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Die Katastrophe im Kernkraftwerk Fukushima nach dem Seebeben vom 11. März 2011 : Beschreibung und Bewertung von Ablauf und Ursachen

Der vorliegende Bericht setzt sich ausführlich mit dem TŌHOKU-CHIHOU-TAIHEIYOU-OKI Erdbeben vom 11. März 2011 und dem dadurch ausgelösten Unfallgeschehen im Kernkraftwerk Fukushima Dai-ichi auseinander. Er beschäftigt sich auf der Grundlage des Berichts der japanischen Regierung an die Internationale Atomenergieagentur (IAEA) sowie einer Vielzahl weiterer Quellen ausführlich mit den Unfallabläufen, den Freisetzungen radioaktiver Stoffe in die Umgebung, der sicherheitstechnischen Auslegung der Anlage und den Maßnahmen zur langfristigen Eingrenzung der Unfallfolgen. Ergänzend wird auf die Auswirkungen der Freisetzungen für die Umgebung der Anlage sowie auf Aspekte des Sicherheitsmanagements und der Sicherheitskultur eingegangen. Der Bericht gibt erste Antworten auf die Fragen, warum es nach dem Seebeben und dem dadurch ausgelösten Tsunami zu der Katastrophe im Kernkraftwerk Fukushima Dai-ichi gekommen ist, wie die Abläufe bis zu den Kernschmelzen und den Zerstörungen der Blöcke 1 - 4 zu erklären sind und was dabei noch nicht abschließend geklärt werden kann, welche Schwächen und Fehler in der Auslegung der Anlage und im regulatorischen System dazu wesentlich beigetragen haben und was zu den Freisetzungen radioaktiver Stoffe in die Atmosphäre und ins Meer gesagt werden kann. // ABSTRACT // This report discusses the TŌHOKU-CHIHOU-TAIHEIYOU-OKI earthquake of March 11, 2011 and the resulting nuclear accident in the Fukushima Dai-ichi nuclear power station. Based on the report of the Japanese government to the International Atomic Energy Agency (IAEA) and on numerous additional sources it examines in considerable detail the accident progression, the emission of radioactive material to the environment, the technical design basis of the plants and the measures taken to mitigate the consequences of the accident. In addition it covers the radiological consequences for the vicinity of the station and aspects of safety management and safety culture. The report provides answers as to why the nuclear catastrophe following the earthquake and ensuing tsunami in the Fukushima Dai-ichi nuclear power station could occur, how the accident progression to core melting and destructions in units 1 - 4 can be explained and what cannot be explained yet, which weaknesses and failures in the design of the plant and within the regulatory system contributed significantly to the accident and which information can be provided on the emission of radioactive material to the atmosphere and to the ocean.

ERA5-Land weekly: Surface temperature, weekly time series for Europe at 1 km resolution (2016 - 2020)

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Surface temperature: Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis (starting from Saturday) for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of surface temperature. File naming: Average of daily average: era5_land_ts_avg_weekly_YYYY_MM_DD.tif Max of daily max: era5_land_ts_max_weekly_YYYY_MM_DD.tif Min of daily min: era5_land_ts_min_weekly_YYYY_MM_DD.tif The date in the file name determines the start day of the week (Saturday). Pixel values: °C * 10 Example: Value 302 = 30.2 °C The QML or SLD style files can be used for visualization of the temperature layers. Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 82N south: 18S west: -32W east: 61E Spatial resolution: 1 km Temporal resolution: weekly Time period: 01/01/2016 - 12/31/2020 Format: GeoTIFF Representation type: Grid Software used: GRASS 8.0 Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Air temperature (2 m): Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m). File naming: Average of daily average: era5_land_t2m_avg_weekly_YYYY_MM_DD.tif Max of daily max: era5_land_t2m_max_weekly_YYYY_MM_DD.tif Min of daily min: era5_land_t2m_min_weekly_YYYY_MM_DD.tif The date in the file name determines the start day of the week (Saturday). Pixel value: °C * 10 Example: Value 44 = 4.4 °C The QML or SLD style files can be used for visualization of the temperature layers. Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 1km Temporal resolution: weekly Time period: 01/01/2016 - 12/31/2020 Format: GeoTIFF Representation type: Grid Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Lineage: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used. Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Other resources: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, info@mundialis.de Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.