INSEA

biophysical modelling: data pre-processing

Workshop at JRC in Ispra, Italy

11th – 12th 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

HRUintersect

Slope classes:

1k-based delineation of Homogeneous Response Unit (HRU):

Texture classes: 1 – coarse2 – medium3 – medium fine4 – fine5 – very fine6 – no texture7 – rock8 – peat

Depth to rock classes:1 – shallow (< 40 cm)2 – moderate (40-80 cm)3 – deep (80-120 cm)4 – very deep (>120 cm)

Depth to Gley horizon:1 – shallow2 – moderate3 – deep

Volume of stones:1 – without2 – moderate3 – stony

Elevation classes: 1 – 0-300 m lowland2 – 300-600 m upland3 – 600-1100 m high mts.4 – > 1100 m very high mts.

Climate:?Annual rainfall

TemporaryHRU 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-basedprototype:

Generates raster subsets for extent of selected NUTS2 regions

• Soil• Topography• Land Use

ASCII outputsCalculated 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 outputsCalculated 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 ROTATIONALLOCATION

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FALLOWTEMP_PASTFLORESVEGETABLESUNFLOWSUGARPULSESPOTATOOIL_RESTRAPEMAIZETOTWINTCERBARLEY_REST

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

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FALLOWTEMP_PASTFLORESVEGETABLESUNFLOWSUGARPULSESPOTATOOIL_RESTRAPEMAIZETOTWINTCERBARLEY_REST

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

iji NPw

where PJ denotes NC share of crops included in i-th crop rotation. Ni is number of crops involved in i-th crop rotation system

Crop Rotation Setup

Original NC dataCrop shares

Broken NC dataCrop shares

Crop rotation systems for NUTS2 region, for its HRUs/ aggregated by altitude classes respectively

CORINE DataArea 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