Datensatz

Zusammenfassung

South Wales is characterised by a rich variety of geologic formations and rocks of different ages and periods, and a large asymmetric syncline, as perhaps its most significant structural geological feature, extending from east to west over a length of approximately 96 km and 30 km from north to south, respectively. This oval-shaped syncline is part of the Variscan orogenic thrust and fold belt in Central Europe and covers some 2,700 km2, with coal-bearing rocks from the Upper Carboniferous (Westphalian Stage) deposited in the central syncline and older rocks outcropping in a peripheral belt around it. The coal-bearing sequence begins with Namurian grits and shales, overlain by the more productive Lower, Middle and Upper Coal Measures. A 3D structural geological model has been implemented for the central part of the South Wales Syncline and its bedrock geology. The oldest rocks in the model domain date back to the Pridoli Series from the uppermost Silurian, the youngest to the Westphalian Stage of the Upper Carboniferous. For model implementation, mainly open access data from the British Geological Survey (BGS) has been used. The final 3D structural geological model covers the entire Central South Wales Syncline and is 32.8 km wide and 36.6 km long. In total, the 3D model includes 21 fault zones and the elevation depth of ten surfaces: (1) Top Upper Coal Measures Formation; (2) Top Middle Coal Measures Formation; (3) Top Lower Coal Measures Formation; (4) Top Millstone Grit Group; (5) Top Dinantian Rocks; (6) Top Upper Devonian Rocks; (7) Top Lower Devonian Rocks (sandstone dominated); (8) Top Lower Devonian Rocks (mudstone dominated); (9) Top Pridoli Rocks; (10) Top Ludlow Rocks (in parts). These risk scenarios were independently calculated making use of the DEUS (Damage Exposure Update Service) available in https://github.com/gfzriesgos/deus. The reader can find documentation about this programme in (Brinckmann et al, 2021) where the input files required by DEUS and outputs are comprehensively described. Besides the spatially distributed hazard intensity measures (IM), other inputs required by DEUS to computed the decoupled risk loss estimates comprise: spatially aggregated building exposure models classified in every hazard-dependent scheme. Each class must be accompanied by their respective fragility functions, and financial consequence model (with loss ratios per involved damage state). The collection of inputs is presented in Gomez-Zapata et al. (2021b). The risk estimates are computed for each spatial aggregation areas of the exposure model. For such a purpose, the initial damage state of the buildings is upgraded from undamaged (D0) to any progressive damage state permissible by the fragility functions. The resultant outputs are spatially explicit .JSON files that use the same spatial aggregation boundaries of the initial building exposure models. An aggregated direct financial loss estimate is reported for each cell after every hazard scenario. It is reported one seismic risk loss distribution outcome for each of the 2000 seismic ground motion fields (GMF) per earthquake magnitude (Gomez-Zapata et al., 2021a). Therefore, 1000 seismic risk estimates from uncorrelated GMF are stored in “Clip_Mwi_uncorrelated” and 1000 seismic risk estimates from spatially cross-correlated GMF (using the model proposed by Markhvida et al. (2018)) are stored in “Clip_ Mwi_correlated”. It is worth noting that the prefix “clip” of these folders refers to the fact that, all of the seismic risk estimates were clipped with respect to the geocells were direct tsunami risk losses were obtained. This spatial compatibility in the losses obtained for similar areas and Mw allowed the construction of the boxplots that are presented in Figure 16 in Gomez-Zapata et al., (2021). The reader should note that folder “All_exposure_models_Clip_8.8_uncorrelated_and_correlated” also contains another folder entitled “SARA_entire_Lima_Mw8.8” where the two realisations (with and without correlation model) selected to produce Figure 10 in Gomez-Zapata et al., (2021) are stored. Moreover, the data to produce Figure 9 (boxplots comparing the variability in the seismic risk loss estimates for this specific Mw 8.8, are presented in the following .CSV file: “Lima_Mw_8.8_direct_finantial_loss_distributions_all_spatial_aggregations_Corr_and_NoCorr.csv”. Naturally, 1000 values emulating the 1000 realisations are the values that compose the variability expressed in that figure. Since that is a preliminary study (preprint version), the reader is invited to track the latest version of the actually published (if so) journal paper and check the actual the definitive numeration of the aforementioned figures.