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Revised dataset of known faults in Italy

This data publication includes a grid composed by contiguous 25 x 25 km square elements covering the Italian area and each parametrized by 1) the maximum length of faults included within the cell, 2) the maximum magnitude from instrumental seismic data, 3) the maximum magnitude from historical seismic data, 4) the maximum magnitude calculated from fault length using empirical scaling laws.This collection represents the basis to a work (Trippetta et al., 2019) aiming to test a fast method comparing the geologic (faults) and the seismologic (historical-instrumental seismicity) information available for a specific region. To do so, (1) a comprehensive catalogue of all known faults and (2) a comprehensive catalogue of earthquakes were compiled by merging the most complete available databases; (3) the related possible maximum magnitudes were derived from fault dimensions, upon the assumption of seismic reactivability of any fault; (4) the calculated magnitudes were compared with earthquake magnitudes recorded in historical and instrumental time series.Faults: to build the dataset of faults for Italy, the following databases were merged: (1) the entire faults collection after the Italian geological maps at the 1:100,000 scale (available online at www.isprambiente.it); (2) the faults compilation from the structural model of Italy at the 1:500,000 scale (Bigi et al., 1989); (3) faults provided in the ITHACA-Italian catalogue of capable faults (Michetti et al., 2000); and (4) the inventory of active faults of the GNDT (Gruppo Nazionale per la Difesa dai Terremoti, Galadini et al., 2000). To improve and implement the database, published complementary studies were selected for some specific areas considered to not be exhaustively covered by the aforementioned collection of faults, including Sardinia, SW Alps, Tuscany, the Adriatic front, Puglia, and the Calabrian Arc. For these areas, faults were selected on the grounds of scientific contributions that documented recent fault activity based on seismic, field, and paleoseismological data. In particular, for the southern Sardinia, the fault pattern proposed by Casula et al. (2001) was used. For the SW Alps, the works of Augliera et al. (1994), Courboulex et al. (1998), Larroque et al. (2001), Christophe et al. (2012), Sue et al. (2007), Capponi et al. (2009), Turino et al. (2009) and Sanchez et al. (2010) were followed. For the Tuscany area, Brogi et al. (2003), Brogi et al. (2005), Brogi (2006), Brogi (2008), Brogi (2011), and Brogi and Fabbrini (2009) were consulted. For the buried northern Apennines and Adriatic front, the fault datasets provided by Scrocca (2006), Cuffaro et al. (2010), and Fantoni and Franciosi (2010) were used. For the Puglia region, data from Patacca and Scandone (2004) and Del Gaudio et al. (2007) were used, while for the Calabrian Arc data were obtained from Polonia et al. (2016).Seismicity: to obtain a complete earthquake catalogue for the Italian territory, the following catalogues of instrumental and historical seismicity were integrated: (1) the CSI1.1 database (http://csi.rm.ingv.it; Castello et al., 2006) for the period 1981–2002, (2) the ISIDe database (http://iside.rm.ingv.it/iside/; IsideWorkingGroup, 2016) for the period 2003–2017 (Figure 3) and the CPTI15 (https://emidius.mi.ingv.it/CPTI15-DBMI15/; Rovida et al., 2016) for the period 1000-1981.The CSI 1.1 database (Castello et al., 2006) is a relocated catalogue of Italian earthquakes during the period 1997–2002. This collection derives from the work of Chiarabba et al. (2005). Most seismic events are lower than 4.0 in magnitude and are mostly located in the upper 12 km of the crust. A few earthquakes exceed magnitude 5.0, and the largest event is Mw 6.0. Due to their poorly constrained location, events with Mw < 2.0 were removed.The ISIDe database (IsideWorkingGroup, 2016) provides the parameters of earthquakes obtained by integrating data from real time and Italian Seismic Bulletin earthquakes. The time-span of this compilation begins in 1985. To avoid an overlap with the CSI database, only the time interval 2003–2017 was considered. Mw = 2.0 is the lower limit used for earthquake magnitude. The CPTI15 database integrates the italian macroseismic database version 2015 (DBMI15, Locati et al., 2016) and instrumental data from 26 different catalogues, databases and regional studies starting from the 1000 up to the 2014. To avoid overlapping of data with the utilized instrumental datasets, from the CPTI2015 we took data for the period 1000-1981 in the range of Mw 4-7.Method: starting from the entire faults dataset, the length of each structure was calculated (Lf, in km). Then, the Italian territory was divided into a grid with square cells of 25 x 25 km. The length of the longest fault crossing each cell characterizes the parameter “fault length” (Lf) of the considered cell. In the second step, these lengths were used as the input parameter to empirically derive the magnitude. The equations provided by Leonard (2010), were applied for earthquake magnitude-fault length relationships to infer the Potential Expected Maximum Magnitude as M = a + b ∗ log (Lf), with a=4.24 and b=1.67. The obtained magnitudes were assigned to each single cell. Furthermore, the maximum magnitude recorded/reported in instrumental/historical catalogs is associated to each containing cell.The resulting datasets are presented in txt format and included in the following files:- Grid_Coordinates.txt (contains ID and coordinates of grid's elements)- Grid_Structure.txt (contains geometry and specifications of the used grid)- Table_results (five columns table containing 1=element ID, 2= element max fault length (Lf_max in km), 3=element max Mw from instrumental record (MwInstr_max), 4=element max Mw from historical record (MwHist_max), 5=element max Mw derived by empirical relationship (PEMM).- The full list of references is included in the file Petricca_2018-003_References.txt

WeCode - Weathering Corrections for denudation rates

Cosmogenic nuclide measurements are commonly biased by weathering within the cosmogenic nuclide production zone. The code package “WeCode” (Weathering Corrections for denudation rates) integrated within the CRONUScalc v2.1 (Marrero et al., 2016) software performs weathering corrections and calculations, as well as offering pixel-by-pixel catchment production rate estimates for alluvial samples. Weathering corrections can be applied for weathering within the regolith or along the regolith-bedrock interface, as is common in carbonate bedrock. The methods for the weathering corrections are described in Ott et al. (2022). Please refer to the README for information on how to use the software. A set of input examples and scripts is provided for illustration. CRONUScalc can be downloaded here https://bitbucket.org/cronusearth/cronus-calc/src/v2.1

3D-CEBS: Three-dimensional lithospheric-scale structural model of the Central European Basin System and adjacent areas

We provide a set of grid files that collectively allow recreating a 3D geological model which covers the Central European Basin System and adjacent areas. The data publication is a complement to the publication of Maystrenko and Scheck-Wenderoth (2013) with a higher spatial and stratigraphic resolution. The structural model consists of (i) 11 sedimentary units including sea water; (ii) five crystalline crust units composed of four upper crustal units and one lower crustal unit; (iii) one lithospheric mantle unit. The available files include information on the regional variation of these geological units in terms of their depth and thickness, both attributes being allocated to regularly spaced grid nodes with horizontal spacing of 4 km. In comparison, the horizontal spacing of data provided by Maystrenko and Scheck-Wenderoth (2013) was 16 km. Besides, the model provided here resolves Permian, Mesozoic and Cenozoic sediments and Permo-Carboniferous volcanics. The model has originally been developed to analyse the first-order structural features characterizing the crust and the lithospheric mantle below the Central European Basin System and adjacent areas and obtain a basis for numerical simulations of heat transport and to calculate the lithospheric-scale conductive thermal field. Such simulations require the subsurface variation of physical rock properties to be defined, the 3D model differentiates units of contrasting materials, i.e. rock types. On that account, a large number of geological and geophysical data have been analysed (see Related Works) and we shortly describe here how they have been integrated into a consistent 3D model (Methods). For further information on the data usage and the characteristics of the units (e.g., lithology, density, thermal properties), the reader is referred to Maystrenko and Scheck-Wenderoth (2013). The contents and structure of the grid files provided herewith are described in the Technical Information section and the associated data description file (pdf).

3D geological model of Berlin - Germany

This dataset provides the grid files which were used to generate the 3d structural model for Berlin, capital city of Germany. It covers a rectangular area around the political boundaries of Berlin. Geologically the region is located in the Northeast German Basin which is in turn part of the Central European Basin System. The data publication is a compliment to the publications Frick et al., (2019) and Haacke et al., (2019) and resolves 23 geological units. These can be separated into eight Cenozoic, eight Mesozoic and three Paleozoic units, the upper and lower crust as well as the lithospheric mantle. We present files which show the regional variation in depth and thickness of all units in the form of regularly spaced grids where the grid spacing is 100 m. This model was created as part of the ongoing project Geothermal potential Berlin which was also partly situated in Energy Systems 2050, both of whom look at the evaluation of the local thermal field and the closely related geothermal potential. These are obtained by simulating fluid- and heatflow in 3d with numerical models built based on the data presented here. These numerical models and simulations rely heavily on a precise representation of the subsurface distribution of rock properties which are in turn linked to the different geological units. Hence, we integrated all available geological and geophysical data (see related work) into a consistent 3D structural model and will describe shortly how this was carried out (Methods). For further information the reader is referred to Frick et al., (2016) and Frick et al., (2019).

3D-CEBS-TTH: transient thermohydraulic model of the Central European Basin System (CEBS)

We provide a single file (exodus II format) that contains all results of the modeling efforts of the associated paper. This encompasses all structural information as well as the pore pressure, temperature, and fluid velocity distribution through time. We also supply all files necessary to rerun the simulation, resulting in the aforementioned output file. The model area covers a rectangular area around the Central European Basin System (Maystrenko et al., 2020). The data publication is compeiment to Frick et al., (2021). The file published here is based on the structural model after Maystrenko et al., (2020) which resolves 16 geological units. More details about the structure and how it was derived can be found in Maystrenko et al., (2020). The file presented contains information on the regional variation of the pore pressure, temperature and fluid velocity of the model area in 3D. This information is presented for 364 time steps starting from 43,000 years before present and ending at 310000 years after present. This model was created as part of the ESM project (Advanced Earth System Modelling Capacity; https://www.esm-project.net). This project looks at the development of a flexible framework for the effective coupling of Earth system model components. In this, we focused on the coupling between atmosphere and the subsurface by simulating the response of glacial loading, in terms of thermal and hydraulic forcing, on the hydrodynamics and thermics of the geological subsurface of Central Europe. For this endeavor, we populated the 3D structural model by Maystrenko and Coauthors (2020) with rock physical properties, applied a set of boundary conditions and simulated the transient 3D thermohydraulics of the subsurface. More details about this can be found in the accompanying paper (Frick et al., 2021)

3D-URG: 3D gravity constrained structural model of the Upper Rhine Graben

We provide a set of grid files that collectively allow recreating a 3D geological model which covers the Upper Rhine Graben and its adjacent tectonic domains, such as portions of the Swiss Alps, the Molasse Basin, the Black Forest and Vosges Mountains, the Rhenish Massif and the Lower Rhine Graben. The data publication is a complement to the publication of Freymark et al. (2017). Accordingly, the provided structural model consists of (i) 14 sedimentary and volcanic units; (ii) a crystalline crust composed of seven upper crustal units and a lower crustal unit; and (iii) two lithospheric mantle units. The files provided here include information on the regional variation of these geological units in terms of their depth and thickness, both attributes being allocated to regularly spaced grid nodes with horizontal spacing of 1 km. The model has originally been developed to obtain a basis for numerical simulations of heat transport, to calculate the lithospheric-scale conductive thermal field and assess the related geothermal potentials, in particular for the Upper Rhine Graben (a region especially well-suited for geothermal energy exploitation). Since such simulations require the subsurface variation of physical rock properties to be defined, the 3D model differentiates units of contrasting materials, i.e. rock types. On that account, a large number of geological and geophysical data have been analysed (see Related Work) and we shortly describe here how they have been integrated into a consistent 3D model (Methods). For further information on the data usage and the characteristics of the units (e.g., lithology, density, thermal properties), the reader is referred to the original article (Freymark et al., 2017). The contents and structure of the grid files provided herewith are described in the Technical Info section.