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INSEA biophysical modelling: data pre-processing

INSEA biophysical modelling: data pre-processing. By Juraj Balkovič & Rastislav Skalský SSCRI Bratislava. Workshop at JRC in Ispra, Italy 11 th – 12 th April, 2005. Outlines:. HRU – delineation GIS-based prototype for EPIC soil and topographical inputs LUCAS Phase I. in EPIC BFM

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INSEA biophysical modelling: data pre-processing

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  1. INSEAbiophysical modelling: data pre-processing By Juraj Balkovič & Rastislav Skalský SSCRI Bratislava Workshop at JRC in Ispra, Italy 11th – 12th April, 2005

  2. Outlines: • HRU – delineation • GIS-based prototype for EPIC soiland topographical inputs • LUCAS Phase I. in EPIC BFM • Crop Rotation Set-Up • Topics for discussion

  3. 1k-based delineation of Homogeneous Response Unit (HRU): Elevation classes: 1 – 0-300 m lowland 2 – 300-600 m upland 3 – 600-1100 m high mts. 4 – > 1100 m very high mts. Depth to rock classes: 1 – shallow (< 40 cm) 2 – moderate (40-80 cm) 3 – deep (80-120 cm) 4 – very deep (>120 cm) HRU intersect Texture classes: 1 – coarse 2 – medium 3 – medium fine 4 – fine 5 – very fine 6 – no texture 7 – rock 8 – peat Depth to Gley horizon: 1 – shallow 2 – moderate 3 – deep Climate: ?Annual rainfall Slope classes: Volume of stones: 1 – without 2 – moderate 3 – stony

  4. Temporary HRU raster for EU25: 126 HRUs It intersects only elevation, slope for arable land and textural classes

  5. HRU – raster (1km)

  6. GIS-based prototype for EPIC soil and topographical inputs • Once HRU-layer is set...The prototype is designed • ERDAS IMAGINE (GIS) • VISUAL BASIC (Conversion) • MS ACCESS (Database)

  7. NUTS 2 GIS-based prototype: Subset in batch 1km data • Soil • Topography • Land Use Generates raster subsets for extent of selected NUTS2 regions AOI layer 1km subset data for NUTS2 regions • Soil • Topography • Land Use

  8. 1km subset data for NUTS2 regions LandCat specific Zone statistics (ERDAS IMAGINE Modul) • Soil • Topography • Land Use ASCII outputs Calculated statistics for combinations of NUTS2 and Land Categories from 1k subset rasters (soil and topography)

  9. VISUAL BASIC Script to append ASCII outputs into final table ASCII outputs Calculated statistics for combinations of NUTS2 and Land Categories from 1k subset rasters (soil and topography) MS ACCESS Ontology table

  10. Filters over RESULT- table (how to reduce the number of HRUs with certain purpose): A. Coding by schematic ontology codes > NUTS2_LC_SOILCLASS ALTIT_SLOPE_TEXT e.g. Aggregate by slope for arable Aggregate by altitude CROP ROTATION ALLOCATION Redistribute and aggregate results by simplifying rules B. Filter by minimum-area criterion > according to SOILIDFR

  11. processing LUCAS Phase I. in EPIC BFM • Breaking Down New Cronos Statistics by LUCAS Data Crop Aggregation, Attribute adjustment, Filter for Agricultural Land LUCAS Rough Database LUCAS Pre-processed Downscale by altitude

  12. LUCAS Phase I. in EPIC BFM NC Crop Shares processing NC Crop shares broken down to altitude classes

  13. LUCAS Phase I. in EPIC BFM

  14. Crop Rotation Setup

  15. Crop Rotation Setup Original NC data Crop shares Broken NC data Crop shares CORINE Data Area of arable land + Hetero agric. area Crop rotation systems for NUTS2 region, for its HRUs/ aggregated by altitude classes respectively

  16. Discussion • Digital data • 1km soil data • Coverage of climate for delineation (e.g. annual precipitation 1km from IIASA) • DEM 1km – statistics from 90 x 90 m DEM source (average slope or dominant slope) – for erosion simulations • Consistency of GISCO GIS Database and EUROSTAT Databases in NUTS2 Coding • Fertilization, irrigation and tillage with CAPRI-DYNASPAT

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