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State Office for Agriculture, Environment and Rural Areas of Schleswig Holstein

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    Moving 6-year analysis of Water body chlorophyll-a in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1980-1985 and 2013-2018. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the Correlation length in the horizontal direction were fixed to 80 km and in the vertical direction to 25 m. Signal to noise ratio were fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is mg/m3.

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    Moving 6-year analysis of Water body dissolved oxygen concentration in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1980-1985 and 2013-2018. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the Correlation length in the horizontal direction were fixed to 80 km and in the vertical direction to 25 m. Signal to noise ratio were fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is umol/l.

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    Moving 6-year analysis of Water body phosphate in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1980-1985 and 2013-2018. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the Correlation length in the horizontal direction were fixed to 80 km and in the vertical direction to 25 m. Signal to noise ratio were fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is umol/l.

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    Moving 6-year analysis of Water body silicate in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1980-1985 and 2013-2018. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the Correlation length in the horizontal direction were fixed to 80 km and in the vertical direction to 25 m. Signal to noise ratio were fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is umol/l.

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    Moving 6-year analysis of Water body dissolved inorganic nitrogen (DIN) in the Baltic Sea. Four seasons (March-May, June-August, September-November, December-February). Every year of the time dimension corresponds to a 6-year centred average. Periods span between 1980-1985 and 2013-2018. Analyses for depths (m) (HELCOM standard depths): 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO Grid (30 arc-seconds) topography is used for the contouring preparation. Files contain analysed fields, error fields and combined field with the deepest value for each grid point selected. Also pre-masked fields using relative error threshold 0.3 and 0.5 are included. In the analyses the Correlation length in the horizontal direction were fixed to 80 km and in the vertical direction to 25 m. Signal to noise ratio were fixed to 1.0. Background fields were created using data for the given time period and season. Log transformation was used in the analyses. No detrending, advection constraints or weighting are applied. Unit is umol/l.

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    Moving 6-year analysis and visualization of Water body silicate in the North Sea, four seasons: winter (dec-feb), spring (mar-may), summer (jun-aug), autumn (sep-nov). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.2. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths.

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    Moving 6-year analysis and visualization of Water body phosphate in the North Sea, four seasons: winter (dec-feb), spring (mar-may), summer (jun-aug), autumn (sep-nov). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.2. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths.

  • Categories  

    Moving 6-year analysis and visualization of Water body dissolved oxygen concentration in the North Sea, four seasons: winter (dec-feb), spring (mar-may), summer (jun-aug), autumn (sep-nov). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.2. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths.

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    This visualization product displays the plastic bags abundance of marine macro-litter (> 2.5cm) per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not exactly 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length) Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from MSFD data for all years. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

  • Categories  

    This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach, per 100m & to 1 survey aggregated over the period 2001 to 2020 from Marine Strategy Framework Directive (MSFD) monitoring surveys. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata (total abundance list). This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Normalization of survey lengths to 100m & 1 survey / year: in some cases, the survey length was not exactly 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. Finally, a median is calculated over the entire period among all these total numbers of litter per 100m calculated for each survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. The size of each circle on this map increases with the calculated median number of marine litter per beach, per 100m & to 1 survey. The median litter abundance values displayed in the legend correspond to the 50 and 99 percentiles and the maximum value. More information is available in the attached documents. Warning: - the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area. - This map was created to give an idea of the distribution of beach litter between 2001 and 2021 in a synthetic manner. NOT ALL BEACHES MAY HAVE DATA FOR THE ENTIRE PERIOD, SO IT IS NOT POSSIBLE TO MAKE A COMPARISON BETWEEN BEACHES.