evolution of magma chemistry. In the course of magma differentiation, monazite-(Ce) chemistry evolves towards enrichment Th and U and development of “irregular” chondrite-normalized LREE patterns, with negative anomalies at La or Nd, or both. Monazite-(Ce) precipitated from more evolved magma batches also tends to be richer in MREE and HREE relative to that occurring in early-stage granites. Composition of zircon in more differentiated sub-intrusions displays a large variability. A greater number of grains or domains are distinguished by enrichment in P, Hf, Al, Sc, Y+HREE and low analytical totals, reflecting their crystallization from volatile-rich magmas and/or their interaction with late-magmatic fluids. Xenotime-(Y) chemistry is comparatively insensitive to changes of magma composition that characterized the Bergen massif. The data set published here contains the complete pile of elecron-microprobe analyses for the three accessory minerals monazite-(Ce) (MonaBrg2018), xenotime-(Y) (XenoBRG2018) and zirkon (ZircBRG2018). All tables are presented as Excel (.xlsx) and csv formats. The content of the tables and further data description are given in the data description file.
# 4
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.
# 5
Yan, Rui • Woith, Heiko • Wang, Rongjiang • Wang, Guangcai
Abstract: A high-fidelity radon record covering nearly 40 years from the hot spring site of BangLazhang (BLZ), Southwestern China allows to study multi-year periodicities. At BLZ, radon dissolved in water (Radon), water temperature (WT), and spring discharge rate (DR) were measured daily from 1976 until 2015. Barometric pressure, regional rainfall, galactic cosmic rays (GCR flux is modulated by solar wind and thus a proxy for solar activity), and regional seismicity from the same period were considered to identify potentially influencing factors controlling the changes in radon [Yan et al., 2017]. Various wavelet techniques indicate that the long-period radon concentration is characterized by a quasi-decadal (8-11 years) cycle, matching well with the concurrent periodicity in water temperature, spring discharge rates. The BLZ hot spring monitoring site is maintained and operated by the China Earthquake Administration of Yunnan Province. Water from the spring is sampled once daily and measurements of radon have been performed routinely in a laboratory since 1976 April 6. The sample time is designated to occur at 8 o’clock in the morning in order to reduce the effect of daily variations. The radon concentration has been measured with three types of radon measurement instruments during the past 40 years. From 1976 April 6, to 1982 June 5, a FD-105 type radon gas detector was used, reporting the radon concentration in Eman. Eman is converted to the metric unit Bq/L using the relationship 1 Eman = 3.7 Bq/L. From 1982 June 6 to 2012 April 11, a FD-105K type electrometer (manufactured by Shanghai Electronic Instrument, co.) was used, the measurements given in Bq/L. Since 2012 April 12, a FD-125 type Radon & Thorium analyzer, manufactured by Beijing Nuclear Instrument Factory, sponsored by CNNC (China National Nuclear Corporation), has been used. Water sampled from the spring is degassed by bubbling air and transported into a chamber, where the radon concentration is measured in a ZnS cell connected to a photomultiplier detector, and a scintillation counter. The measurement precision of the instruments is 0.1 Bq/L. A solid radium source (226Ra) with a known radioactive radon content is used for the calibration of the water radon under normal working conditions. This source is used to measure and calculate the calibration value of the instrument. In addition to radon, water temperature and spring discharge rate are measured at the spring site when the water is sampled for radon. Temperature is measured using a mercury thermometer with a resolution of 0.1°C. Discharge rate is measured using the stopwatch capacity method, i.e., the required time per unit volume of water is measured. Barometric pressure has been measured since 1997. Regional rainfall data were downloaded through the CPC Merged Analysis of Precipitation (CMAP) for the same period to evaluate its possible influence on radon in the present study.
# 6
Dykowski, Przemyslaw • Sekowski, Marcin • Krynski, Jan
Abstract: The International Geodynamics and Earth Tide Service (IGETS) was established in 2015 by the International Association of Geodesy. IGETS continues the activities of the Global Geodynamics Project (GGP) between 1997 and 2015 to provide support to geodetic and geophysical research activities using superconducting gravimeter (SG) data within the context of an international network. As a new addition to this network, the iGrav-027 superconducting gravimeter had been installed at the Borowa Gora Geodetic-Geophysical Observatory which has been established in late 1930s. Continuous time-varying gravity and atmospheric pressure data from the SGs at Borowa Gora are integrated in the IGETS data base hosted by ISDC (Information System and Data Center) at GFZ. Borowa Gora Geodetic-Geophysical Observatory is located in Poland, situated 50 km north of Warsaw (longitude: 21.0359 E, latitude: 52.2755 N, height above MSL: 109 m). The operation and maintenance of the Borowa Gora instrumentation is done by staff of the Institute of Geodesy and Cartography. The shortest distance to the Baltic Sea coastline is approx. 240 km. The area is located in a tectonically quiet zone. Geologically the situation is not well recognized, a significant size artificial reservoir is located within 1-2 km from the Observatory. The environment is a not significantly urbanized area with visible daily seismicity. The climate at this place has rough winters (up to -20 degrees Celsius) and hot summers (up to 35 degrees Celsius). The iGrav-027 is located in a specially prepared chamber in the basement of one of the Observatory buildings. It is separated from the compressor operating in a separate room. The location of the gravimeter ensures a relatively stable temperature of 21°C ±2°C throughout the year. The instrument is placed on a specially prepared concrete monument of 1.2 × 1.2 m horizontal and 1.5 m vertical dimensions (ca 1.3 m deep below floor level). The sensor of the instrument is located about 2 m below ground level, and the position and height of the instrument has been determined with a centimetre accuracy, before the installation. The iGrav-027 is co-located in the same building with the A10-020 absolute gravimeter. There are three well monumented pillars for absolute gravity determinations, which can be conducted along with the operating iGrav-027 (e.g. for the comparison with absolute gravimeters). In the vicinity of the observatory several further pillars were set up for various other geodetic antennas and instrumentation. Borowa Gora is a geodynamic observatory comprising space techniques and ground instruments. The iGrav-027 operation started at the end of April 2016, official start is assigned as from 1th of May 2016. Since that time the time series is carried out without interruption up to present. The time sampling of the raw gravity and barometric pressure data of IGETS Level 1 is 1 minute. Future plans include uploading 1s data sampling also. In addition, Borowa Gora is equipped with auxiliary data supporting the interpretation of the SG measurements, which is, however, not provided in the IGETS data base due to complexity. These are a local network of hydrological and meteorological sensors as well as two permanent GNSS (Global Navigation Satellite Systems) stations BOGO and BOGI. Additionally magnetic field variations are also recorded.
# 7
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert • Gebel, Ronja
Abstract: This is an updated version of Gütschow et al. (2017, http://doi.org/10.5880/pik.2017.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its first update. For a detailed description of the changes please consult the CHANGELOG included in the data description document. This 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 2015 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) 1996 categories. For CO2‚‚ from energy and industry time series for subsectors are available. List of datasets included in this data publication:(1) PRIMAP-hist_v1.2_14-Dec-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.2_14-Dec-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.2_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.2_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: - UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2017B)- UNFCCC Biennal Update Reports: UNFCCC (2016)- UNFCCC Common Reporting Format (CRF): UNFCCC (2016), UNFCCC (2017)- BP Statistical Review of World Energy: BP (2017)- CDIAC: Boden et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2016)- Houghton land use CO2: Houghton (2008)- RCP historical data: Meinshausen et al. (2011)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)- SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999)- FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015)
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. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and calculations for the needed agricultural land.
# 8
Natho, Stephanie • Thieken, Annegret
Abstract: Version history:The current M DELENAH 1.1 is an updated version of M DELENAH with changes in the sectors agriculture, unpaved and paved roads, public sector and forest, and industry and commerce (correction of code comment only). Details of code updating are described in the User's Manual. Updates include (1) new features for the agricultural sector (specific livestock loss calculation based on a matrix where numbers of affected animals per type can be inserted), (2) correction of mistakes (wrong divisor, or wrong cell relation – all of less importance for total results in test cases) and (3) exchange of numbers to parameters (to make M DELENAH more convenient most parameters can be directly changed via constants for minimum requirement sheet in excel). As one of the 195 member countries of the United Nations, Germany signed the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR). With this, though voluntary and non-binding, Germany agreed to report on measures taken to reduce disaster impacts and to monitor impacts. Among other targets, the SFDRR aims at reducing direct economic losses in relation to the global gross domestic product by 2030. The United Nations Office for Disaster Risk Reduction (UNISDR) has hence proposed a methodology for consistently estimating direct economic losses per event and country on the basis of physically damaged or destroyed items in different sectors, derived from event documentation, standardized costs per item and mean loss ratios. The method was developed based on experiences from developing countries. Therefore, Natho & Thieken (2018) test the approach for assessing costs of natural hazards in Germany and validate the existing method for an industrialized country for the first time. The methodology, presented here as Excel VBA code, was tested for the three costliest natural hazard types in Germany, i.e. floods, wind and hail storms, considering 12 case studies on the federal or state scale between 1984 and 2016. In the Excel presented here example data sets for one flood, one wind storm, and one hail storm are available. The M. DELENAH Manual provides step-by-step information for recalculating examples, create new data sets and calculate the UNISDR method or adapted versions of the UNISDR method. Adaptation, further than only adapting parameters of the UNISDR method was necessary because analyses of loss and event reports revealed that important damage components are not included in the UNISDR method. Therefore, three new modules were developed to better adapt this methodology to German conditions: transportation (cars), forestry and paved roads. Furthermore, overheads are proposed to include the damage costs of (housing) contents as well as the overall damage costs of urban infrastructure, one of the most important but often neglected damage sectors. Altogether three different versions of the methodology are presented in the Excel. Selection of the version requested is carried out in the readme-sheet where also a short description of the sectors considered can be found. The country-specific method (adapted parameters and modules) is set as default when “Start” is chosen. “Reference” refers to the UNISDR reference method and “Parameter” implies country-specific parameters on the basis of the original modules. Further details on the functioning of the Excel can be found in the M. DELENAH Manual attached to this data publication and information on deduction, calibration and testing are described in detail in Natho & Thieken (2018). The presented versions can be applied to available datasets or datasets created by the user. For application in Europe we suggest applying the country-specific method because the original UNISDR method both over- and underestimates the losses of the tested events by a wide margin. The parameter-adapted method leads to more realistic results and the adapted, country-specific method is finally able to calculate losses well for river floods, hail storms and storms (see Natho & Thieken, 2018). Only for flash floods with huge debris load, where urban infrastructure can account for more than 90% of the total losses, is the method not reasonable. The adapted methodology serves as a good starting point for macro-scale loss estimations by accounting for the most important damage sectors. By publishing the VBA code for adaptation and discussion we aim to support the implementation of the SFDRR and contribute to a better documentation standard after natural hazards. However, the method and data presented is suitable for research purposes only, it has not been tested for engineering/insurance/other practical applications.
# 9
Dykowski, Przemyslaw • Sekowski, Marcin • Krynski, Jan
Abstract: The International Geodynamics and Earth Tide Service (IGETS) was established in 2015 by the International Association of Geodesy. IGETS continues the activities of the Global Geodynamics Project (GGP) between 1997 and 2015 to provide support to geodetic and geophysical research activities using superconducting gravimeter (SG) data within the context of an international network. As a new addition to this network, the iGrav-027 superconducting gravimeter had been installed at the Borowa Gora Geodetic-Geophysical Observatory which has been established in late 1930s. Continuous time-varying gravity and atmospheric pressure data from the SGs at Borowa Gora are integrated in the IGETS data base hosted by ISDC (Information System and Data Center) at GFZ. Borowa Gora Geodetic-Geophysical Observatory is located in Poland, situated 50 km north of Warsaw (longitude: 21.0359 E, latitude: 52.2755 N, height above MSL: 109 m). The operation and maintenance of the Borowa Gora instrumentation is done by staff of the Institute of Geodesy and Cartography. The shortest distance to the Baltic Sea coastline is approx. 240 km. The area is located in a tectonically quiet zone. Geologically the situation is not well recognized, a significant size artificial reservoir is located within 1-2 km from the Observatory. The environment is a not significantly urbanized area with visible daily seismicity. The climate at this place has rough winters (up to -20 degrees Celsius) and hot summers (up to 35 degrees Celsius). The iGrav-027 is located in a specially prepared chamber in the basement of one of the Observatory buildings. It is separated from the compressor operating in a separate room. The location of the gravimeter ensures a relatively stable temperature of 21°C ±2°C throughout the year. The instrument is placed on a specially prepared concrete monument of 1.2 × 1.2 m horizontal and 1.5 m vertical dimensions (ca 1.3 m deep below floor level). The sensor of the instrument is located about 2 m below ground level, and the position and height of the instrument has been determined with a centimetre accuracy, before the installation. The iGrav-027 is co-located in the same building with the A10-020 absolute gravimeter. There are three well monumented pillars for absolute gravity determinations, which can be conducted along with the operating iGrav-027 (e.g. for the comparison with absolute gravimeters). In the vicinity of the observatory several further pillars were set up for various other geodetic antennas and instrumentation. Borowa Gora is a geodynamic observatory comprising space techniques and ground instruments. The iGrav-027 operation started at the end of April 2016, official start is assigned as from 1th of May 2016. Since that time the time series is carried out without interruption up to present. The time sampling of the pre-processed gravity and barometric pressure data of IGETS Level 2 is 1 minute. Level 2 data is derived from Level 1 data corrected for small gaps, major earthquakes and jumps. In addition, Borowa Gora is equipped with auxiliary data supporting the interpretation of the SG measurements, which is, however, not provided in the IGETS data base due to complexity. These are a local network of hydrological and meteorological sensors as well as two permanent GNSS (Global Navigation Satellite Systems) stations BOGO and BOGI. Additionally magnetic field variations are also recorded.
# 10
Waldhoff, Guido
Abstract: This data set contains the preliminary land use classification of 2016 for the study area of the CRC/Transregio 32: "Patterns in Soil-Vegetation-Atmosphere Systems: monitoring, modelling and data assimilation", which corresponds to the catchment of the river Rur. The study area is mainly situated in the western part of North Rhine-Westphalia (Germany) and parts of the Netherlands and Belgium. The classification is provided in GeoTIFF format. Spatial resolution: 15 m; Projection: WGS84, UTM Zone 32N.
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