Technical guidance for grid based provision of data for MSFD reporting

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Presentation transcript:

Technical guidance for grid based provision of data for MSFD reporting Dr. Lidija Globevnik Dunja Vrenko Zupan Institute for Water of the Republic of Slovenia TC VODE - Thematic center for Water (ETC ICM Water) Event/ date: Copenhagen, MFSD - WG DIKE; 2.7.2012 1

Purpose of the document Guidance document to support reporting of MFSD spatial data Presentation of basic concepts and definitions of grid based spatial data as developed and used in Europe Description of methods for data manipulation

MSFD reporting in 2012 Reporting under Articles 8,9,10 Data related to MarineUnitID/Region/SubRegion/SubDivision/AssessmentArea Data connected to geographical Assessment Area - different feature classes: Point, Line, Polygon (irregular, regular: grid cells) Most used grid cells are square cells

Example of national data - Land use 1 KM GRID_ETRS89-LAEA

Data handling methodology Aggregation is the process of grouping spatial data at a level of detail or resolution that is coarser than the level at which the data were collected. Disaggregation is the breakdown of observations, usually within a common branch of a hierarchy, to a more detailed level to that at which detailed observations are taken. Spatial disaggregation or downscaling is the process by which information at a coarse spatial scale is translated to finer scales while maintaining consistency with the original dataset.

Reporting based on grid system Advantages Easy use in spatial analysis Usage of square grid cells Grid-based “statistics” is independent of administrative borders Disadvantages Problems with harmonization and integration of national and European grid systems – direct transformation not possible Re-projecting national grids into an European projection system may distort the formerly square grid cells MSs as a data producer should have the source data (original data) in a format different from grid. For grid-based purposes the developed methodology for creating grid based data should be prepared. When Member States use grid system in national coordinate system and prepare data for European level in European grid system the pre-prepared system for both types of coordinate systems will manage to do the appropriate output.

Aggregation methods for polygon and point data For transformation from small cells to large cells in a different grid system or to polygons (geographic units), simple methods, such as adding small cells with the centre inside the new cell or polygon, or simple areal weighting (allocation proportional to area) are acceptable. POLYGON DATA MAXIMUM AREA CRITERIA (uncountable variables) PROPORTIONAL CALCULATION PROPORTIONAL AND WEIGHTED CALCULATION (countable variables) POINT DATA AVERAGE OF ORIGINAL VALUE MEDIAN ORIGINAL VALUE

Disaggregation methods for polygon data Whenever it is possible PROPORTIONAL AND WEIGHTED CALCULATION should be used to disaggregate the data, as it gives an added value to the source data, providing more interesting results when different data are put together on a cell-by-cell basis. Other methods: SIMPLE AREA WEIGHTING (should be avoided) MASK AREA WEIGHTING DASYMETRIC DISAGGREGATION STOCHASTIC ALLOCATION

PROPORTIONAL AND WEIGHTED CALCULATION Cell value = Wc Σ ( Vi * Sharei ) Where: Vi = Value of unit i Sharei = Share of unit i within the cell Wc = weight assigned to cell c In the example: Wc * (V1 * 0.85 + V2 * 0.15) the cell takes also a proportionally calculated value, but this value is weighted for each cell, according to an external variable (e.g. population) Proposed for countable variables

MAXIMUM AREA CRITERIA Cell takes the value of the unit which covers most of the cell area Proposed for uncountable variables

Examples of national data & considerations 1 km grid_ETRS89-LAEA Examples of national data & considerations

Grid design from polygon and point data (number of inhabitants) count intersect V1 V2 V3

Protected areas – points/polygons

Habitat status – midlittoral and infralittoral (5 classes)

Aggregation methods for point data: Three types of distribution of point features: random, uniform and clustered. Median of original value is the middle occuring value of all points selected to be aggregated. Average of original value is the average value of all the points selected to be aggregated. In case of grid cells means all values of points on the selected grid cell.

Status in regard to Chlorophyll a (5 classes)