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Step wise modelling approach. Climate data long and short term. Soil schematisation. Soil physics. Soil temperatures. Water fluxes and moisture contents: long and short term. Carbon long term; static experiment. Nitrogen: short term. CO 2 short term. Climate. - PowerPoint PPT Presentation
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ALTERRA
Step wise modelling approach
Climate data long and short term
Soil schematisation
Water fluxes and moisture contents: long and short term
Soil physics
Carbon long term; static experiment
Nitrogen: short term CO2 short term
Soil temperatures
ALTERRA
Climate Long term and short term experiment Data applied:
Rainfall (not corrected) Long term: ET using Dutch equation using Tair,
Rglobal/HrsSun Short term: PenmanMonteith
Result: long term evaporation excess of 57 mm/year Precip-Evap (mm/year)
-400
-300
-200
-100
0
100
200
300
400
ALTERRA
Soil schematization
3 m soil profile 25 soil layers / horizons 45 model compartments water
flow 26 model compartments solute
flow physical dispersion of 2.5-10
cm
ALTERRA
Hydrology – soil temperature
numerical model to solve soil heat equation
example for plot12a/b
ALTERRA
soil physics
Short term experiment
Different relations theta-h
Calibration:1. Default MVG-set2. Hysteresis3. Reduced theta_sat
ALTERRA
Model exercise on static experiment
Management: Soil tillage Mineral N fertilizer 2 types of organic
manure
Initial partitioning: 90 % native SOM
(stable) 10% humus/biomass
ALTERRA
peculiarities
Meteo: precipitation of short and long term experiment differ (88 mm in 1998)
Soil physical data; same theta gives different heads (what about quality / uncertainty in measurements ?
Nitrate concentration: extremely high in soil solution (625 mg/l NO3-N)
ALTERRA
Conclusions (1)
Long term predictions demand for an appropriate description of slow processes
Partitioning requires long term data sets For long term simulations, generalized data on
land management are sufficient Data of the static experiment are of great value Little influence of soil physical characteristics on
long term carbon dynamics (large on short term N?)
ALTERRA
Partitioning of organic matter pools in the Animo model is important for short term leaching studies: determines mineralization rates Biologigal activity -> denitrification
Animo model could easily be calibrated to data of static experiment
Animo was able to simulate the soil-N contents quite well, but not the soil moisture concentrations
But, it seems there is a discrepancy between soil- nitrogen and soil moisture nitrogen measurements
Conclusions (2)
ALTERRA
On the use of SWAP/ANIMO:
Elaborate more on trace house gas emissions
Short term carbon and nitrogen dynamics requires further analysis, influence of soil physical properties, soil temperature?
Standardize calibration techniques (e.g. GLUE?)
Standardize storage of valuable data sets; include uncertainties
discussion