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In order to understand the difference between high temperature drop across the mantle's basal thermal boundary layer and much lower plume excess temperatures we evaluated computations with ASPECT. Some of them are published in the Ph.D. thesis of Poulami Roy, some others in previous work. Hence here we only include those models that are not published elsewhere. We also provide the routine to extract maximum and average plume temperatures versus depth. Our results show reduced excess temperatures, if plumes are more sheet-like, similar to 2-D models, or temperature at their source depth is less than at the CMB, for example if they are sourced on top of thermochemical piles. Excess temperatures are further reduced when averaged over the plume conduit or melting region. We provide here the prm files and required input files for the Aspect 2-D cases shown in Figures 2 and 3, which are the only cases that are neither included in Steinberger et al. (2023) nor in the Ph.D. thesis of Poulami Roy (2024). Figure 2 is computed with matteo_4.prm; in this case, the initial temperature is in initial_temp_ascii_2, prescribed (zero) surface velocitites are in vel-top-zero Figure 3 is computed with matteo_14.prm; in this case, the initial temperature is in in initial_temp_ascii_4b. In both cases, radial_visc_simple.txt is the radial viscosity structure corresponding to adiabatic temperatures, and the file temp-viscosity-prefactor.txt specifies the lateral viscosity variations due to temperature variations. We also provide the Routine post_processing_matteo_10km.py for extracting plume temperatures versus depth, written by Matteo Jopke. Furthermore, we provide csv files for all time steps listed in Tables B1 and B2 and shown in Figures 5-7 of the paper. These data have been used to compute plume temperatures and anomalous mass fluxes, in order to address the question posed in the title of the paper. Files are grouped according to model runs into tar files with the same name. The tables are also provided in the Appendix of this data description. The model files are grouped in .tar files according to the model types: single_plume.tar, 2_10.tar; 2.5_2_10.tar; no_slap.tar)
This dataset provides risk estimates from the long-term (5000-year) simulations of the process-based Regional Flood Model chain (RFM) developed for Germany (Falter et al. 2015). The 5000-year simulation is run as an ensemble of 50 100-year simulations. Each of those 100-year simulations is referred to as a scenario. The risk estimates are derived in Euros adjusted to prices as of 2018 for all major catchments in Germany – Elbe, Danube, Rhine, Weser and Ems. The dataset consists of the risk estimates for every simulated event at the catchment-level classified according to the sector – private sector (ps), commercial (com) and agriculture (agr). Losses to buildings and contents are estimated for private and commercial sectors. Crop losses are estimated for the agriculture sector. The full description of the RFM along with the derivation of the risk estimates and uncertainty measurement is provided in Sairam et al. (2021).
The data presented here contains PHREEQC geochemical modeling input and output files to model mineralogical-geochemical reactions due to the CO2 injection at the Ketzin CO2 storage site, Germany. The used modeling tool is PHREEQC version 3.4 (Parkhurst & Appelo, 2013), and the Pitzer database (PITZER.dat) is applied. The geochemical model is conducted to investigate the potential mineral precipitation in the reservoir. The available characterization of the Stuttgart Formation (Norden & Frykman, 2013) and pristine formation fluid (Würdemann et al., 2010) is used in the models. Ketzin baseline (referred as to B, data collected by Würdemann et al. (2010) and post-CO2 injection (referred as to PI, previously unpublished observation data) brine solutions were sampled and analyzed under the surface (B-S and PI-S) and reservoir conditions (B-R and PI-R). Ketzin reservoir pressure and temperature data were obtained at the observation well Ktzi 202 at a depth of 650m before and after the CO2 injection (previously unpublished observation data).
The data set consists of dispersion curves and the corresponding 2D phase velocity maps based on earthquake generated Rayleigh surface waves and ambient noise, as well as the resultant shear-wave velocity model for entire Scandinavia (Norway, Sweden and Finland). We resolved the crust and mantle to 250 km depth to provide new insight into the maintenance of the Paleozoic Scandes mountain range and the lithospheric architecture of the Precambrian Baltic Shield (Mauerberger et al., in review). For this study, we use the virtual ScanArray network which consists of more than 220 seismic stations of the following contributing networks: The ScanArray Core (1G network, Thybo et al., 2012) consists of 72 broadband instruments which were operated by the ScanArray consortium (Thybo et al., 2021) between 2013-2017. We also used 28 stations from the NEONOR2 (2D network), 20 stations from the SCANLIPS3D (ZR network; England et al., 2015), 72 permanent stations from the Swedish National Seismic Network (SNSN; UP network; SNSN 1904) as well as further 35 permanent stations from the Finnish (HE and FN networks), Danish (DK network), Norwegian (NO network (NORSAR, 1971); NS (University of Bergen, 1982)) and international IU network (ALS/USGS, 1988). Since the exact operation times of the different temporary networks differ, we analyse data between 2014 and 2016, when most of the stations were operational. The pre-processing of the data involved the removal of a linear trend, application of a band-pass filter between 0.5 s and 200 s, downsampling to 5 Hz and deconvolution of the instrument response to obtain velocity seismograms. We also corrected for the misorientations stated in Grund et al., 2017.
We construct a precomputed lookup table to predict flood loss to private households based on predictor variables from a Bayesian Network model (BN-FLEMO∆). BN-FLEMO∆ is a probabilistic model that provides multinomial probability distributions of relative building loss (i.e. absolute building loss/building value) in discrete classes. More information on the development of BN-FLEMO∆ can be found in Rafiezadeh Shahi et al. (2025). The zip folder contains the precomputed lookup table, where all possible combinations of predictor and response values are stored. The lookup table contains an ID for each unique combination of possible predictor and response (i.e., relative loss) values. The file name is coded as “2023-002_Rafiezadeh Shahi-et-al_lookup.csv”.
This dataset presents the raw data of an experimental series of analogue models performed to investigate the influence of inherited brittle fabrics on narrow continental rifting. This model series was performed to test the influence of brittle pre-existing fabrics on the rifting deformation by cutting the brittle layer at different orientations with respect to the extension direction. An overview of the experimental series is shown in Table 1. In this dataset we provide four different types of data, that can serve as supporting material and for further analysis: 1) The top-view photos, taken at different steps and showing the deformation process of each model; they can be used to interpret the geometrical characteristics of rift-related faults; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models, allowing for quantitative analysis of the fault pattern. 3) Short movies built from top-view photos which help to visualize the evolution of model deformation; 4) line-drawing of fault and fracture patters to be used for fault statistical quantification. Further details on the modelling strategy and setup can be found in Corti (2012), Maestrelli et al. (2020), Molnar et al. (2020), Philippon et al. (2015), Zwaan et al. (2021) and in the publication associated with this dataset. Materials used for these analogue models were described in Montanari et al. (2017) Del Ventisette et al. (2019) and Zwaan et al. (2020).
In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.
This dataset comprises time series of 6-hourly surges and the daily streamflow records simulated from hydrodynamic-hydrologic modelling to quantify the compound effects of surges and peak river discharge over northwestern Europe. We simulate the surge height (m) and river discharge (m3/s) at the vicinity of the coast in the reference (1981–2005) and projected (2040–2069) periods using time series of high-resolution (0.11⁰, which is about 12 km) regional dynamically downscaled meteorological forcings from the World Climate Research Program CORDEX (COordinated Regional Climate Downscaling EXperiment) framework (Nikulin et al., 2011) (https://esg-dn1.nsc.liu.se/search/esgf-liu/) for Europe, forced by five host (or parent)-GCMs from the CMIP5 project. Given data availability, we use meteorological forcing dataset from SHMI’s Rossby Centre regional atmospheric model (RCA4; Strandberg et al., 2015) driven by five host GCMs participating in CMIP5, i.e., CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, IPSL-IPSL-CM5A-MR, MOHC-HadGEM2-ES, and MPI-M-MPI-ESM-LR. For each host GCM, the first ensemble member (r1i1p1) of climate realization has been used except the ICHEC-EC-EARTH model, r12i1p1 realization has been used. All simulations have the same physical version (p1) and initialization method (i1) but differ in initial states (i.e., r1 and r12). After 2005, the future scenarios diverge, and we investigate projected change in compound flood climatology during 2040 – 2069 using business as usual RCP8.5 scenario to cover extremes. While we simulate surge at 33 tide gauges using hydrodynamic model Delft3D (Delft3D-FLOW, 2014), the simulation of discharge from 39 stream gauges is performed using the global hydrological and water use model, WaterGAP 2.2d (Müller Schmied et al., 2014). Since we are mostly interested in the meteorological phenomena that drive the compound flood mechanism, we focus on modeling of surges and do not simulate tides. The individual datasets of the surge and discharge time series for each host GCMs in the GCM-RCM chains are available in the sub-folders ‘Discharge’ and ‘Surge’ under the zip-file ‘CF_drivers’.
In order to test the feasibility of density and viscosity models suitable to explain geoid and dynamic topography in West Antarctica, we perform computations of a thermal plume that enters at the base of a cartesian box corresponding to a region in the upper mantle, as well as some whole-mantle thermal plume models, as well as some instantaneous disk models, with ASPECT. The plume models have typically a narrow conduit and the plume tends to only become wider as it spreads beneath the lithosphere, typically shallower than ~300 km. These results are most consistent with a shallow disk model with reduced uppermost mantle viscosity, hence providing further support for such low viscosities beneath West Antarctica. The data are a supplement to the following article: Steinberger, B., Grasnick, M.-L. & Ludwig, R., Exploring the Origin of Geoid Low and Topography High in West Antarctica: Insights from Density Anomalies and Mantle Convection Models, Tektonika, https://doi.org/10.55575/tektonika2023.1.2.35
We present a 3-D lithospheric-scale model covering the area of Germany that images the regional structural configuration. The model comprises 31 lithostratigraphic units: seawater, 14 sedimentary units, 14 crystalline crustal units and 2 lithospheric mantle units. The corresponding surfaces are integrated from previous studies of the Central European Basin System, the Upper Rhine Graben and the Molasse Basin, together with published geological and geophysical data. The model is a result of a combined workflow consisting of 3-D structural, gravity and thermal modelling applied to derive the 3-D thermal configuration.The top surface elevations and thicknesses of corresponding layers of the 3-D-D model are provided as ASCII files, one for each individual layer of the model. The columns in each file are identical: the Easting is given in the “X COORD (UTM Zone 32N)”, the Northing is in the “Y COORD (UTM Zone 32N)”, the top surface elevation of each layer is given as "TOP (m.a.s.l)", the thickness of each layer is given as "THICKNESS (m)".
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