Which salt formations are suitable for storing hydrogen or compressed air? In the InSpEE-DS research project, scientists developed requirements and criteria for the assessment of suitable sites even if their exploration is still at an early stage and there is little knowledge of the salinaries’ structures. Scientists at DEEP.KBB GmbH in Hanover, worked together with their project partners at BGR and the Leibniz University Hanover, Institute for Geotechnics, to develop the planning basis for the site selection and for the construction of storage caverns in flat layered salt and multiple or double saliniferous formations. Such caverns could store renewable energy in the form of hydrogen or compressed air. While the previous project InSpEE was limited to salt formations of great thickness in Northern Germany, salt horizons of different ages have now been examined all over Germany. To estimate the potential, depth contour maps of the top and the base as well as thickness maps of the respective stratigraphic units were developed. Due to the present INSPIRE geological data model, it was necessary, in contrast to the original dataset, to classify the boundary lines of the potential storage areas in the Zechstein base and thickness layers, whereby the classification of these lines was taken from the top Zechstein layer. Consequently, the boundary element Depth criterion 2000 m (Teufe-Kriterium 2000 m) corresponds on each level to the 2000 m depth of Top Zechstein. However, the boundary of national borders and the boundary of the data basis could not be implemented in the data model and are therefore not included in the dataset. Information on compressed air and hydrogen storage potential is given for the identified areas and for the individual federal states. According to the Data Specification on Geology (D2.8.II.4_v3.0) the content of InSpEE-DS (INSPIRE) is stored in 18 INSPIRE-compliant GML files: InSpEE_DS_GeologicUnit_Isopachs_Zechstein.gml contains the Zechstein isopachs. InSpEE_DS_GeologicUnit_Isobaths_Top_Zechstein.gml and InSpEE_DS_GeologicUnit_Isobaths_Basis_Zechstein.gml contain the isobaths of the top and basis of Zechstein. The three files InSpEE_DS_GeologicStructure_ThicknessMap_Zechstein, InSpEE_DS_GeologicStructure_Top_Zechstein and InSpEE_DS_GeologicStructure_Basis_Zechstein represent the faults of the Zechstein body as well as at the top and at the basis of the Zechstein body. InSpEE_DS_GeologicUnit_Boundary_element_Potential_areas_Zechstein.gml contains the boundary elments of the potential areas at the top and the basis of Zechstein as well as of the Zechstein body. The three files InSpEE_DS_GeologicUnit_Uncertainty_areas_ThicknessMap_Zechstein.gml, InSpEE_DS_GeologicUnit_Uncertainty_areas_Top_Zechstein.gml, InSpEE_DS_GeologicUnit_Uncertainty_areas_Basis_Zechstein.gml represent the uncertainty areas of the Zechstein body as well as at the top and at the basis of the Zechstein body. InSpEE_DS_GeologicUnit_Potentially_usable_storage_areas_Storage_potential_in_the_federal_states.gml comprises the areas with storage potential for renewable energy in the form of hydrogen and compressed air. The six files InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Malm.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Keuper.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Muschelkalk.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Roet.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Zechstein.gml and InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Rotliegend.gml represent the salt distribution of the respective stratigraphic unit. InSpEE_DS_GeologicUnit_General_salt_distribution.gml represents the general salt distribution in Germany. This geographic information is product of a BMWi-funded research project "InSpEE-DS" running from the year 2015 to 2019. The acronym stands for "Information system salt: planning basis, selection criteria and estimation of the potential for the construction of salt caverns for the storage of renewable energies (hydrogen and compressed air) - double saline and flat salt layers".

INSPIRE theme Existing Land Use

This dataset clc5 (2015) describes the landscape according to the CORINE Land Cover (CLC) nomenclature. These classes contain mainly information about landcover mixed with some aspects of landuse. CLC5 is based on the more detailed German landcover model from 2015 (LBM-DE2015) which uses separate classes for landcover and landuse and attribute-information about percentage of vegetation and sealing. The mimimum unit for an object is 1 ha. For the CLC5 dataset landcover and landuse classes are combined to unique CLC-classes taking into account the percentage of vegetation and sealing , followed by a generalisation process.

INSPIRE theme Environmental Monitoring Facilities. The data of the Marine Environmental Monitoring Network contain information on water temperature, salinity, level and current at different depths, air temperature, wind speed and direction, air pressure, humidity of the North and Baltic Sea at the stations Darßer Schwelle, Ems, Fehmarn Belt, Nordseeboje lll, Nordseeboje ll, Arkona Basin, German Bight, Fino 1, Kiel, Oder Bank.

The WMS GRSN (INSPIRE) represents the seismological stations of the German Regional Seismic Network (GRSN) equipped with 3-component broadband seismometer and digital data aquisition system. The recorded data are directly transmitted to the data center at BGR in Hannover and made available to the public near realtime. According to the Data Specification on Geology (D2.8.II.4_v3.0, subtopic Geophysics) the information with respect to the seismological stations is INSPIRE-compliant. The WMS GRSN (INSPIRE) contains a layer of the seismological stations (GE.seismologicalStation) displayed correspondingly to the INSPIRE portrayal rules. Via the getFeatureInfo request the user obtains the content of the INSPIRE attributes platformType, relatedNetwork, stationType und stationRank.

INSPIRE theme Geology. Provision of the sediment distribution of the seabed in the North and Baltic Sea.

2m temperature measurements at DWD stations in the Regional Basic Synoptic Network of the WMO, plus additional stations from the so called "Global Dataset" of DWD

This product shows globally the daily snow cover extent (SCE). The snow cover extent is the result of the Global SnowPack processor's interpolation steps and all data gaps have been filled. Snow cover extent is updated daily and processed in near real time (3 days lag). In addition to the near real-time product (NRT_SCE), the entire annual data set is processed again after the end of a calendar year in order to close data gaps etc. and the result is made available as a quality-tested SCE product. There is also a quality layer for each day (SCE_Accuracy), which reflects the quality of the snow determination based on the time interval to the next "cloud-free" day, the time of year and the topographical/geographical location. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to: Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480 Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551 Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432 Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752 Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

wind speed measurements at DWD stations in the Regional Basic Synoptic Network of the WMO, plus additional stations from the so called "Global Dataset" of DWD