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# 1
Borg, Erik • Maass, Holger • Renke, Frank • Jahncke, Dirk • Stender, Vivien • (et. al.)
Abstract: This data collection compiles the climate stations of the DEMMIN test site operated by the National Ground Segment Neustrelitz (Remote Sensing Data Center, German Aerospace Center DLR) in cooperation with GFZ German Research Centre for Geosciences (GFZ). The site was originally installed by the DLR in 2000 and has become part of the TERENO Northeastern German Lowland Observatory in 2011. This data collection only comprises the DLR climate stations. Climate and soil moisture stations operated by GFZ are published as separate data compilations (Itzerott et al., 2018, 2018). The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulating ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (1981–2010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25-15 k ago (Weichselian glaciation). Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ) and 63 soil moisture stations (GFZ). A lysimeter-hexagon (DLR, FZJ) was installed near to the village Rustow and is part of the SOILCan project. A crane completes the measurement infrastructure currently available in the test site installed by GFZ/ DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data is carried out by DLR using visual inspection and automatic quality processing is performed by GFZ since 2012. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The dataset is also available through the TERENO Data Discovery Portal. The dataset will be dynamically extended as more data is acquired at the stations. New data will be added after a delay of several months to allow manual interference with the quality control process. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich – FZJ, Helmholtz Centre for Environmental Research – UFZ, Karlsruhe Institute of Technology – KIT, Helmholtz Zentrum München - German Center for Environmental Health – HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center – DLR) ( spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level.
# 2
Ziegler, Moritz O. • Ziebarth, Malte • Reiter, Karsten
Abstract: In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. Especially outcropping or out-pinching geological units, i.e. geological layers that are represented in the model volume, pose serious challenges. Changes in the geometry of a model may occur well into a project at a point, when re-meshing is not an option anymore or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing uniform mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and contains no or only very few and simple structures. The mesh has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the element location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon.
# 3
Rosenau, Matthias • Horenko, Illia • Corbi, Fabio • Rudolf, Michael • Kornhuber, Ralf • (et. al.)
Abstract: This data set provides data from subduction zone earthquake experiments and analysis described in Rosenau et al. (2019). In the experiments analogue seismotectonic scale models of subduction zones characterized by two seismogenic asperities are used to study the interaction of asperities over multiple seismic cycles by means of static (Coulomb failure) stress transfer. Various asperity geometries (lateral/along-strike of the subduction zone distance and vertical/across-strike of the subduction zone offset) are tested on their effect on recurrence pattern of simulated great (M8+) earthquakes. The results demonstrate the role of stress coupling in the synchronization of asperities leading to multi-asperity M9+ events in nature. The data set contains time series of experimental surface velocities from which analogue earthquakes are detected and classified into synchronized events and solo events. The latter are subcategorized into main events and aftershocks and into normal and thrust events. An analogue earthquake catalogue lists all categorized events of the 12 experiments used for statistical analysis. Moreover, results from elastic dislocation modelling aimed ate quantifying the stress coupling between the asperities for the various geometries are summarized. Basic statistics of classified events (e.g. percentage of categorized events, coefficient of variation in size and recurrence time etc.) are documented. Matlab scripts are provided to visualize the data as in the paper.
# 4
Blanke, Aglaja • Kwiatek, Grzegorz • Martínez-Garzón, Patricia • Bohnhoff, Marco
Abstract: This data set is supplementary to the BSSA research article of Blanke et al. (2019), in which the local S-wave coda quality factor at The Geysers geothermal field, California, is investigated. Over 700 induced microseismic events recorded between June 2009 and March 2015 at 31 short-period stations of the Berkeley-Geysers Seismic Network were used to estimate the frequency-dependent coda quality factor (Q_C) using the method of Phillips (1985). A sensitivity analysis was performed to different input parameters (magnitude range, lapse time, moving window width, total coda length and seismic sensor component) to gain a better overview on how these parameters influence Q_C estimates. Tested parameters mainly show a low impact on the outcome whereas applied quality criteria like signal-to-noise ratio and allowed uncertainties of Q_C estimates were found to be the most sensitive factors. Frequency-dependent mean-Q_C curves were calculated from seismograms of induced earthquakes for each station located at The Geysers using the tested favored input parameters. The final results were tested in the context of spatio-temporal behavior of Q_C in the reservoir considering distance-, azimuth and geothermal production rate variations. A distance and azimuthal dependence was found which is related to the reservoir anisotropy, lithological-, and structural features. By contrast, variations in geothermal production rates do not influence the estimates. In addition, the final results were compared with previous estimated frequency-independent intrinsic direct S-wave quality factors (Q_D) of Kwiatek et al. (2015). A match of Q_D was observed with Q_C estimates obtained at 7 Hz center-frequency, suggesting that Q_D might not be of an intrinsic but of scattering origin at The Geysers. Additionally, Q_C estimates feature lower spreading of values and thus a higher stability. The Geysers geothermal field is located approximately 110 km northwest of San Francisco, California in the Mayacamas Mountains. It is the largest steam-dominated geothermal reservoir operating since the 1960s. The local seismicity is clearly related to the water injections and steam production with magnitudes up to ~5 occurring down to 5 km depth, reaching the high temperature zone (up to 360°C). The whole study area is underlain by a felsite (granitic intrusion) that shows an elevation towards the southeast and subsides towards northwest. A fracture network induces anisotropy into the otherwise isotropic rocks featuring different orientations. Moreover, shear-wave splitting and high attenuating seismic signals are observed and motivate to analyze the frequency-dependent coda quality factor. Two data sets were analyzed: one distinct cluster located in the northwest (NW) close to injection wells Prati-9 and Prati-29, and the other one southeast (SE) of The Geysers, California, USA, close to station TCH (38° 50′ 08.2″ N, 122° 49′ 33.7″ W and 38° 46′ 59.5″ N, 122° 44′ 13.2″ W, respectively). The frequency-dependent coda quality factor is estimated from the seismic S-wave coda by applying the moving window method and regression analysis of Phillips (1985). Different input parameters including moving widow width, lapse time and total coda length are used to obtain Q_C estimates and associated uncertainties. Within a sensitivity analysis we investigated the influence of these parameters and also of magnitude ranges and seismic sensor components on Q_C estimates. The coda analysis was performed for each event at each sensor component of each station. The seismograms were filtered in predefined octave-width frequency bands with center-frequencies ranging from 1-69 Hz. The moving window method was applied starting in the early coda (after the S-onset) for each frequency band measuring the decay of Power Spectral Density spectra. The decay of coda amplitudes was fitted with a regression line and Q_C estimates were calculated from its decay slope for each frequency band. In a final step a mean-Q_C curve was calculated for each available station within the study area resulting in different curves dependent on event location sites in the northwest and southeast. Data Description The data contain final mean-Q_C estimates of the NW and SE Geysers, coda Q estimates at 7 Hz center-frequency calculated by using the NW cluster, and initial direct Q estimates of Kwiatek et al. (2015) using the same data of the NW cluster. Table S1 shows final mean coda quality factor estimates obtained from the NW cluster at injection wells Prati-9 and Prati-29. The column headers show stations (station), center-frequencies of octave-width frequency bands in Hertz (f[Hz]), mean coda Q estimates (meanQc) and related standard deviations (std), all obtained by coda analysis. Table S2 shows the final mean coda quality factor estimates obtained from additional selected 100 events in the SE Geysers. Column headers correspond to those in Table S1. Table S3 shows coda Q estimates related to 7 Hz center-frequency. The column headers show stations (station), center-frequency of octave-width frequency bands in Hertz (f[Hz]), coda Q estimates at 7 Hz center-frequency (Q_C) and related standard deviations (std2sigma; 95% confidence level), all obtained by coda analysis. Table S4 shows selected direct S-wave quality factors of Kwiatek et al. (2015) obtained by spectral fitting. The column headers show stations (station) and direct S-wave Q estimates (Q_D). The four tables are provided in tab separated txt format. Tables S3 and S4 are used for a comparative study and displayed in Figure 12 of the BSSA article mentioned above.
# 5
Pick, Leonie • Korte, Monika
Abstract: The HMC (Hourly Magnetospheric Currents) index measures the activity of large-scale magnetospheric currents on Earth's surface from 1900 to 2015. It resolves the absolute intensity of low-frequency variations, especially at periods relevant to the solar cycle, more robustly than existing geomagnetic indices. HMC is based on hourly means of vector magnetic field measurements from 34 mid latitude geomagnetic observatories obtained from WDC Edinburgh ( This data has been manually revised to correct for spikes, jumps and drifts. A detailed description of the derivation method is given in Pick et al., 2018 to which these data are supplementary material. This directory contains the HMC index (hmc1900phor.hor) and the modified observatory data that it is based on ( The index and the observatory data files are formatted in compliance with the IAGA-2002 ASCII exchange format ( Individual file names are composed of:[IAGA code of observatory] + [first active year during 1900-2015] + [p(provisional)] + [hor(hourly)] + [_mod(modified)].hor Also included is information on how the data modifications (list in modifications.pdf) were applied (readme.txt).
# 6
Steed, Robert • Fuenzalida, Amaya
Abstract: This archive contains datasets pertaining to the article "Crowdsourcing triggers rapid, reliable earthquake locations" by Steed et al. (2018). There is a dataset containing the European-Mediterranean Seismological Centre's detections of seismological events via crowdsourced methods (i.e. monitoring of internet traffic on the site, usage of the EMSC app LastQuake or monitoring of tweets containing earthquake related words). This dataset covers the years 2016 and 2017 and contains 2590 detections. The other dataset contains the raw results from testing the CsLoc system (Crowdseeded seismic Location) on the historical data of 2016 and 2017; this system is described in the article for which this dataset is supplemental material. This dataset was used for the creation of the results presented in the article. The archive contains more detailed descriptions of the datasets, which are stored in csv files, including the definition of column heads (*_dataset_description.csv). List of files:2018-068_Steed-et-al_README.txtcrowdsourced_detections_dataset.csvcrowdsourced_detections_dataset_descriptions.csvcrowdsourced_detections_auditting.txtCsLoc_publication_dataset.csvCsLoc_publication_dataset_descriptions.csv
# 7
GEOFON Data Centre
Abstract: P-phase arrival times automatically created by the SeisComP3 ( software at the GFZ scanning all stations available in real-time at GEOFON Data Centre and listed in the contributors list. Data have been used in the publication by Steed et al 2018 to test the new CsLoc method, sent in relatime to EMSC using the HMB application (Heinloo, 2016, The data sets includes P-phases from 2016 and 2017. The data are presented in two csv tables (part I and part II) that are included in the folder
# 8
Stare, Jurij • Kyba, Christopher
Abstract: Radiance Light Trends is a GIS web application that is designed to quickly display information about radiance trends at a specific location (available online at It uses data from two satellite systems, DMSP-OLS and VIIRS DNB, with data processing by NOAA. New VIIRS layers are added automatically as soon as NOAA makes them available to public. The web application allows the user to examine changes in nighttime light emissions (nearly) worldwide, from 1992 up until last month. From 1992 to 2013, data comes from the Operational Linescan System of the Defense Meteorological Satellite Program (DMSP) satellites. From 2012 to the present, data comes from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite instrument (VIIRS DNB). Due to significant differences in the instruments (as described by Miller et al., 2013), it is not possible to have a single record running from 1992 to today. A description of the VIIRS DNB night lights product used in this application was published by Elvidge et al. (2017), the data used in the app can be accessed from the NOAA Earth Observation Group (EOG) Website:
# 9
Corbi, Fabio • Sandri, Laura • Bedford, Jonathan • Funiciello, Francesca • Brizzi, Silvia • (et. al.)
Abstract: This data set includes the results of digital image correlation of one experiment on subduction megathrust earthquakes with interacting asperities performed at the Laboratory of Experimental Tectonics (LET) Univ. Roma Tre in the framework of AspSync, the Marie Curie project (grant agreement 658034) lead by F. Corbi in 2016-2017. Detailed descriptions of the experiments and monitoring techniques can be found in Corbi et al. (2017 and 2019) to which this data set is supplementary material. We here provide Digital Image Correlation (DIC) data relative to a 7 min long interval during which the experiment 
produces 40 seismic cycles with average duration of about 10.5 s (see Figure S1 in Corbi et al., 2019). The DIC analysis yields quantitative about the velocity field characterizing two consecutive frames, measured in this case at the model surface. For a detailed description of the experimental procedure, set-up and materials used, please refer to the article of Corbi et al. (2017) paragraph 2. This data set has been used for: a) studying the correlation between apparent slip-deficit maps and earthquake slip pattern (see Corbi et al., 2019; paragraph 4); and b) as input for the Machine Learning investigation (see Corbi et al., 2019; paragraph 5). Further technical information about the methods, data products and matlab scripts is proviced in the data description file. The list of files explains the file and folder structure of the data set.
# 10
Gosling, Simon • Müller Schmied, Hannes • Betts, Richard • Chang, Jinfeng • Ciais, Philippe • (et. al.)
Abstract: VERSION HISTORY:-On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.- On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit). ----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under For accessing the data set as in before November 27, 2018 please write to the ISIMIP Data Management Team: isimip-data[at] DATA DESCRIPTION: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2
The ISIMIP2a water (global) outputs are based on simulations from 13 global hydrology models (see listing) according to the ISIMIP2a protocol ( The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here:
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