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Modeling GHG emissions and carbon sequestration

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Presentation by Ciniro Costa Junior, CCAFS, at the CLIFF-GRADS workshop on 6-7 October 2019 in Bali.

The two-day workshop was organized by the CCAFS Low Emissions Development Flagship and the Global Research Alliance on Agricultural Greenhouse Gases (GRA). Read more: https://ccafs.cgiar.org/cliff-grads-workshop

Published in: Environment
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Modeling GHG emissions and carbon sequestration

  1. 1. Ciniro Costa Junior CCAFS CLIFF-GRADS WORKSHOP OCTOBER 6-7, 2019 - BALI, INDONESIA Modeling GHG emissions and carbon sequestration
  2. 2. GHG emissions and carbon stocks are the result of biogeochemical processes. (Li, 2000) Soil C stocks
  3. 3. Basic structure of models for GHG emission and C stocks Strategies for modeling GHGs and C stocks dynamics can be classified as empirical or process-based. GHG emission | Δ C Stocks = Activity data x Emission Factor (EF) Quantity of GHG | Δ C stocks associated the Activity Data tCO2 activity data-1 y-1 Activity influencing GHG Quantity of GHG | Δ Soil C stocks Number of animals (head) Area (ha) Fertilizer (t) …
  4. 4. Empirical models rely on statistical relationships for which data is available. Activity data Emission Factor (EF) GHGemission (Shcherbak et al., 2014)
  5. 5. (IPCC Tier 1) (IPCC Tier 2) The IPCC Tiers 1 and 2 methodologies are examples of empirical models. (Giltrap et al., 2013)(FAO)
  6. 6. The IPCC Tiers 1 and 2 methodologies are examples of empirical models.
  7. 7. Least squares fitting to a linear function result in equation below (r2 = 0.8): E = 1 + 0.0125 x F where E = emission (kg N20-N ) and F = fertilizer application rate (kg N ha-1 y-1). This relationship was based on only 20 experiments, with measurements covering a full year; its global applicability is highly uncertain. Example of empirical model development / application (Bowman et al. 1996) Simple to use and transparent but are not sensitive for a range of soil, climate and farm management practices.
  8. 8. Process-based models attempt to simulate the underlying biogeochemical processes Activity data Emission Factor (EF) GHGemission|ΔCstocks (Li, 2000)
  9. 9. Site Validation Sensitivity tests Scale up Process-based models can be more responsive to the effects of soil properties, climate and management (Li et al., 2013; Lugato et al., 2010)
  10. 10. Examples of common process models: Events and management practices such as fire, grazing, cultivation, residue management, and organic matter or fertilizer additions are modeled. Set of farming management practices such as crop rotation, tillage, residue management, fertilization, manure amendment, irrigation, flooding, grazing, etc. • N2O, NOx, CH4, and CO2. • Nitrate leaching loss. • Soil carbon sequestration • Crop development and biomass yields. Soil management, crop management. • Soil carbon. • N2O, NOx, CH4, and CO2. • Nitrogen losses. • Soil carbon sequestration • Crop development and biomass yields.
  11. 11. Examples of common process models: Choosing a model Does it perform well for my target scope (GHG emissions/Soil C Stocks)? Has it been tested in similar conditions of mine (Country/Region /AgManag)? Did it have a good performance/validation? What was the main issues related to calibration and validations (data and parametrization)?
  12. 12. Find the appropriated process model: Literature review is one of the first step (Smith et al, 1997)
  13. 13. Set and run the model • Climate • Soil Properties • Cropping / Livestock system and management
  14. 14. Step 1: The model is first run in “default” mode (DEF) Step 2: Then run in calibration mode (CAL) using values for soil parameters that gave the closest fit to measurements. Step 3: Validation using another set of data - determine the confidence level in the model. Calibrate and validate the model using measured values/data (Rafique et al., 2011)
  15. 15. (Cui & Wang, 2019) Step 1: The model is first run in “default” mode (Original) Step 2: Then run in calibration mode (Modified) using values for soil parameters that gave the closest fit to measurements. Calibrate and validate the model using measured values/data
  16. 16. R-squared (r2) how close the data points around the fitted regression line. Root Mean Square Error (RMSE) SD of the residuals (prediction errors) – indicates how close the observed data points are to the model’s predicted values. 0 – 100% The Higher, the Better fit (% of the variability of simulations explained) 0 – 100% The Lower, the Better fit. (how accurate is the model) does not provide a formal hypothesis test for this relationship (F-test determines statistically significant) Model performance evaluation (Statistical analysis)
  17. 17. Model performance evaluation (Statistical analysis) (Rafique et al., 2011)
  18. 18. Further evaluation/validation: Test if the model really “understands” your system Deng et al. (2012) Deng et al. (2015) Lugato et al. (2010)
  19. 19. Model applications: Testing variables and evaluating alternative scenarios (Li et al., 2013)
  20. 20. Model applications: Scaling up (Lugato et al., 2010)
  21. 21. Model applications: Scaling up for GHG inventory (Smith et al., 2010)
  22. 22. Availability of data to satisfy the input requirements of models and understanding process driven GHG emissions and C stocks. Limitation/Challenge Model’s parametrization for tropical conditions
  23. 23. Conclusion • Statistical models are almost always simpler, more transparent, and easier to use than process models. • Therefore, there is less risk of obtaining a spurious prediction from a statistical model than from a process model. • In contrast, process models attempt to represent all processes affecting environmental flows and stocks, providing more flexibility in modeling different land use scenarios. • Limited biophysical data present a challenge for validation of both statistical and process models, limiting the regions and management practices for which models are suitable for predictions • Field experiments and model parameterization/calibration are crucial and necessary to improve understanding and predictions of environmental processes.

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