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Step wise modelling approach

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.

irma-brennan
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Step wise modelling approach

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  1. 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 CO2 short term

  2. 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

  3. 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

  4. Hydrology – soil temperature • numerical model to solve soil heat equation • example for plot12a/b

  5. soil physics • Short term experiment • Different relations theta-h • Calibration: • Default MVG-set • Hysteresis • Reduced theta_sat

  6. Hydrologysoil moistureplot12a/bcrop rotation

  7. Hydrologysoil moistureplot12a/bcrop rotation

  8. 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

  9. Short term conc. NH4-N and NO3-N: black fallow

  10. Short term conc. NH4-N and NO3-N: : plot12a/b cropped

  11. 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)

  12. 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?)

  13. Conclusions (2) • 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

  14. discussion • 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

  15. Thank you for your attention

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