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

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

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

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

HRU – raster (1km)

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)

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

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

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

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 Redistribute and aggregate results by simplifying rules B. Filter by minimum-area criterion > according to SOILIDFR Aggregate by altitude CROP ROTATION ALLOCATION

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

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

LUCAS Phase I. in EPIC BFM

Crop Rotation Setup

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

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