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  • Data  (7)
  • GFZ German Research Center for Geosciences  (5)
  • CRC/TRR228 Database (TRR228DB)  (2)
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  • 1
    Publication Date: 2022-01-04
    Description: Abstract
    Description: Existing methodologies for estimating woody aboveground biomass and carbon stored therein have been developed for forests but are not tailored to the vast dryland ecosystems where vegetation is heterogenous and highly disturbed. Still, those methods are widely applied with questionable results and possible problematic implications, not only for biomass quantification but also for disturbance ecology, biodiversity research, and ecosystem service assessments. We hereby propose a new methodology especially designed to encompass small, disturbed, and irregular woody growth while keeping sampling effort within reasonable limits. Meaningful demographic growth classes are deployed which enable a stratified sampling design and structure a practicable workflow for integration of different allometric models. To account for the high natural and anthropogenic disturbance levels typically shaping dryland vegetation, our method incorporates a detailed damage assessment by harnessing the ecological archive contained in trees. This allows for quantification of biomass losses to certain disturbance agents, uncovers interactive effects between disturbance agents, and enables assessing the impact of disturbance regime shifts. Extrapolation of biomass losses to stand or landscape level also greatly improves the usual reference state comparison approach. Here, we review the problems of conventional methodologies being applied to drylands, develop and present the improved method proposed by us, and perform a formal method comparison between the two. Results indicate that the conventional allometric method is systematically underestimating biomass and carbon storage in disturbed dryland ecosystems. The bias is highest where general biomass density is lowest and disturbance impacts are severest. Damage assessment demonstrates a dependency between main disturbance agents (elephants and fire) while generally biomass is decreased by increasing elephant densities. The method proposed by us is more time consuming than a conventional allometric approach, yet it can cover sufficient areas within reasonable timespans. Consequent higher data accuracy with concomitant applicability to a wider range of research questions are worth the effort. The proposed method can easily be attuned to other ecosystems or research questions, and elements of it may be adapted to fit alternative sampling schemes.
    Description: Other
    Description: This article is a preprint and has not been certified by peer review. The finally published paper can be accessed at: https://doi.org/10.1016/j.ecolind.2021.108466
    Keywords: Ecology ; Biota ; Biomass ; Carbon ; Carbon Storage Dynamics ; Conservation Areas ; Ecology ; Ecosystem ; National Park ; Vegetation ; Vegetation Structure ; Wildlife
    Type: Text , Text
    Format: PDF
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  • 2
    Publication Date: 2022-01-11
    Description: Abstract
    Description: Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Taiwan 2022 is based on the WSM database release 2016. However, all data records have been checked and we added a large number of new data from earthquake focal mechanisms from the national earthquake catalog and from publications. The total number of data records has increased from n=401 in the WSM 2016 to n=6,498 (4,234 with A-C quality) in the stress map of Taiwan 2022 The update with earthquake focal mechanims is even larger since another 1313 earthquake focal mechanism data records beyond the scale of this map have been added to the WSM database. The digital version of the stress map is a layered pdf file generated with GMT (Wessel et al., 2019). It also provide estimates of the mean SHmax orientation on a regular 0.1° grid using the tool stress2grid (Ziegler and Heidbach, 2019). Two mean SHmax orientations are estimated with search radii of r=25 and 50 km, respectively, and with weights according to distance and data quality. The stress map and data are available on the landing page at https://doi.org/10.5880/WSM.Taiwan2022 where further information is provided. The earthquake focal mechanism that are used for this stress map are provided by the Taiwan Earthquake Research Center (TEC) available at the TEC Data Center (https://tec.earth.sinica.edu.tw).
    Description: Other
    Description: The World Stress Map (WSM) is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ German Research Centre for Geosciences and since 2012 the WSM is a member of the ICSU World Data System. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale.
    Type: Other , Other
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  • 3
    Publication Date: 2022-03-09
    Description: Abstract
    Description: Nature conservation and restoration in terrestrial ecosystems is often focused on increasing the numbers of megafauna, expecting them to have positive impacts on ecological self-regulation processes and biodiversity. In sub-Saharan Africa, conservation efforts also aspire to protect and enhance biodiversity with particular focus on elephants. However, elephant browsing carries the risk of woody biomass losses. In this context, little is known about how increasing elephant numbers affects carbon stocks in soils, including the subsoils. We hypothesized that (1) increasing numbers of elephants reduce tree biomass, and thus the amount of C stored therein, resulting (2) in a loss of soil organic carbon (SOC). If true, a negative carbon footprint could limit the sustainability of elephant conservation from a global carbon perspective. To test these hypotheses, we selected plots of low, medium, and high elephant densities in two national parks and adjacent conservancies in the Namibian component of the Kavango Zambezi Transfrontier Area (KAZA), and quantified carbon storage in both woody vegetation and soils (1 m). Analyses were supplemented by the assessment of soil carbon isotopic composition. We found that increasing elephant densities resulted in a loss of tree carbon storage by 6.4 t ha−1. However, and in contrast to our second hypothesis, SOC stocks increased by 4.7 t ha−1 with increasing elephant densities. These higher SOC stocks were mainly found in the topsoil (0–30 cm) and were largely due to the formation of SOC from woody biomass. A second carbon input source into the soils was megaherbivore dung, which contributed with 0.02–0.323 t C ha−1 year−1 to ecosystem carbon storage in the low and high elephant density plots, respectively. Consequently, increasing elephant density does not necessarily lead to a negative C footprint, as soil carbon sequestration and transient C storage in dung almost compensate for losses in tree biomass.The dataset contains the raw data of soil analyses up to 1 m soil depth and vegetation data on plot level. A third sheet of the excelfile contains necessary information about abbreviations used within the dataset.
    Keywords: Ecology ; Environment ; Carbon ; Soil Organic Carbon ; Conservation Areas ; Ecosystem ; Vegetation
    Type: Dataset , Dataset
    Format: MS Excel
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  • 4
    Publication Date: 2022-03-10
    Description: Abstract
    Description: Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Great Britain and Ireland 2022 is based on the WSM database release 2016. All data records have been checked and we added a number of new data from earthquake focal mechanisms from the national earthquake catalog and borehole data. The number of data records has increased from n=377 in the WSM 2016 to n=474 in this map. Some locations and assigned quality of WSM 2016 data were corrected due to new information. The digital version of the map is a layered pdf generated with GMT (Wessel et al., 2019) using the topography of Tozer et al. (2019). We also provide on a regular 0.1° grid values of the mean SHmax orientation which have a standard deviation 〈 25°. The mean SHmax orientation is estimated using the tool stress2grid of Ziegler and Heidbach (2019). For this estimation we used only data records with A-C quality and applied weights according to data quality and distance to the grid points. The stress map is available at the landing page of the GFZ Data Services at http://doi.org/10.5880/WSM.GreatBritainIreland2022 where further information is provided.
    Description: Other
    Description: The World Stress Map (WSM) is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ German Research Centre for Geosciences and since 2012 the WSM is a member of the ICSU World Data System. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale.
    Keywords: crustal stress ; in situ stress ; tectonic stress ; crustal stress pattern ; geophysics ; tectonics ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 NEOTECTONICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 FAULT MOVEMENT ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 STRESS
    Type: Dataset , Dataset
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  • 5
    Publication Date: 2023-06-28
    Description: Abstract
    Description: This catalogue is the extended version of “The European-Mediterranean Earthquake Catalogue (EMEC) for the last millennium” (Grünthal and Wahlstrom, 2012, 2012a). It is an earthquake catalogue for tectonic events in the broader European Mediterranean area. It reached from the Azores (Mid-Atlantic Ridge) in the west, to Africa north of the Sahara in the south, the Arctic Sea in the north, and the regions of Levant, eastern Turkey, and the Caucasus in the west. This areal coverage gave the name to the catalogue: EMEC—The European-Mediterranean Earthquake Catalogue. It extends the previous version (Grünsthal and Wahlström, 2012), by the years 2007 to 2021 and thus contains tectonic events for the period AD 1000 to 2021 with a uniform magnitude Mw from the threshold of 3.5. The dataset contains 71271 entries.
    Keywords: earthquake catalogue ; magnitude harmonization ; europe ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE MAGNITUDE/INTENSITY ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE OCCURRENCES ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 CATALOGING ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 DATA DELIVERY ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 TRANSFORMATION/CONVERSION
    Type: Dataset , Dataset
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  • 6
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    GFZ German Research Center for Geosciences
    Publication Date: 2023-12-04
    Description: Abstract
    Description: The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures.
    Description: Other
    Description: GNU General Public License, Version 3, 29 June 2007 Copyright © 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany FAST Calibration is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. FAST Calibration is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
    Keywords: geomechanical-numerical model ; stress ; in-situ stress ; model calibration ; stress tensor calibration ; modelling tool ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 NEOTECTONICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 CRUSTAL MOTION 〉 CRUSTAL MOTION DIRECTION ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 FAULT MOVEMENT ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 PLATE BOUNDARIES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 STRESS
    Type: Software , Software
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  • 7
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    Unknown
    GFZ German Research Center for Geosciences
    Publication Date: 2023-12-04
    Description: Abstract
    Description: The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures. In order to bridge the scale gap between a regional stress model and a local reservoir model, the multistage calibration procedure is applied where a local model is calibrated solely on the stress state provided by a regional model. FAST Calibration provides the necessary tools and guidelines.
    Keywords: geomechanical-numerical model ; stress ; in-situ stress ; model calibration ; stress tensor calibration ; modelling tool ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 NEOTECTONICS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 CRUSTAL MOTION 〉 CRUSTAL MOTION DIRECTION ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 FAULT MOVEMENT ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 PLATE BOUNDARIES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 STRESS
    Type: Software , Software
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