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The model–data fusion pitfall: assuming certainty in an uncertain world

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Abstract

Model–data fusion is a powerful framework by which to combine models with various data streams (including observations at different spatial or temporal scales), and account for associated uncertainties. The approach can be used to constrain estimates of model states, rate constants, and driver sensitivities. The number of applications of model–data fusion in environmental biology and ecology has been rising steadily, offering insights into both model and data strengths and limitations. For reliable model–data fusion-based results, however, the approach taken must fully account for both model and data uncertainties in a statistically rigorous and transparent manner. Here we review and outline the cornerstones of a rigorous model–data fusion approach, highlighting the importance of properly accounting for uncertainty. We conclude by suggesting a code of best practices, which should serve to guide future efforts.

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References

  • Abramowitz G, Pitman A, Gupta H, Kowalczyk E, Wang P (2007) Systematic bias in land surface models. J Hydrometeorol 8:989–1001

    Google Scholar 

  • Alton P, Bodin P (2010) A comparative study of a multilayer and a productivity (light-use) efficiency land-surface model over different temporal scales. Agric For Meterorol 150:182–195

    Article  Google Scholar 

  • Baldocchi D (2008) Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust J Bot 56:1–26

    Article  CAS  Google Scholar 

  • Barrett DJ, Hill MJ, Hutley LB, Beringer J, Xu JH, Cook GD, Carter JO, Williams RJ (2005) Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods. Aust J Bot 53:689–714

    Google Scholar 

  • Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36

    Google Scholar 

  • Beven KJ, Smith PJ, Freer JE (2008) So just why would a modeller choose to be incoherent? J Hydrol 354:15–32

    Article  Google Scholar 

  • Braswell BH, Sacks WJ, Linder E, Schimel DS (2005) Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Glob Change Biol 11:335–355

    Article  Google Scholar 

  • Brown JD (2010) Prospects for the open treatment of uncertainty in environmental research. Prog Phys Geogr 34:75–100

    Article  Google Scholar 

  • Butts MB, Payne JT, Kristensen M, Madsen H (2004) An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J Hydrol 298:242–266

    Article  Google Scholar 

  • Carbone MS, Vargas R (2008) Automated soil respiration measurements: new information, opportunities and challenges. New Phytol 177:295–297

    Article  Google Scholar 

  • Carvalhais N, Reichstein M, Seixas J, Collatz GJ, Santos Pereira J et al (2008) Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval. Glob Biochem Cycles 22:GB2007. doi:10.1029/2007GB003033

  • Carvalhais N, Reichstein M, Ciasis P, Collatz GJ, Mohecha MD et al (2010) Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints. Glob Change Biol 16:2813–2829

    Google Scholar 

  • Cressie N, Calder CA, Clark JS, Hoef JMV, Wikle CK (2009) Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecol Appl 19:553–570

    Article  PubMed  Google Scholar 

  • Desai AR (2010) Climatic and phenological controls on coherent regional interannual variability of carbon dioxide flux in a heterogeneous landscape. J Geophys Res 115:G00J02. doi:10.1029/2010JG001423

    Article  Google Scholar 

  • Desai AR, Richardson AD, Moffat AM, Kattge J, Hollinger DY et al (2008) Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agric For Meteorol 148:821–838

    Google Scholar 

  • Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York

    Google Scholar 

  • Fox A, Williams M, Richardson AD, Cameron D, Gove JH et al (2009) The REFLEX project: comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data. Agric For Meteorol 149:1597–1615

    Google Scholar 

  • Franks SW, Beven KJ, Quinn PF, Wright IR (1997) On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: Equifinality and the problem of robust calibration. Agric For Meteorol 86:63–75

    Article  Google Scholar 

  • Franks SW, Beven KJ, Gash JHC (1999) Multi-objective conditioning of a simple SVAT model. Hydrol Earth Sys Sci 3:488–489

    Google Scholar 

  • Friedlingstein P et al (2006) Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J Climatol 19:3337–3353

    Article  Google Scholar 

  • Hanson PJ, Edwards NT, Garten CT, Andrews JA (2000) Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48:115–146

    Article  CAS  Google Scholar 

  • Hastings WK (1970) Monte-Carlo sampling methods using markov chains and their applications. Biometrika 57:97

    Article  Google Scholar 

  • Högberg P (2010) Is tree root respiration more sensitive than heterotrophic respiration to changes in soil temperature? New Phytol 188:9–10

    Article  PubMed  Google Scholar 

  • Knorr W, Kattge J (2005) Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling. Glob Change Biol 11:1333–1351

    Article  Google Scholar 

  • Kuzyakov Y (2006) Sources of CO2 efflux from soil and review of partitioning methods. Soil Biol Biochem 38:425–448

    Article  CAS  Google Scholar 

  • Lasslop G, Reichstein M, Kattge J, Papale D (2008) Influences of observation errors in eddy flux data on inverse model parameter estimation. Biogeosciences 5:1311–1324

    Article  CAS  Google Scholar 

  • Luo Y, Clark JS, Hobbs T, Lakshmivarahan S, Latimer AM, Ogle K, Schimel D, Zhou X (2008) Symposium 23. Toward ecological forecasting. Bull Ecol Soc Am 89:467–474

    Google Scholar 

  • Luo Y, Weng E, Wu X, Gao C, Zhou X, Zhang L (2009) Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. Ecol Appl 19:571–574

    Article  PubMed  Google Scholar 

  • Medvigy D, Wofsy SC, Munger JW, Hollinger DY, Moorcroft PR (2009) Mechanistic scaling of ecosystem function and dynamics in space and time: ecosystem demography model version 2. J Geophys Res 114:G01002. doi:10.1029/2008JG000812

    Article  Google Scholar 

  • Moore D, Hu J, Sacks WJ, Schimel DS, Monson RK (2008) Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes. Agric For Meteorol 148:1467–1477

    Article  Google Scholar 

  • Pappenberger F, Beven KJ (2006) Ignorance is bliss: or seven reasons not to use uncertainty analysis. Water Resour Res 42: W05302. doi:10.1029/2005WR004820

  • Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: The art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  • Rastetter EB (2003) The collision of hypotheses: what can be learned from comparisons of ecosystem models? In: Canham CD, Cole JJ, Lauenroth WK (eds) Models in ecosystem science. Princeton University Press, Princeton, pp 221–224

    Google Scholar 

  • Raupach MR et al (2005) model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications. Glob Change Biol 11:378–397

    Article  Google Scholar 

  • Reichstein M et al (2003) Inverse modeling of seasonal drought effects on canopy CO2/H2O exchange in three Mediterranean ecosystems. J Geophys Res 108(D23):4726. doi:10.1029/2003JD003430

    Article  Google Scholar 

  • Richardson AD, Hollinger DY (2005) Statistical modeling of ecosystem respiration using eddy covariance data: maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models. Agric For Meteorol 131:191–208

    Article  Google Scholar 

  • Richardson AD, Braswell BH, Hollinger DY, Burman P, Davidson EA et al (2006) Comparing simple respiration models for eddy flux and dynamic chamber data. Agric For Meteorol 141:219–234

    Google Scholar 

  • Richardson AD, Mahecha MD, Falge E, Kattge J, Moffat AM et al (2008) Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals. Agric For Meteorol 148:38–50

    Google Scholar 

  • Richardson A, Williams M, Hollinger DY, Moore DJP, Dail DB et al (2010) Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints. Oecologia 164:25–40

    Article  PubMed  Google Scholar 

  • Sacks WJ, Schimel DS, Monson RK (2007) Coupling between carbon cycling and climate in a high-elevation, subalpine forest: a model-data fusion analysis. Oecologia 151:54–68

    Article  PubMed  Google Scholar 

  • Santaren D, Peylin P, Viovy N, Ciais P (2007) Optimizing a process-based ecosystem model with eddy-covariance flux measurements: a pine forest in southern France. Glob Biogeochem Cyc 21:GB2013. doi:10.1029/2006GB002834

    Article  Google Scholar 

  • Savage K, Davidson EA, Richardson AD (2008) A conceptual and practical approach to data quality and analysis procedures for high-frequency soil respiration measurements. Funct Ecol 22:1000–1007

    Article  Google Scholar 

  • Taylor JR (1997) An introduction to error analysis: the study of uncertainties in physical measurements. University Science Books, Sausalito

    Google Scholar 

  • Trudinger CM, Raupach MR, Rayner PJ, Kattge J, Liu Q et al (2007) OptIC project: an intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models. J Geophys Res 112:G02027. doi:10.1029/2006JG000367

  • Vargas R, Carbone M, Reichstein M, Baldocchi D (2011) Frontiers and challenges in soil respiration research: from measurements to model-data integration. Biogeochemistry 102:1–13

    Article  Google Scholar 

  • Wang YP, Baldocchi D, Leuning R, Falge E, Vesala T (2006) Estimating parameters in a land-surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites. Glob Change Biol 12:1–19

    Article  CAS  Google Scholar 

  • Wang YP, Trudinger CM, Enting IG (2009) A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales. Agric For Meteorol 149:1829–1842

    Article  Google Scholar 

  • Weng E, Luo Y (2011) Relative information contributions of model vs. data to constraints of short- and long-term forecasts of forest carbon dynamics. Ecol Appl 21:1490–1505

    Google Scholar 

  • Williams M, Schwarz PA, Law BE, Irvine J, Kurpius MR (2005) An improved analysis of forest carbon dynamics using data assimilation. Glob Change Biol 11:89–105

    Article  Google Scholar 

  • Williams M, Richardson AD, Reichstein M, Stoy PC, Peylin P et al (2009) Improving land surface models with FLUXNET data. Biogeosciences 6:1341–1359

    Google Scholar 

  • Wu XW, Luo YQ, Weng ES, White L, Ma Y, Zhou XH (2009) Conditional inversion to estimate parameters from eddy-flux observations. J Plant Ecol 2:55–68

    Article  Google Scholar 

  • Xie H, Eheart JW, Chen Y, Bailey BA (2009) An approach for improving the sampling efficiency in the Bayesian calibration of computationally expensive simulation models. Water Resour Res 45:W06419. doi:10.1029/2007WR006773

    Article  Google Scholar 

  • Xu T, White L, Hul D, Luo Y (2006) Probabilistic inversion of a terrestrial ecosystem model: analysis of uncertainty in parameter estimation and model prediction. Glob Biogeochem Cycles 20:GB2007. doi:10.1029/2005GB002468

  • Zhang Q, Xiao X, Braswell B, Linder E, Baret F, Moore B (2005) Estimating light absorption by chlorophyll, leaf and canopt in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens Environ 99:359–371

    Google Scholar 

  • Zhang L, Luo Y, Yu G, Zhang L (2010) Estimated carbon residence times in three forest ecosystems of eastern China: applications of probabilistic inversion. J Geophys Res 115:G01010. doi:10.1029/2009JG001004

    Article  Google Scholar 

  • Zhou X et al (2010) Concurrent and lagged impacts of an anomalously warm year on autotrophic and heterotrophic components of soil respiration: a deconvolution analysis. New Phytol 187:184–198

    Article  PubMed  Google Scholar 

  • Zobitz JM, Moore D, Sacks WJ, Monson RK, Bowling DR, Schimel DS (2008) Integration of process-based soil respiration models with whole ecosystem CO2 measurements. Ecosystems 11:250–269

    Article  CAS  Google Scholar 

  • Zobitz JM, Desai, AR, Moore DJP, Chadwick MA (2011) A primer for data assimilation with ecological models using markov chain Monte Carlo (MCMC). Oecologia. doi:10.1007/s00442-011-2107-9

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Acknowledgments

TFK and ADR acknowledge support from the Northeastern States Research Cooperative, and from the Office of Science (BER), U.S. Department of Energy, through the Terrestrial Carbon Program under Interagency Agreement number DE-AI02-07ER64355, and through the Northeastern Regional Center of the National Institute for Climatic Change Research. MR acknowledges support from the CARBO-Extreme project of the European Commission (FP7-ENV-2008-1-226701). MSC acknowledges support from the Kearney Foundation of Soil Science.

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Correspondence to Trevor F. Keenan.

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Communicated by Russell Monson.

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Keenan, T.F., Carbone, M.S., Reichstein, M. et al. The model–data fusion pitfall: assuming certainty in an uncertain world. Oecologia 167, 587–597 (2011). https://doi.org/10.1007/s00442-011-2106-x

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  • DOI: https://doi.org/10.1007/s00442-011-2106-x

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