Das Forschungsprojekt ist Teil einer in groesserem Rahmen erfolgenden systemanalytischen Betrachtung von Wildbacheinzugsgebieten. In diesem Kontext lautet die uebergeordnete Fragestellung: Wie reagieren hydrologische und geomorphologische Prozesse in Wildbacheinzugsgebieten auf Umwelt- und Klimaveraenderungen? Innerhalb des NFP 31 ergibt sich folgendes Konzept: In den fuer langfristige Beobachtungen und Messungen ausgeruesteten Wildbacheinzugsgebieten Rotenbach/Schwarzsee und Erlenbach/Alptal (WSL) sowie im vergleichsweise wesentlich steileren Einzugsgebiet Spissibach/Leissigen (GIUB) werden einerseits wichtige hydrologische und geomorphologische Prozesse in repraesentativen, verschieden ausgestatteten Hang- und Gerinnesequenzen gezielt untersucht und andererseits anhand bestehender Datensaetze kleinere und groessere Hochwasserereignisse mit ihrer Vorgeschichte ausgewertet. Bestehende Modellansaetze werden zur Entwicklung von Teilmodellen zur 'Abflussbildung' und zur 'Feststofflieferung' weitgehend uebernommen und wo noetig angepasst. Diese beiden Teilaspekte werden in der praktischen Durchfuehrung weitgehend gemeinsam und synoptisch bearbeitet. In der Synthese soll ein vorlaeufiges 'Gesamtmodell Wildbach' formuliert werden, das relevante Vorgaenge im Wildbachgeschehen unter heutigen Bedingungen beschreibt. Mit Hilfe dieses Modells und der einzelnen Teilmodelle soll die Sensitivitaet von Teilsystemen und des Gesamtsystems auf Aenderungen des Witterungsablaufs und der Umwelt untersucht werden, wobei auf vorzugebenden Klima- und Umweltszenarien basiert wird.
Extreme sea level events, such as storm surges, pose a threat to coastlines around the globe. Many tide gauges have been measuring sea level and recording these extreme events for decades, some for over a century. The data from these gauges often serve as the basis for evaluating the extreme sea level statistics, which are used to extrapolate sea levels that serve as design values for coastal protection. Hydrodynamic models often have difficulty in correctly reproducing extreme sea levels and, consequently, extreme sea level statistics and trends. In this study, we generate a 13-member hind-cast ensemble for the non-tidal Baltic Sea from 1979 to 2018 using the coastal ocean model GETM (General Estuarine Transport Model). In order to cope with mean biases in maximum water levels in the simulations, we include both simulations with and without wind speed adjustments in the ensemble. We evaluate the uncertainties in the extreme value statistics and recent trends of annual maximum sea levels. Although the ensemble mean shows good agreement with observations regarding return levels and trends, we still find large variability and uncertainty within the ensemble (95% confidence levels up to 60 cm for the 30-year return level). We argue that biases and uncertainties in the atmospheric reanalyses, e.g.\ variability in the representation of storms, translate directly into uncertainty within the ensemble. The translation of the variability of the 99th percentile wind speeds into the sea level elevation is in the order of the variability of the ensemble spread of the modelled maximum sea levels. Our results emphasise that 13 members are insufficient and that regionally large ensembles should be created to minimise uncertainties. This should improve the ability of the models to correctly reproduce the underlying extreme value statistics and thus provide robust estimates of climate change-induced changes in the future.
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 6249 randomly distributed buildings in the urban area of Chía (Colombia). The survey has been carried out between May and July 2020 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (and footprints from OpenStreetMap (OSM), both with vintages of May 2020. The buildings were inspected by dozens of local students of civil engineering students from the Universidad de La Sabana (Chía, Colombia). Their attribute values in terms of the GEM v.2.0 taxonomy.
The Total Exchange Flow analysis framework computes consistent bulk values quantifying the estuarine exchange flow using salinity coordinates since salinity is the main contributor to density in estuaries and the salinity budget is entirely controlled by the exchange flow. For deeper and larger estuaries temperature may contribute equally or even more to the density. That is why we included potential temperature as a second coordinate to the Total Exchange Flow analysis framework which allows gaining insights in the potential temperature-salinity structure of the exchange flow as well as to compute consistent bulk potential temperature and therefore heat exchange values with the ocean. We applied this theory to the exchange flow of the Persian Gulf, a shallow, semi-enclosed marginal sea, where dominant evaporation leads to the formation of hyper-saline and dense Gulf water. This drives an inverse estuarine circulation which is analyzed with special interest on the seasonal cycle of the exchange flow. The exchange flow of the Persian Gulf is numerically simulated with the General Estuarine Transport Model (GETM) from 1993 to 2016 and validated against observations. Results show that a clear seasonal cycle exists with stronger exchange flow rates in the first half of the year. Furthermore, the composition of the outflowing water is investigated using passive tracers which mark different surface waters. The results show that in the first half of the year, most outflowing water comes from the southern coast, while in the second half most water originates from the north-western region.
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
The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code description lon longitude in fraction of degrees lat latitude in fraction of degrees object_id unique id of the building surveyed MAT_TYPE Material Type MAT_TECH Material Technology MAT_PROP Material Property LLRS Type of Lateral Load-Resisting System LLRS_DUCT System Ductility HEIGHT Height YR_BUILT Date of Construction or Retrofit OCCUPY Building Occupancy Class - General OCCUPY_DT Building Occupancy Class - Detail POSITION Building Position within a Block PLAN_SHAPE Shape of the Building Plan STR_IRREG Regular or Irregular STR_IRREG_DT Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE Type of Irregularity NONSTRCEXW Exterior walls ROOF_SHAPE Roof Shape ROOFCOVMAT Roof Covering ROOFSYSMAT Roof System Material ROOFSYSTYP Roof System Type ROOF_CONN Roof Connections FLOOR_MAT Floor Material FLOOR_TYPE Floor System Type FLOOR_CONN Floor Connections. For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Tajikistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra (submitted). The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Tajikistan (provided as a separate file). The model integrates around 1'000 building observations (see related dataset Pittore et al. 2019a). The following specific modelling parameters have been employed: Prior strength=10, 100 Epsilon=0.001 For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
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