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  • Data  (14)
  • RIESGOS
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  • Data  (14)
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  • 1
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    Customised focus maps and resultant CVT-based aggregation entities for Lima and Callao (Peru) (2021)
    GFZ Data Services
    Details
    Publication Date: 2021-10-07
    Description: Abstract
    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).
    Keywords: spatial aggregation areas ; CVT ; Central Voronoi Tessalations ; focus map ; geocells ; raster ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
  • 2
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    Scripts to generate (1) attribute-based fuzzy scores for SARA and HAZUS building classes, and (2) probabilistic inter-scheme compatibility matrices. An application on the residential building stock of Valparaiso (Chile) for seismic risk applications (2021)
    GFZ Data Services
    Details
    Publication Date: 2021-10-26
    Description: Abstract
    Description: This repository is composed of two main folders: (1) “Exposure_fuzzy_scores” and (2) “Inter-scheme_mapping”. The first one contains an ipython notebook with a complete description of two earthquake building schemes: SARA and HAZUS in terms of faceted attributes contained in the GEM V.2.0 taxonomy. Both schemes have already been proposed for exposure modelling at the third administrative division “commune” in Chile in earlier works. They are inputs for the use of a Python script (contained in the second folder) to calculate an inter-scheme compatibility matrix, that uses SARA as the source and HAZUS as the target schemes. These models and data are supplement material to Gomez-Zapata et al. (2021).
    Description: Other
    Description: Licence Statement: Data: Creative Commons Attribution 4.0 International License (CC BY 4.0) Code: Apache License, Version 2.0 (January 2004) Copyright © 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: exposure modelling ; building schemes ; compatibility matrix ; faceted taxonomy ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region
    Type: Software , Software
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    DATA GFZ
  • 3
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    Spatial representation of direct loss estimates on the residential building stock of Lima (Peru) from decoupled earthquake and tsunami scenarios on variable resolutions exposure models (2021)
    GFZ Data Services
    Details
    Publication Date: 2022-02-15
    Description: Abstract
    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.
    Keywords: tsunami risk ; earthquake risk ; risk scenario ; physical vulnerability ; loss ; deterministic risk ; fragility function ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS ; EARTH SCIENCE SERVICES 〉 HAZARDS MANAGEMENT
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
  • 4
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    RRVS Building survey for building exposure modelling in Valparaiso and Viña del Mar (Chile) (2021)
    GFZ Data Services
    Details
    Publication Date: 2022-11-23
    Description: Abstract
    Description: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 604 randomly distributed buildings in the urban area of Valparaiso and Viña del Mar (Chile). The survey has been carried out between November and December 2018 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: December 2018) and footprints from OpenStreetMap (OSM). The buildings were inspected by local structural engineers from the Chilean Research Centre for Integrated Disaster Risk Management (CIGIDEN) while collecting their attribute values in terms of the GEM v.2.0 taxonomy
    Keywords: taxonomy ; RRVS ; GEM ; risk exposure ; attributes ; survey ; Valparaiso ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
  • 5
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    Residential building exposure and physical vulnerability models for ground-shaking and tsunami risk in Lima and Callao (Peru) (2021)
    GFZ Data Services
    Details
    Publication Date: 2022-11-23
    Description: Abstract
    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.
    Keywords: exposure modelling ; physical vulnerability ; consequence model ; fragility function ; earthquake vulnerability ; tsnami vulnerability ; occupancy types ; residential building ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
  • 6
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    Creation of simplified state-dependent fragility functions through ad-hoc scaling factors to account for previous damage in a multi-hazard risk context. An application to flow-depth-based analytical tsunami fragility functions for the Pacific coast of South America (2022)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    Description: This data repository contains a brief description of the building classification scheme for physical vulnerability to tsunamis and corresponding fragility functions originally proposed by Medina, 2019. These fragility functions are used as input to construct their associated state-dependent fragility functions using scaling factors, which were obtained as ad-hoc calibration parameters. A Python script to produce a file with such a model is provided along with the needed inputs and resulting output files.
    Description: Other
    Description: In recent decades, the risk to society due to natural hazards has increased globally. To counteract this trend, effective risk management is necessary, for which reliable information is essential. Most existing natural hazard and risk information systems address only single components of a complex risk assessment chain, such as, for instance, focusing on specific hazards or simple loss measures. Complex interactions, such as cascading effects, are typically not considered, as well as many of the underlying sources of uncertainty. This can lead to inadequate or even miss-leading risk management strategies, thus hindering efficient prevention and mitigation measures, and ultimately undermining the resilience of societies. Therefore, experts from different disciplines work together in the joint project RIESGOS 2.0 (Scenario-based multi-risk assessment in the Andes region) and develop innovative scientific methods for the evaluation of complex multi-risk situations with the aim to transfer the results as web services into a demonstrator for a multi-risk information system.
    Keywords: fragility function ; tsunami vulnerability ; multi-hazard ; attributes ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
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    DATA GFZ
  • 7
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    Tsunami flow depth in Lima/Callao (Peru) caused by six hypothetical simplified tsunami scenarios offshore Lima (2021)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    Description: This data collection contains six inundation maps in Lima and Callao (Peru) based on tsunami simulations with the wave propagation and run-up model TsunAWI (see Rakowsky et al. 2015). The simulations were carried out in the framework of the RIESGOS project (see riesgos.de). The sources are hypothetical earthquake events in the magnitude range Mw 8.5 to Mw 9.0 offshore Lima. The source area of the events is based on the historical event from October 1746, the parameters are derived from the study Jimenez et al. (2013). The sources are considerably simplified since we aim at a systematic investigation of the tsunami impact and restrict the parameter variation between scenarios to one parameter only, the slip value. The source area is split into five subfaults, however we use a constant slip distribution. The corresponding tsunami simulations are carried out in a triangular mesh with resolution ranging from 7km in the deep ocean to a finest value of about 7m in the coastal land part of the pilot area Lima/Callao. The flow depth distribution in Lima/Callao obtained from the simulation is interpolated to a raster file and provided as Golden Software Binary Grids. The numerical results are obtained from simulations with the finite element model TsunAWI (Rakowsky et al. 2015). The mesh resolution in the pilot area Lima/Callao is approximately 20m, the smallest edge length is about 7m. The main model parameters are listed in Table 1. Concerning the bottom roughness, we use a constant Manning coefficient of 0.02 in all of the model domain.
    Keywords: tsnami vulnerability ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
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    DATA GFZ
  • 8
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    Seismic ground motion fields for six deterministic earthquake scenarios (Mw 8.5-9.0) for Lima (Peru) (2021)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    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.
    Keywords: RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; ground motion ; seismic ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
  • 9
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    Quakeledger: a web service to serve earthquake scenarios (2021)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    Description: This version of Quakeledger (V.1.0) is a Python3 program that can also be used as a WPS (Web Processing Service). It returns the available earthquake events contained within a given local database (so called catalogue) that must be customised beforehand (e.g. historical, expert and/or stochastic events). This is a rewrite from: https://github.com/GFZ-Centre-for-Early-Warning/quakeledger and https://github.com/bpross-52n/quakeledger. In these original codes, an earthquake catalogue had to be initially provided in .CSV format. The main difference with this version is that, this code is refactored and uses a SQLITE database. The user can find the parser code in: “quakeledger/assistance/import_csv_in_sqlite.py”
    Description: Other
    Description: License: BSD 3-Clause Copyright © 2021 Early Warning and Impact Assessment Group at Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Quakeledger is free software: you can redistribute it and/or modify it under the terms of the BSD 3-Clause License. Quakeledger is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the BSD 3-Clause License for more details. You should have received a copy of the BSD 3-Clause License along with this program. If not, see 〈https://opensource.org/licenses〉
    Keywords: Earthquake catalogue ; provider ; script ; python ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING ; EARTH SCIENCE SERVICES 〉 WEB SERVICES ; EARTH SCIENCE SERVICES 〉 WEB SERVICES 〉 DATA PROCESSING SERVICES
    Type: Software , Software
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    DATA GFZ
  • 10
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    Probabilistic inter-scheme compatibility matrices for buildings. An application using existing vulnerability models for earthquakes and tsunami from synthetic datasets constructed using the AeDEs form through expert-based heuristics (2022)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    Description: This folder contains the scripts, input and output files required to calculate the inter-scheme conversion matrices for building types and the implicit damage states of their respective fragility models for two selected vulnerability schemes: one for earthquakes and the other for tsunamis. They were used in previous studies to characterize the residential building stock of Lima. The outcomes generated in this data repository are valuable inputs to then calculate the disaggregated and cumulative damage and losses expected for cascading hazard scenarios.
    Description: Other
    Description: In recent decades, the risk to society due to natural hazards has increased globally. To counteract this trend, effective risk management is necessary, for which reliable information is essential. Most existing natural hazard and risk information systems address only single components of a complex risk assessment chain, such as, for instance, focusing on specific hazards or simple loss measures. Complex interactions, such as cascading effects, are typically not considered, as well as many of the underlying sources of uncertainty. This can lead to inadequate or even miss-leading risk management strategies, thus hindering efficient prevention and mitigation measures, and ultimately undermining the resilience of societies. Therefore, experts from different disciplines work together in the joint project RIESGOS 2.0 (Scenario-based multi-risk assessment in the Andes region) and develop innovative scientific methods for the evaluation of complex multi-risk situations with the aim to transfer the results as web services into a demonstrator for a multi-risk information system.
    Keywords: machine learning ; vulnerability ; multi-hazard ; earthquake fragility ; tsunami fragility ; cumulative damage ; Bayesian approach ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
    Location Call Number Expected Availability
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    DATA GFZ
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