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  • 1
    Publication Date: 2022-01-03
    Description: Abstract
    Description: This dataset contains processed (downsampled, rotated to local Äspö96 coordinate system, cut) broadband seismograms from two seismometers (Trillium Compact 120s), showing long-period transients on the horizontal components recorded during multiple hydraulic fracturing experiments in the Äspö Hard Rock Laboratory (HRL). Furthermore, the dataset contains extracted tilt time series and the injection parameters of the experiment to allow reproducing the results of Niemz et al. (2021). The seismic waveforms were recorded during meter-scale hydraulic fracturing experiments in the Äspö Hard Rock Laboratory (HRL) in Sweden (Zang et al., 2017). This dataset only contains a subset of the data recorded during the experiments, monitored by a complementary monitoring system. The two seismometers contained in this dataset (A89 and A8B) were located in galleries adjacent/close to the injection borehole (see Fig. 2 in Niemz et al., 2021). The experiments were conducted at the 410m-depth level of the Äspö HRL. Each of the six experiments (HF1 to HF6) consisted of multiple stages with an initial fracturing and three to five refracturing stages (see injection parameters contained in this dataset). The six injection intervals were located along a 28m-long injection borehole. The borehole was drilled sub-parallel to the minimum horizontal compressive stress direction. The distance of the two seismometers to the injection intervals in the injection borehole is between 17 m and 29 m for sensor A89 and 52 m to 72 m for sensor A8B. A89 and A8B correspond to BB1 and BB2 in Niemz et al., 2021. For more details regarding the experimental setup, see Zang et al., 2017; Niemz et al., 2020; and Niemz et al., 2021. The records of the two seismometers show long-period transients that correlate with the injection parameters. These transients are the response of the seismometers to a tilting of the gallery floor. The extracted tilt time series provide independent insight into the fracturing process during the hydraulic stimulations (Niemz et al., 2021).
    Keywords: Tilt ; Äspö Hardrock Laboratory ; Broadband seismometers ; Hydraulic fracturing ; energy 〉 energy type 〉 non-conventional energy 〉 geothermal energy ; In Situ/Laboratory Instruments 〉 Magnetic/Motion Sensors 〉 Seismometers 〉 SEISMOMETERS
    Type: Dataset , Dataset
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  • 2
    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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  • 3
    Publication Date: 2022-01-05
    Description: Abstract
    Description: The Central Andean orogeny is caused by the subduction of the Nazca oceanic plate beneath the South-American continental plate. In Particular, the Southern Central Andes (SCA, 27°-40°S) are characterized by a strong N-S and E-W variation in the crustal deformation style and intensity. Despite being the surface geology relatively well known, the information on the deep structure of the upper plate in terms of its thickness and density configurations is still scarcely constrained. Previous seismic studies have focused on the crustal structure of the northern part of the SCA (~27°-33°S) based upon 2D cross-sections, while 3D crustal models centred on the South-American or the Nazca Plate have been published with lower resolution. To gain insight into the present-day state of the lithosphere in the area, we derived a 3D model that is consistent with both the available geological and seismic data and with the observed gravity field. The model consists on a continental plate with sediments, a two-layer crust and the lithospheric mantle being subducted by an oceanic plate. The model extension covers an area of 700 km x 1100 km, including the orogen, the forearc and the forelands.
    Description: Methods
    Description: Different data sets were integrated to derive the lithospheric features: - We used the global relief model of ETOPO1 (Amante and Eakins 2009) for the topography and bathymetry. - The sub-surface structures were defined by integrating seismically constrained models, including the South-American crustal thickness of Assumpção et al. (2013; model A; 0.5 degree resolution), the sediment thickness of CRUST1 (Laske et al. 2013) and the slab geometry of SLAB2 (Hayes et al. 2018). - Additionally, we included seismic reflection and refraction profiles performed on the Chile margin (Araneda et al. 2003; Contreras-Reyes et al. 2008, 2014, 2015; Flueh et al. 1998; Krawzyk et al. 2006; Moscoso et al. 2011; Sick et al. 2006; Von Huene et al. 1997). - Besides, we used sediment thickness maps from the intracontinental basin database ICONS (6 arc minute resolution, Heine 2007) and two oceanic sediment compilations: one along the southern trench axis (Völker et al. 2013) and another of global-scale (GlobSed; Straume et al. 2019). To build the interfaces between the main lithospheric features, we compiled and interpolated these datasets on a regular grid with a surface resolution of 25 km. For that purpose, the convergent algorithm of the software Petrel was used. We assigned constant densities within each layer, except for the lithospheric mantle. In this case, we implemented a heterogeneous distribution by converting s-wave velocities from the SL2013sv seismic tomography (Schaeffer and Lebedev 2013) to densities. The python tool VelocityConversion was used for the conversion (Meeßen 2017). To further constrain the crustal structure of the upper plate, a gravity forward modelling was carried out using IGMAS+ (Schmidt et al. 2010). The gravity anomaly from the model (calculated gravity) was compared to the free-air anomaly from the global gravity model EIGEN-6C4 (observed gravity; Förste et al 2014; Ince et al. 2019). Subsequently, the crystalline crust of the upper plate was split vertically into two layers of different densities. We inverted the residual between calculated and observed gravity to compute the depth to the interface between the two crustal layers. For the inverse modelling of the gravity residual, the Python package Fatiando a Terra was used (Uieda et al. 2013) For each layer, the depth to the top surface, thickness and density can be found as separate files. All files contain identical columns: - Northing as "X Coord (UTM zone 19S)"; - Easting as "Y Coord (UTM zone 19S)"; - depth to the top surface as "Top (m.a.s.l)" and - thickness of each layer as "Thickness (m)". The header ‘Density’ indicates the bulk density of each unit in kg/m3. For the oceanic and continental mantle units, a separate file is provided with a regular grid of the density distribution with a lateral resolution of 8 km x 9 km and a vertical resolution of 5 km. The containing columns are: Northing as "X Coord (UTM zone 19S)"; Easting as "Y Coord (UTM zone 19S)"; depth as "Depth (m.a.s.l)" and density as "Density (kg/m3)"
    Keywords: Lithosphere ; Gravity Modelling ; Andes ; EARTH SCIENCE ; EARTH SCIENCE 〉 LAND SURFACE 〉 TOPOGRAPHY 〉 TOPOGRAPHICAL RELIEF ; EARTH SCIENCE 〉 OCEANS 〉 BATHYMETRY/SEAFLOOR TOPOGRAPHY 〉 BATHYMETRY ; EARTH SCIENCE 〉 SOLID EARTH ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC LANDFORMS 〉 MOUNTAINS ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC PROCESSES 〉 SUBDUCTION ; EARTH SCIENCE 〉 SOLID EARTH 〉 GRAVITY/GRAVITATIONAL FIELD ; EARTH SCIENCE 〉 SOLID EARTH 〉 GRAVITY/GRAVITATIONAL FIELD 〉 GRAVITY ; EARTH SCIENCE 〉 SOLID EARTH 〉 ROCKS/MINERALS/CRYSTALS 〉 SEDIMENTS ; EARTH SCIENCE SERVICES 〉 MODELS 〉 GEOLOGIC/TECTONIC/PALEOCLIMATE MODELS
    Type: Dataset , Dataset
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  • 4
    Publication Date: 2022-01-05
    Description: Abstract
    Description: The southern Central Andes (SCA, 29°S-39°S) are characterized by the subduction of the oceanic Nazca Plate beneath the continental South American Plate. One striking feature of this area is the change of the subduction angle of the Nazca Plate between 33°S and 35°S from the Chilean-Pampean flat-slab zone (〈 5° dip) in the north to a steeper sector in the south (~30° dip). Subduction geometry, tectonic deformation, and seismicity at this plate boundary are closely related to the lithospheric strength in the upper plate. Despite recent research focused on the compositional and thermal characteristics of the SCA lithosphere, the lithospheric strength distribution remains largely unknown. Here we calculated the long-term lithospheric strength on the basis of an existing 3D model describing the variation of thickness, density and temperature of geological units forming the lithosphere of the SCA. The model consists of a continental plate with sediments, a two-layer crust and the lithospheric mantle being subducted by an oceanic plate. The model extension covers an area of 700 km x 1100 km, including the orogen (i.e. magmatic arc, main orogenic wedge), the forearc and the foreland, and it extents down to 200 km depth.
    Description: Methods
    Description: To compute the lithospheric strength distribution in the SCA, we used the geometries and densities of the units forming the 3D lithospheric scale model of Rodriguez Piceda et al. (2020a,b). The units considered for the rheological calculations are (1) oceanic and continental sediments; (3) upper continental crystalline crust; (4) lower continental crystalline crust; (5) continental lithospheric mantle (6) shallow oceanic crust; (7) deep oceanic crust; (8) oceanic lithospheric mantle; and (9) oceanic sub-lithospheric mantle. The thermal field was derived from a temperature model of the SCA (Rodriguez Piceda et al. under review) covering the same region as the structural model of Rodriguez Piceda et al. (2020a). To calculate the temperature distribution in the SCA, the model volume was split into two domains: (1) a shallow domain, including the crust and uppermost mantle to a depth of ~50 km below mean sea level (bmsl), where the steady-state conductive thermal field was calculated using as input the 3D structural and density model of the area of Rodriguez Piceda et al. (2020b, a) and the finite element method implemented in GOLEM (Cacace and Jacquey 2017); (2) a deep domain between a depth of ~50 and 200 km bmsl, where temperatures were converted from S wave seismic velocities using the approach by Goes et al. (2000) as implemented in the python tool VelocityConversion (Meeßen 2017). Velocities from two alternative seismic tomography models were converted to temperatures (Assumpção et al. 2013; Gao et al. 2021). A detailed description of the method can be found in Rodriguez Piceda et al. (under review). The yield strength of the lithosphere (i.e. maximum differential stress prior to permanent deformation) was calculated using the approach by Cacace and Scheck-Wenderoth (2016). We assumed brittle-like deformation as decribed by Byerlee’s law (Byerlee 1968) and steady state creep as the dominant form of viscous deformation. Low-temperature plasticity (Peierls creep) at differential stresses greater than 200 MPa was also included (Goetze et al. 1978; Katayama and Karato 2008). In addition, effective viscosities were computed from a thermally activated power-law (Burov 2011) We assigned rheological properties to each unit of the model on the basis of laboratory measurements (Goetze and Evans 1979; Ranalli and Murphy 1987; Wilks and Carter 1990; Gleason and Tullis 1995; Hirth and Kohlstedt 1996; Afonso and Ranalli 2004). These properties were chosen, in turn, based on the dominant lithology of each layer derived from seismic velocities and gravity-constrained densities. More methodological details and a table with the rheological properties are depicted in Rodriguez Piceda et al. (under review). The rheological results using the thermal model derived from the seismic tomography of Assumpção et al. (2013) and Gao et al. (2021) can be found in Rodriguez Piceda et al. (under review, under review), respectively
    Description: Other
    Description: Two comma-separated files can be found with the calculated lithospheric temperature, strength and effective viscosity for all the points in the model (2,274,757). These points are located at the top surface of each model unit. Therefore, the vertical resolution of the model is variable and depends on the thickness and refinement of the structural modelled units. SCA_RheologicalModel_V01.csv corresponds to the results using the mantle thermal field from the tomography by Assumpção et al. (2013) and presented in Rodriguez Piceda et al. (under review). SCA_RheologicalModel_V02.csv includes the results using the mantle thermal field of Gao et al. (2021) and presented in Rodriguez Piceda et al. (under review). Each of these files contains the following columns: -Northing as " X COORD (m [UTM Zone 19S]) " -Easting as " Y COORD (m [UTM Zone 19S]) " -Depth to the top surface as " Z COORD (m.a.s.l.)" -Temperature in degree Celsius as " TEMP (deg. C) " -Yield strength in MPa as “STRENGTH (MPa)” -Effective viscosity in base-10 logarithm of Pa*s as “EFF VISCOSITY (log10(Pa*s))” The dimensions of the model is 700 km x 1100 km x 200 km. The horizontal resolution is 5 km, while the vertical resolution depends on the thickness of the structural units.
    Keywords: Lithosphere ; Rheology ; Subduction ; Andes ; EARTH SCIENCE ; EARTH SCIENCE 〉 SOLID EARTH ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC LANDFORMS 〉 MOUNTAINS ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC PROCESSES 〉 SUBDUCTION ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 PLATE TECTONICS 〉 STRESS
    Type: Dataset , Dataset
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  • 5
    Publication Date: 2022-01-05
    Description: Abstract
    Description: The Central Andean orogen formed as a result of the subduction of the oceanic Nazca plate beneath the continental South-American plate. In the southern segment of the Central Andes (SCA, 29°S-39°S), the oceanic plate subducts beneath the continental plate with distinct dip angles from north to south. Subduction geometry, tectonic deformation, and seismicity at this plate boundary are closely related to lithospheric temperature distribution in the upper plate. Previous studies provided insights into the present-day thermal field with focus on the surface heat flow distribution in the orogen or through modelling of the seismic velocity distribution in restricted regions of the SCA as indirect proxy of the deep thermal field. Despite these recent advances, the information on the temperature distribution at depth of the SCA lithosphere remains scarcely constrained. To gain insight into the present-day thermal state of the lithosphere in the region, we derived the 3D lithospheric temperature distribution from inversion of S-wave velocity to temperature and calculations of the steady state thermal field. The configuration of the region – concerning both, the heterogeneity of the lithosphere and the slab dip – was accounted for by incorporating a 3D data-constrained structural and density model of the SCA into the workflow (Rodriguez Piceda et al. 2020a-b). The model consists on a continental plate with sediments, a two-layer crust and the lithospheric mantle being subducted by an oceanic plate. The model extension covers an area of 700 km x 1100 km, including the orogen (i.e. magmatic arc, main orogenic wedge), the forearc and the foreland, and it extents down to 200 km depth.
    Description: Methods
    Description: To predict the temperature distribution in the SCA, the model volume was subdivided into two domains: (1) a shallow domain, including the crust and uppermost mantle to a depth of ~50 km below mean sea level (bmsl), where the steady-state conductive thermal field was calculated using as input the 3D structural and density model of the area (Rodriguez Piceda et al., 2020a-b); (2) a deep domain between a depth of ~50 and 200 km bmsl, where temperatures were converted from S wave seismic velocities (Assumpção et al., 2013) using the approach by Goes et al. (2000) as implemented in the python tool VelocityConversion (Meeßen 2017). The 3D model of Rodriguez Piceda et al. (2020) consists of the following layers: (1) water; (2) oceanic sediments; (3) continental sediments; (4) upper continental crystalline crust; (5) lower continental crystalline crust; (6) continental lithospheric mantle (7) shallow oceanic crust; (8) deep oceanic crust; (9) oceanic lithospheric mantle; and (10) oceanic sub-lithospheric mantle. For the computation of temperatures in the shallow domain, three main modifications were made to the 3D model of Rodriguez Piceda et al. (2020a-b). First, we removed the water layer thus considering the topography/bathymetry as the top of the model. Second, the horizontal resolution was increased to 5 km and, third, the layers were vertically refined by a factor of 3 to 32. We assigned constant thermal properties (bulk conductivity λ and radiogenic heat production S) to each layer of the model according to each lithology (Alvarado et al. 2007, 2009; Ammirati et al. 2013, 2015, 2018; Araneda et al., 2003; Brocher, 2005; Čermák and Rybach, 1982; Contreras-Reyes et al., 2008; Christensen & Mooney, 1995; Gilbert et al., 2006; Hasterok & Chapman, 2011; He et al., 2008; Marot et al., 2014, Pesicek et al., 2012; Rodriguez Piceda et al., 2020; Scarfi & Barbieri, 2019; Vilà et al.,2010; Wagner et al., 2005; Xu et al., 2004). The steady-state conductive thermal field in the shallow domain was calculated applying the Finite Element Method as implemented in the software GOLEM (Cacace & Jacquey, 2017; Jacquey & Cacace, 2017). For the computation, we assigned fixed temperatures along the top and base of the model as thermal boundary conditions. The upper boundary condition was set at the topography/bathymetry and it is the temperature distribution from the ERA-5 land data base (Muñoz Sabater, 2019). The lower boundary condition was set at a constant depth of 50 km bmsl for areas where the Moho is shallower than 50 km bmsl and at the Moho depth proper where this interface is deeper than the abovementioned threshold. The temperature distribution at this boundary condition was calculated from the conversion of S-wave velocities to temperatures (Assumpção et al., 2013).
    Keywords: Lithosphere ; Andes ; Subduction ; Thermal Model ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC LANDFORMS 〉 MOUNTAINS ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC PROCESSES 〉 SUBDUCTION ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOTHERMAL DYNAMICS 〉 GEOTHERMAL TEMPERATURE ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOTHERMAL DYNAMICS 〉 GEOTHERMAL TEMPERATURE 〉 TEMPERATURE PROFILES ; EARTH SCIENCE 〉 SOLID EARTH 〉 ROCKS/MINERALS/CRYSTALS 〉 SEDIMENTS ; EARTH SCIENCE SERVICES 〉 MODELS 〉 GEOLOGIC/TECTONIC/PALEOCLIMATE MODELS
    Type: Dataset , Dataset
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  • 6
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    WDCC
    Publication Date: 2022-01-10
    Description: The hydrodynamic model TRIM-NP in a barotropic mode is used to simulate the strong storm tide in March 1906 forced by ECMWF ERA-20C and CERA-20C ensemble of coupled climate reanalyses (https://www.ecmwf.int). The model area covers the region of 20W to 30E and 42N to 65N with a spatial resolution of 12.8x12.8 km for grid 1. At the lateral boundaries of grid 1, the water level is calculated with tide model FES2004. TRIM-NP calculates one way nested with higher resolution the North Sea (with 6.4km, grid2), southern North Sea (with 3.2km, grid3) and the German Bight (with 1.6km, grid4). In this data bank, the datasets are available hourly for grid 2 and grid 4. Please contact the authors for grid 1 and grid 3.
    Type: experiment
    Format: NetCDF
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  • 7
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    WDCC
    Publication Date: 2022-01-10
    Description: The hydrodynamic model TRIM-NP in a barotropic mode is used to simulate the strong storm tide in March 1906 forced by NOAA-CIRES-DOE Twentieth Century Reanalysis (20CR) version 2c and 3. datasets (https://portal.nersc.gov/project/20C_Reanalysis/). The model area covers the region of 20W to 30E and 42N to 65N with a spatial resolution of 12.8x12.8 km for grid 1. At the lateral boundaries of grid 1, the water level is calculated with tide model FES2004. TRIM-NP calculates one way nested with higher resolution the North Sea (with 6.4km, grid2), southern North Sea (with 3.2km, grid3) and the German Bight (with 1.6km, grid4). In this data bank, the datasets are available hourly for grid 2 and grid 4. Please contact the authors for grid 1 and grid 3.
    Type: experiment
    Format: NetCDF
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  • 8
    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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  • 9
    Publication Date: 2022-01-12
    Description: Das GERICS hat für alle 401 deutschen Landkreise, Kreise, Regionalkreise und kreisfreien Städte einen Klimaausblick veröffentlicht. https://www.gerics.de/products_and_publications/fact_sheets/landkreise/index.php.de Jeder Bericht fasst die Ergebnisse für Klimakenngrößen wie z.B. Temperatur, Hitzetage, Trockentage oder Starkregentage auf wenigen Seiten zusammen. Die Ergebnisse zeigen die projizierten Entwicklungen der Klimakenngrößen im Verlauf des 21. Jahrhunderts für ein Szenario mit viel Klimaschutz, ein Szenario mit mäßigem Klimaschutz und ein Szenario ohne wirksamen Klimaschutz. Datengrundlage sind 85 EURO-CORDEX-Simulationen, sowie der HYRAS-Datensatz des Deutschen Wetterdienstes. GERICS has published a climate report for each of the 401 German districts. https://www.gerics.de/products_and_publications/fact_sheets/landkreise/index.php.de Each report summarizes a selection of climate indices like temperature, hot days, dry days or days with heavy precipitation on a few pages. The results show the future development of these indices in the 21st century for three scenarios with strong, medium and weak climate protection, respectively. The data originates from 85 EURO-CORDEX simulations with regional climate models, and the HYRAS dataset of the German Weather Service.
    Type: experiment
    Format: CSV
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  • 10
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    WDCC
    Publication Date: 2022-01-17
    Description: Source code of the Max Planck Institute Earth System Model (MPI-ESM1.2) adopted to the project PRIMAVERA for the comparison of four different ocean vertical mixing schemes.
    Type: experiment
    Format: tar.gz
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