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Lookup table of the BN-FLEMO∆: A Bayesian Network-based Flood Loss Estimation Model for Ho Chi Minh City, Vietnam

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”.

Input files for the 2-D cases of the ASPECT models of a mantle plume and csv files for all output timesteps in Tables B1 and B2

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)

3D-SCS: Three-dimensional lithospheric-scale structural and density model of the South China Sea

We present a comprehensive 3D lithospheric-scale model of the South China Sea region (SCS), which reveals the structural configuration of the area. This model delineates seven distinct geological units: (1) seawater, (2) sedimentary cover, (3) continental crystalline crust, (4) oceanic crust, (5) upper lithospheric mantle, (6) lower lithospheric mantle, and (7) sub-lithospheric mantle. The model covers an area of 960 km × 1260 km and reach down to a depth of 250 km. It is provided as uniformly spaced grids with 10 km intervals for each unit. The geometries and density distributions within the crust have been compiled and interpolated from a variety of datasets, predominantly seismic data (see section 6). To eliminate boundary effects, the model boundaries have been extended by more than 500 km in all horizontal directions, incorporating additional constraining data from the extended region. Additionally, we provide gridded gravity field data, a density voxel cube for the sub-lithospheric mantle, and relevant tomography data. Notably, the density of the lower lithospheric mantle was derived from 3D gravity inversion modeling.

A database of centrifuge analogue models testing the influence of inherited brittle fabrics on continental rifting

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).

ASPECT models of a mantle plume under West Antarctica

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

3D-NEA: Three-dimensional lithospheric-scale structural model of the North East Atlantic

The Northeast Atlantic (NEA) region has long been a subject of interest due to its complex geological history, particularly regarding the interaction between the Iceland plume and the lithospheric plates. In this data publication, we present a comprehensive three-dimensional structural and density model of the NEA crust and uppermost mantle, consolidating and integrating a wide range of previously fragmented data sets. Our model highlights the influence of the Iceland plume on the region's geological evolution, shedding light on the mechanisms that facilitated the continental breakup between Europe and Laurentia during the earliest Eocene period. The whole workflow and methods are described in Gomez Dacal et al. (2023) and its Supplementary Information.

Dispersion curves, phase velocity maps and shear-wave velocity model for Scandinavia based on teleseismic Rayleigh surface waves and ambient noise

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.

STEIN - Stochastic erosion in-situ cosmogenic nuclide model

The stochastic erosion in-situ cosmogenic nuclide model is a 1D numerical model that simulates the evolution of the concentrations of in situ-produced Be-10, C-14, and He-3 alongside the bedrock thermal field in the shallow Earth surface. It is useful for evaluating cosmogenic nuclide data derived from field samples, in order to determine the erosion rate, erosion style, as well as the time-integrated bedrock thermal history. The model simulates erosion in four styles: no erosion, uniform (steady-state) erosion, episodic erosion, and stochastic erosion. It is particularly useful for evaluating the time-temperature evolution of bedrock hillslopes in mountainous regions.

Risk Estimates from Process-based Regional Flood Model for Germany

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).

Evaluation of mineral precipitation by geochemical modeling at the Ketzin CO2 storage site, Germany

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).

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