INSEA biophysical modelling: data pre-processing Workshop at JRC in Ispra, Italy 11 th – 12 th...
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Transcript of INSEA biophysical modelling: data pre-processing Workshop at JRC in Ispra, Italy 11 th – 12 th...
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
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
< 300 300-600 > 600
Altitude
LU
CA
S F
req
uen
cies
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
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
< 300 300-600 > 600
Altitude
LU
CA
S F
req
uen
cies
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