Budget of soil carbon and greenhouse gases. Rene Dechow Annette Freibauer

Transcription

1 Budget of soil carbon and greenhouse gases Rene Dechow Annette Freibauer

2 Max-Planck-Institute for Biogeochemistry Founded in 997 Staff: 45, out of which ~6 scientists Research about global carbon cycle My research group Nitrogen and N 2 O in agricultural soils Carbon sequestration / turnover Tropical deforestation / degradation Consultations to policy

3 GHG measurements in a commercial cropland 26: Manual chambers 27: Automated chambers N 2 O, CH 4, (CO 2 ) N 2 O, CH 4, CO 2

4 Tasks in SeqCure Modelling the budget of C and GHGs at crop measurement sites Extrapolation to the GHG effects of bioenergy crops (soils only) in Italy for the next decades

5 Our N 2 O model: a tool for all GHGs Based on fuzzy reasoning a flexible tool for non-linear optimization Process knowledge used for expert rules Data used for a continuous learning algorithm Results obtainable at daily, seasonal and annual time scale Data availability determines which rules will be prioritized Developed for N 2 O emissions from croplands and grasslands in Europe with very little data coverage in warm temperate and Mediterranean climate Extension to soil C concentrations in preparation.

6 Model structure IF WFPS is small AND Precipitation high THEN N2O emission is high. IF A and B and C and Conclusion =Z Aggregation Result 3. IF A and B and C and Conclusion =Z A={A= WFPS is small, A2= WFPS is medium, A3= WFPS is high } Z={Z= Emission is small, Z2=Emission is medium, Z3= Emission is high } Membership A= small A2= middle A3= high A= small A2= middle A3= high WFPS

7 Potentially important factors Factors with daily resolution Soil temperature - process oriented model WFPS -capacity model and Darcy equation Nmin -Snow cover, Evapotranspiration -Fertilisation, deposition -Leaching (CDE) Emission potentials Factors with seasonal, long term resolution resolution Corg Norg ph WFPS at fieldcapacity Sand Clay + T mean (year, spring, summer,autumn, winter ) P (year, spring, summer, autumn, winter ) Mean number of annual frostdays

8 Dataset Grassland Cropland - 25 locations - Cropland: 49 sites - Grassland:47 sites - minimal measurement periode: 2 days - range of fertilisation -4 kg/ha - Texture: sand-clay

9 Emissionpotentials to consider event driven peaks Precipitation E P d wet d frost = 3 i = E = frost thaw P d i / precipitation 3 days Frost-Thaw Cycles d thaw E fert T ( ) const d i ( d ) frost d thaw lenght of frost periode Fertilisation Nmin d time after frostperiode = 3 i = N min i / ( ) const d N input by fertilisation days since fertilisation i i N2O [g/ha/d] N2O emission [g/ha/d] Nmin [mg/kg] Potemtial [] N2O emission emission potential Potsdam,Hellebrandt (25) N2O emission emission potential Bavaria,Dörsch (2) Nmin Emission potential Scotland, Smith et al., (2)

10 Longterm factors for cropland EF [-].6.4 winter.2 autumn. summer.8 spring EF [-].3.2. R 2 = mean of frostdays Fig.: Seasonal EF Fig.: EF Summer versus mean of frostdays Tab.: Regressionanalyses for relationships between seasonal EF and factors Year Spring Summer Autumn Winter Mar-May Jun-Aug Sep-Nov Dez-Febr No. of frostdays No. of frostdays No. of frostdays Clay P autumn P autumn Clay ph R 2 =.37 R 2 =.42 R 2 =.22 R 2 =.28 R 2 =.9 p<. p<. p<. p<. p<.2

11 Modelling the nitrous oxide emission on an annual scale N2O,calculated [g/ha/a] calculated [ln N2O kg/ha/a] Grassland Fertilisation Precipitation, summer Temperature, summer Calibration slope=.99 R2 =.83 Calibration slope=.6 R 2 =.837 Validation slope=.72 R 2 = N2O, measured [g/ha/a] Validation slope=.99 R2 = measured [ln N2O kg/ha/a] calculated N2O [kg/ha] calculated ln N2O [g/ha/d] Cropland Fertilisation Mean number of frost days Precipitation, autumn Clay Calibration slope=.93 R2 =.78 Validation slope=.93 R2 = measured N2O [kg/ha] Calibration slope=.2 R 2 =.83 Validation slope=. R 2 = measured ln N2O [g/ha/a]

12 What the model can and cannot do. Improving the representativness of the annual-resolutionmodel by adding the Bouwman data 2. Regional differences can be adressed, which seem to be beside the fertilisation amount the most important drivers 3. Relationships between weather and fertilisation state can be modeled, but other management options (kind of fertilizers, crop type) are not predictable because of the limited training data: How much can SeqCure add? How relevant are these secondary management parameters?

13 Issues to discuss Match between parameters measured and model input requirements (model complexity) Major target variables Data for extrapolation Which scenarios? Link with GHGs in other farm activities

14 Proccess interaction Physical environment Resource Availability Seasonal/longterm Frost/Thaw Cycles Dry/Wet Cycles Moisture, T (means, variability) ph C,N Micro-scale NO 3,NH 4 O 2 C ph Temperature Empirical approach Process oriented Process Rate Microbial Community Structure Microbial Community Function Balser et al. (26) Plant Soil (changed)

15 Model calibration. Recombination of fuzzy numbers (daily resolution) WFPS precipitation N 2 O emission IF small AND high THEN high 2. Continious adaption (annual resolution) AND AND

16 Modelling daily emissions Sensitive factors:. Fertilisation potential R² slope 2. Frost potential Calibration 3. Rain potential 4. Number of frost days whole period annual WFPS at fieldcapacity Validation 6. P Summer 7. Vegetation (Grass or Crop) whole period annual N2O, calculated [kg/ha] calibration validation calculated [kg/ha] calibration validation N2O, measured [kg/ha] 5 5 measured [kg/ha] Fig.: Calculated versus measured fluxes for the measurement period Fig.: Calculated versus measured annual fluxes

17 Comparision of measured and modelled time series Frost N2O [g/ha] Prec. Prec. Frost simulated measured Bavaria,Dörsch (2) N2O [g/ha/d] 5 5 calculated measured Potsdam, Hellebrandt (25) N2O [g/ha/d] Frost Frost calculated measured South Finland, Syväsalo et al. (24)

18 Uncertainty estimation Input factors Calibration data -Measurement error -Interpolation error relative residuals between original and artificial time series % quantil standard devation mean mean timestep. Bulding classes of possible mean time steps between measurements (-2 days,3-5 days,..) 2. Enlarging the time steps for each N2O timeseries until the next class is reached and calculation of the yearly residuals between original and artificial time series 3. Doing this 2 times per time series and class 4. Mean, standard devation, 95% quantil of the resulting residual distribution

19 Fuzzy logic approach as interpolating tool long term factor long term factor 2 Weighting factor IF small AND high THEN : Modellconfig Result at Loc. X Weighting factor IF medium AND medium THEN : Model config. 2

20 Inference scheme for cropland emissions. Fertilization Frostdays P_autumn Result

21 Monthly emission N2O [g/ha/month] sucessesiv monthly time series