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9 th LBA-ECO Science Team Meeting

Assessing the Influence of Observational Data Error on SiB 2 Model Parameter Uncertainty. 9 th LBA-ECO Science Team Meeting. Luis A. Bastidas 1 , E. Rosero 1 , S. Pande 1 , W.J. Shuttleworth 2 1 Civil and Environmental Engineering and Utah Water Research Laboratory, Logan, Utah

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9 th LBA-ECO Science Team Meeting

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  1. Assessing the Influence of Observational Data Error on SiB 2 Model Parameter Uncertainty 9th LBA-ECO Science Team Meeting Luis A. Bastidas1, E. Rosero1, S. Pande1, W.J. Shuttleworth2 1Civil and Environmental Engineering and Utah Water Research Laboratory, Logan, Utah 2Hydrology and Water Resources, SAHRA – NSF Science and Technology Center University of Arizona, Tucson, Arizona

  2. Model Qualification Analysis Conceptual Model Reality Model Validation Code Verification Simulation Programming Model Construction Model Code Model Model Calibration Modeling Modified from Refsgaard, 2001

  3. Xo Inputs  Outputs State Variables X I O Model Structure Model Structure Xt = F ( Xt-1, , It-1 ) Ot = G ( Xt, , It ) Initial States Parameters Components of a Model

  4. Pareto Optimality Parameter Space Criterion Space f2 β f1 Criterion f2 Parameter 2 δ γ α α γ δ β Parameter 1 Criterion f1

  5. Sensitivity Analysis. SiB 2 @ Santarem Km 83 MOGSA Algorithm Bastidas et al., JGR,1999 Multi Objective Generalized Sensitivity Analysis The further away from the center the more sensitive the parameter Pre-logging Post-logging

  6. Pareto Set of Parameters

  7. Observed vs Computed. Pre-logging

  8. Dry Typical Periods

  9. Wet Typical Periods

  10. Daily Average E H CO2

  11. Performance Measures E H CO2 E H CO2 R NSE BIAS RMSE

  12. Objectives for Radiation and Precipitation Errors Heteroscedastic Error Added

  13. Parameter Distributions

  14. Parameter Distributions 0 1

  15. Daily Average E H CO2 0 24 0 24 0 24

  16. Beware …. FOOL OUTSIDE “A fool with a tool is still a fool”

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