ALBERT

Description: This repository contains spatially distributed ground motion fields (GMF) for six determinist subduction earthquake scenarios for Metropolitan Lima and Callao (Peru). They have moment magnitudes between Mw 8.5 to 9.0 and emulate the historical earthquake that occurred in 1746 and caused extensive damage to that area. 1000 ground motion realisations in .XML format are generated using a single ground motion prediction equation per earthquake rupture with uncorrelated and cross-correlated residuals.

Description: This data publication is composed by two main folders: (1) “Top-down_exposure_modelling_Lima” and (2) “Vulnerability_models_Lima/”. The first one contains a complete collection of data models used to represent the residential building portfolio of Lima and Callao (Peru) using a top-down approach (census-based desktop study). Therein, the reader can find a comprehensive description of the procedure of how the exposure models were constructed. This includes python scripts and postprocessed geodatasets to represent these building stock into predefined and separate classes for earthquake and tsunami physical vulnerabilities. The second folder contains sets of fragility functions for these building classes and the assumed economic consequence model. These models are suplement material of a submitted paper (Gomez-Zapata et al., 2021b). Please note it is an unpublished preprint version at the time of writing this document. The reader is strongly advised to look for the definitive version once (if so) it is accepted and published.

Description: This data repository contains the spatial distribution of the direct financial loss computed expected for the residential building stock of Metropolitan Lima (Peru) after the occurrence of six decoupled earthquake and tsunami risk scenarios (Gomez-Zapata et al., 2021a; Harig and Rakowsky, 2021). 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.

Description: The software component DEUS (Damage-Exposure-Update-Service) is a Python3 script to evaluate/ update the physical damage and the structural vulnerability of a given building stock classified in terms of hazard-dependent classes (i.e. exposure model). This is obtained by estimating the damage evolution of the building stock given their initial damage state; the location of the scenario-based IM; and the use of selected fragility functions that must be compatible with the predefined building classes and IM. It can be run locally on your computer as well as a WPS (Web Processing Service). This version can handle single or consecutive deterministic hazard scenarios with spatially distributed Intensity Measures (IM). For single hazard scenarios, the process requires a single execution. In the case of consecutive deterministic hazard scenarios, the executions are proportional to the number of consecutive risk scenario (events) of interest.

Description: This data publication is composed by two main folders: (1) “Focus_map_construction” and (2) “CVT_models”. The first one contains the individual raster inputs (tsunami inundation and population distribution) that are combined to construct two different focus maps for the cities of Lima and Callao (Peru). The reader can find a more complete description about the focus map concept in Pittore (2015). These raster focus maps are used as inputs to generate variable-resolution CVT (Central Voronoi Tessellation) geocells following the method presented in Pittore et al., (2020). They are vector-based data (ESRI shapefiles) that are stored in the second folder. These resultant CVT-geocells are used by Gomez-Zapata et al., (2021) as spatial aggregation boundaries to represent the residential building portfolio for the cities of Lima and Callao (Peru).

Description: In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaíso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that building class reconnaissance, either from prior assumptions by desktop studies with aggregated data (top–down approach), or from building-by-building data collection (bottom–up approach), plays a fundamental role in the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches.

Description: We propose the use of variable resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models for risk assessment to various natural hazards. Such a framework is especially beneficial when the spatial distribution of the considered hazards presents intensity measures with contrasting footprints and spatial correlations, such as in coastal environments. This work avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) can be considered to be representative within large-sized geo-cells for physical vulnerability assessment, without, at the same time, increasing the complexity of the overall model. We present decoupled earthquake and tsunami scenario-based risk estimates for the residential building stock of Lima (Peru). We observe that earthquake loss models for far-field subduction sources are practically insensitive to the exposure resolution. Conversely, tsunami loss models and associated uncertainties depend on the spatial correlations of the hazard intensities as well as on the resolution of the exposure models. We note that for the portfolio located in the coastal area exposed to both perils in Lima, the ground shaking dominates the losses for lowermagnitude earthquakes, whilst tsunamis cause the most damage for larger-magnitude events. For the latter, two sets of existing empirical flow depth fragility models are used, resulting in large differences in the calculated losses. This study, therefore, raises awareness about the uncertainties associated with the selection of fragility models and spatial aggregation entities for exposure modelling and loss mapping.

Description: The integration of site effects into Probabilistic Seismic Hazard Assessment (PSHA) is still an open issue within the seismic hazard community. Several approaches have been proposed varying from deterministic to fully probabilistic, through hybrid (probabilistic-deterministic) approaches. The present study compares the hazard curves that have been obtained for a thick, soft non-linear site with two different fully probabilistic, site-specific seismic hazard methods: (1) The analytical approximation of the full convolution method (AM) proposed by Bazzurro and Cornell 2004a,b and (2) what we call the Full Probabilistic Stochastic Method (SM). The AM computes the site-specific hazard curve on soil, HC(Ss a( f )), by convolving for each oscillator frequency the bedrock hazard curve, HC(Sr a( f )), with a simplified representation of the probability distribution of the amplification function, AF( f ), at the considered site The SM hazard curve is built from stochastic time histories on soil or rock corresponding to a representative, long enough synthetic catalog of seismic events. This comparison is performed for the example case of the Euroseistest site near Thessaloniki (Greece). For this purpose, we generate a long synthetic earthquake catalog, we calculate synthetic time histories on rock with the stochastic point source approach, and then scale them using an adhoc frequency-dependent correction factor to fit the specific rock target hazard. We then propagate the rock stochastic time histories, from depth to surface using two different one-dimensional (1D) numerical site response analyses, while using an equivalent-linear (EL) and a non-linear (NL) code to account for code-to-code variability. Lastly, we compute the probability distribution of the non-linear site amplification function, AF( f ), for both site response analyses, and derive the site-specific hazard curve with both AM and SM methods, to account for method-to-method variability. The code-to-code variability (EL and NL) is found to be significant, providing a much larger contribution to the uncertainty in hazard estimates, than the method-to-method variability: AM and SM results are found comparable whenever simultaneously applicable. However, the AM method is also shown to exhibit severe limitations in the case of strong non-linearity, leading to ground motion “saturation”, so that finally the SM method is to be preferred, despite its much higher computational price. Finally, we encourage the use of ground-motion simulations to integrate site effects into PSHA, since models with different levels of complexity can be included (e.g., point source, extended source, 1D, two-dimensional (2D), and three-dimensional (3D) site response analysis, kappa effect, hard rock . . . ), and the corresponding variability of the site response can be quantified.