INSEA biophysical modelling: data preprocessing

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INSEAbiophysical modelling data
pre-processing
By Juraj Balkovic Rastislav Skalský SSCRI
Bratislava
Workshop at JRC in Ispra, Italy 11th 12th
April, 2005
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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

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1k-based delineation of Homogeneous Response Unit
(HRU)
Depth to rock classes 1 shallow (lt 40 cm) 2
moderate (40-80 cm) 3 deep (80-120 cm) 4 very
deep (gt120 cm)
Elevation classes 1 0-300 m lowland 2
300-600 m upland 3 600-1100 m high mts. 4 gt
1100 m very high mts.
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
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Temporary HRU raster for EU25 126 HRUs
It intersects only elevation, slope for arable
land and textural classes
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HRU raster (1km)
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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)

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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

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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)
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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
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Filters over RESULT- table (how to reduce the
number of HRUs with certain purpose) A. Coding
by schematic ontology codes gt 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 gt according
to SOILIDFR
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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
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LUCAS Phase I. in EPIC BFM
NC Crop Shares
processing
NC Crop shares broken down to altitude classes
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LUCAS Phase I. in EPIC BFM
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Crop Rotation Setup
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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
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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