Abstract. 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 lower-magnitude 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.
Abstract. This study generates a process in GEE (Google Earth Engine) for SUHI (Surface Urban Heat islands) identification derived from TIRS (Thermal Infrared Sensor) and OLI (Operational Land Imager) sensors of Landsat 8 imagery in the area of Stuttgart, Germany. By comparing the temperature images in winter and summer seasons through a regression model, a relation between the Surface Cover (SC), the Terrain Shape (DEM) and the LST (Land Surface Temperature) is established. A Python code is developed for modelling the data and displaying the results linked to GEE. Three different models are used to establish the relationship between different variables (Temperature, Height, Wind etc.). Accuracy/goodness of fit of these models are measured using R-squared and standard error. Results shows that polynomial regression of 3rd order degree fits best to the dataset used in this study. Moreover, it is found that temperature values are not perfect for this study, as Landsat 8 have been acquired at 10’o clock in the morning (local time), whereas night time acquisition (which was not available for Stuttgart, Germany) would be best suited for the study. The results indicate that urban areas and meadow (open areas without vegetation) get the bigger values of temperature. Terrain Shape with respect to height indicates that the bigger the height, the lower the temperature in most of the regions. This project provides insight into the development of applications using a web-based platform and leads to a fast and accurate result for identifying the SUHI effect. It can contribute to the necessity of planning more vegetation areas in order to reduce hot temperature values in Stuttgart.
<p>Residential building exposure models for risk and loss estimations related to natural hazards are usually defined in terms of specific&#160;schemas&#160;describing mutually exclusive, collectively exhaustive (MECE) classes of buildings. These models are&#160;derived from: (1) the analysis of census data or (2) by means of individual observations in the field. In the first case, expert elicitation has been conventionally used to classify the building inventory into particular schemas, usually aggregated over geographical administrative units whose size area and shape are country-specific. In the second case, especially for large urban areas, performing a visual inspection of every building in order to assign a class according to the specific schema used is a highly time- and resource intensive task, often simply unfeasible.</p> <p>Remote sensing data based on the analysis of satellite imagery has proved successful in integrating large-scale information on the built environment and as such can provide valuable vulnerability-related information, although often lacking the level of spatial and thematic resolution requested by multi-hazard applications. Volunteered Geo Information (VGI) data can also prove useful in this context, although in most cases only geometric attributes (shape of the building footprint) and some occupancy information are recorded thus leaving out most of the building attributes controlling the vulnerability of the structures to the different hazards. An additional drawback of VGI is the incompleteness of the information, which is based on the unstructured efforts of voluntary mappers.</p> <p>Former efforts have been proposing a top-down/bottom-up approach moving from regional scale to neighbourhood and per-building scale, based on the analysis and integration of different data sources at increasing spatial resolutions and thematic detail. Following the same principle, this work focuses on the downscaling of already existing building exposure models based on census data making use of a probabilistic approach based on Bayesian updating. Different aggregation models can be taken into account to increase the spatial resolution of the building exposure model, also including variable-resolution models based on geostatistical approaches. Land-use masks are first generated after&#160;a supervised classification&#160;of&#160;Sentinel-2 images, in order&#160;to better relate the built- up area to meaningful geographical entities. Two independent statistical models are then created based on prior input information. Maximum likelihood estimations are obtained for each model. Two types of auxiliary data have been employed in order to constrain the downscaling via a specific likelihood term in the Bayesian updating: 1)&#160;building footprints area from the&#160;open-source-volunteered geo-information&#160;OpenStreetMaps&#160;&#160;and 2) built-up height and density estimators based on remote sensing developed by&#160;the DLR (the German Aerospace Agency).</p> <p>This approach, developed within the scope of the RIESGOS,&#160;was tested in Valparaiso and Vi&#241;a del Mar (Chile) where the residential building exposure model proposed by the GEM-SARA project has been downscaled. The performance of the different&#160;auxiliary data were separately tested and compared. An independent building survey has also been carried out by&#160;experts from CIGIDEN (Chile) using a Rapid Remote Visual Screening Survey and used for preliminary validation of the approach.</p>
Abstract. We propose the use of variable resolution boundaries based on Central Voronoi Tessellations (CVT) 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 present intensity measures with contrasting footprints and spatial correlations such as in coastal environments. This proposal avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) are considered as representative within large sized geocells 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 observe that for the portfolio located in the coastal area exposed to both perils in Lima, the ground-shaking dominates the losses for lower magnitudes whilst the tsunami does for the larger ones. For the latter, two sets of existing empirical flow-depth fragility models are used, finding large differences in the losses. This study arises awareness about the uncertainties in the selection of fragility models and aggregations entities for exposure modelling and loss mapping.
Efforts have been made in the past to enhance building exposure models on a regional scale with increasing spatial resolutions by integrating different data sources. This work follows a similar path and focuses on the downscaling of the existing SARA exposure model that was proposed for the residential building stock of the communes of Valparaíso and Viña del Mar (Chile). Although this model allowed great progress in harmonising building classes and characterising their differential physical vulnerabilities, it is now outdated, and in any case, it is spatially aggregated over large administrative units. Hence, to more accurately consider the impact of future earthquakes on these cities, it is necessary to employ more reliable exposure models. For such a purpose, we propose updating this existing model through a Bayesian approach by integrating ancillary data that has been made increasingly available from Volunteering Geo-Information (VGI) activities. Its spatial representation is also optimised in higher resolution aggregation units that avoid the inconvenience of having incomplete building-by-building footprints. A worst-case earthquake scenario is presented to calculate direct economic losses and highlight the degree of uncertainty imposed by exposure models in comparison with other parameters used to generate the seismic ground motions within a sensitivity analysis. This example study shows the great potential of using increasingly available VGI to update worldwide building exposure models as well as its importance in scenario-based seismic risk assessment.
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