Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation

Journal of Geophysical Research: Biogeosciences

Volumn 118, Issue 4, 2013, Pages 1674-1688

  Ren, Xiaoli ^a,b   He, Honglin ^a   Moore, David J P ^c   Zhang, Li ^a   Liu, Min ^d   Li, Fan ^a,b   Yu, Guirui ^a   Wang, Huimin ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c UNIVERSITY OF ARIZONA   (United States)

^d EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

ecosystem model; Markov Chain Monte Carlo (MCMC); sensitivity analysis; Sobol' method; uncertainty analysis

Indexed keywords

CARBON FLUX; CONIFEROUS TREE; ECOSYSTEM MODELING; EVAPOTRANSPIRATION; MARKOV CHAIN; MONTE CARLO ANALYSIS; NET PRIMARY PRODUCTION; PHOTOSYNTHESIS; PLANTATION FORESTRY; SENSITIVITY ANALYSIS; SUBTROPICAL REGION; TRANSPIRATION; UNCERTAINTY ANALYSIS; WATER FLOW;

CHINA; JIANGXI; QIANYANZHOU;

EID: 84892907101     PISSN: None     EISSN: 21698961     Source Type: Journal    
DOI: 10.1002/2013JG002402     Document Type: Article

References (67)

1. Aber, J. D., and, C. A. Federer, (1992), A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems, Oecologia, 92 (4), 463-474. (Pubitemid 24408706)
2. Aber, J. D., S. V. Ollinger, C. A. Federer, P. B. Reich, M. L. Goulden, D. W. Kicklighter, J. M. Melillo, and, R. G. Lathrop, (1995), Predicting the effects of climate change on water yield and forest production in the northeastern United States, Climate Res., 5 (3), 207-222. (Pubitemid 3017489)
3. 2 exchange to the canopy: A generalized model of forest photosynthesis compared with measurements by eddy correlation, Oecologia, 106 (2), 257-265.
4. Aber, J. D., S. V. Ollinger, and, C. T. Driscoll, (1997), Modeling nitrogen saturation in forest ecosystems in response to land use and atmospheric deposition, Ecol. Modell., 101 (1), 61-78. (Pubitemid 27288835)
5. Beer, C., et al. (2010), Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate, Science, 329 (5993), 834-838.
6. Bonan, G. B., (2008), Forests and climate change: Forcings, feedbacks, and the climate benefits of forests, Science, 320 (5882), 1444-1449. (Pubitemid 351929396)
7. Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. B. Zeng, Y. J. Dai, R. E. Dickinson, and, Z. L. Yang, (2002), The land surface climatology of the community land model coupled to the NCAR community climate model, J. Clim., 15 (22), 3123-3149. (Pubitemid 36006339)
8. Braswell, B. H., W. J. Sacks, E. Linder, and, D. S. Schimel, (2005), Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations, Global Change Biol., 11 (2), 335-355. (Pubitemid 40273703)
9. Cao, M. K., and, F. I. Woodward, (1998), Dynamic responses of terrestrial ecosystem carbon cycling to global climate change, Nature, 393 (6682), 249-252. (Pubitemid 28261719)
10. Confalonieri, R., G. Bellocchi, S. Bregaglio, M. Donatelli, and, M. Acutis, (2010), Comparison of sensitivity analysis techniques: A case study with the rice model WARM, Ecol. Modell., 221 (16), 1897-1906.
11. Dietze, M. C., D. S. LeBauer, and, R. Kooper, (2013), On improving the communication between models and data, Plant, cell Environ., 36 (9), 1575-1585, doi: 10.1111/pce.12043.
12. Dixon, R. K., S. Brown, R. A. Houghton, A. M. Solomon, M. C. Trexler, and, J. Wisniewski, (1994), Carbon pools and flux of global forest ecosystems, Science, 263 (5144), 185-190.
13. Dufrene, E., H. Davi, C. Francois, G. le Maire, V. Le Dantec, and, A. Granier, (2005), Modelling carbon and water cycles in a beech forest Part I: Model description and uncertainty analysis on modelled NEE, Ecol. Modell., 185 (2-4), 407-436. (Pubitemid 40564512)
14. Falge, E., et al. (2001), Gap filling strategies for defensible annual sums of net ecosystem exchange, Agric. For. Meteorol., 107 (1), 43-69. (Pubitemid 32176659)
15. Friedlingstein, P., et al. (2006), Climate-carbon cycle feedback analysis: Results from the C(4)MIP model intercomparison, J. Clim., 19 (14), 3337-3353. (Pubitemid 44177898)
16. He, H. L., et al. (2010), Uncertainty analysis of eddy flux measurements in typical ecosystems of ChinaFLUX, Ecol Inform., 5 (6), 492-502.
17. Heimann, M., and, M. Reichstein, (2008), Terrestrial ecosystem carbon dynamics and climate feedbacks, Nature, 451 (7176), 289-292.
18. Hollinger, D. Y., and, A. D. Richardson, (2005), Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25 (7), 873-885. (Pubitemid 40995637)
19. Hu, J., D. J. P. Moore, S. P. Burns, and, R. K. Monson, (2010), Longer growing seasons lead to less carbon sequestration by a subalpine forest, Global Change Biol., 16 (2), 771-783.
20. Hurtt, G. C., and, R. A. Armstrong, (1996), A pelagic ecosystem model calibrated with BATS data, Deep Sea Res., Part II, 43 (2-3), 653-683.
21. Jacques, J., C. Lavergne, and, N. Devictor, (2006), Sensitivity analysis in presence of model uncertainty and correlated inputs, Reliab. Eng. Syst. Safe, 91 (10-11), 1126-1134. (Pubitemid 44055917)
22. Keenan, T. F., M. S. Carbone, M. Reichstein, and, A. D. Richardson, (2011), The model-data fusion pitfall: Assuming certainty in an uncertain world, Oecologia, 167 (3), 587-597.
23. Keenan, T. F., E. A. Davidson, J. W. Munger, and, A. D. Richardson, (2013), Rate my data: Quantifying the value of ecological data for the development of models of the terrestrial carbon cycle, Ecol. Appl., 23 (1), 273-286.
24. Larocque, G. R., J. S. Bhatti, R. Boutin, and, O. Chertov, (2008), Uncertainty analysis in carbon cycle models of forest ecosystems: Research needs and development of a theoretical framework to estimate error propagation, Ecol. Modell., 219 (3-4), 400-412.
25. Lasslop, G., M. Reichstein, J. Kattge, and, D. Papale, (2008), Influences of observation errors in eddy flux data on inverse model parameter estimation, Biogeosciences, 5 (5), 1311-1324.
26. LeBauer, D. S., D. Wang, K. T. Richter, C. C. Davidson, and, M. C. Dietze, (2013), Facilitating feedbacks between field measurements and ecosystem models, Ecol. Monogr., 83 (2), 133-154.
27. Lilburne, L., and, S. Tarantola, (2009), Sensitivity analysis of spatial models, Int. J. Geogr. Inf. Sci., 23 (2), 151-168.
28. Lin, J. C., M. R. Pejam, E. Chan, S. C. Wofsy, E. W. Gottlieb, H. A. Margolis, and, J. H. McCaughey, (2011), Attributing uncertainties in simulated biospheric carbon fluxes to different error sources, Global Biogeochem. Cycles, 25, GB2018, doi: 10.1029/2010GB003884.
29. Liu, Q. J., H. Q. Zeng, and, Z. Q. Ma, (2008), Carbon sequestration of Pinus elliottii plantation in relation with water consumption in subtropical China, Acta Ecol. Sin., 28 (11), 5322-5330.
30. 2 flux components in subtropical evergreen coniferous plantation, Sci. China Ser. D, 52 (2), 257-268.
31. Liu, M., H. He, G. Yu, X. Sun, L. Zhang, S. Han, H. Wang, and, G. Zhou, (2012), Uncertainty analysis in data processing on the estimation of net carbon exchanges at different forest ecosystems in China, J. For. Res., 17 (3), 312-322.
32. 2 exchange using eddy covariance data: Some perils and pitfalls, Tree Physiol., 25 (7), 839-857. (Pubitemid 40995635)
33. Mercado, L. M., N. Bellouin, S. Sitch, O. Boucher, C. Huntingford, M. Wild, and, P. M. Cox, (2009), Impact of changes in diffuse radiation on the global land carbon sink, Nature, 458 (7241), 1014-1017.
34. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and, E. Teller, (1953), Equation of state calculations by fast computing machines, J. Chem. Phys., 21 (6), 1087-1092.
35. 2O fluxes, Agric. For. Meteorol., 148 (10), 1467-1477.
36. Morris, M. D., (1991), Factorial sampling plans for preliminary computational experiments, Technometrics, 33 (2), 161-174.
37. Most, T., (2012), Variance-based sensitivity analysis in the presence of correlated input variables, paper presented at Proc. 5th Int. Conf. Reliable Engineering Computing (REC), Brno, Czech Republic.
38. Pan, Y. D., et al. (2011), A large and persistent carbon sink in the world's forests, Science, 333 (6045), 988-993.
39. Pappas, C., S. Fatichi, S. Leuzinger, A. Wolf, and, P. Burlando, (2013), Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues, J. Geophys. Res. Biogeosci., 118, 505-528, doi: 10.1002/jgrg.20035.
40. Quillet, A., M. Garneau, and, S. Frolking, (2013), Sobol' sensitivity analysis of the Holocene Peat Model: What drives carbon accumulation in peatlands?, J. Geophys. Res. Biogeosci., 118, 203-214, doi: 10.1029/ 2012JG002092.
41. Radtke, P. J., T. E. Burk, and, P. V. Bolstad, (2001), Estimates of the distributions of forest ecosystem model inputs for deciduous forests of eastern North America, Tree Physiol., 21 (8), 505-512. (Pubitemid 32579581)
42. Raupach, M. R., P. J. Rayner, D. J. Barrett, R. S. DeFries, M. Heimann, D. S. Ojima, S. Quegan, and, C. C. Schmullius, (2005), Model-data synthesis in terrestrial carbon observation: Methods, data requirements and data uncertainty specifications, Global Change Biol., 11 (3), 378-397.
43. Reich, P. B., B. D. Kloeppel, D. S. Ellsworth, and, M. B. Walters, (1995), Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species, Oecologia, 104 (1), 24-30.
44. Ren, X. L., H. L. He, L. Zhang, L. Zhou, G. R. Yu, and, J. W. Fan, (2013), Spatiotemporal variability analysis of diffuse radiation in China during 1981-2010, Ann. Geophys., 31 (2), 277-289.
45. Ricciuto, D. M., A. W. King, D. Dragoni, and, W. M. Post, (2011), Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length, J. Geophys. Res., 116, G01033, doi: 10.1029/2010JG001400.
46. Richardson, A. D., et al. (2010), Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints, Oecologia, 164 (1), 25-40.
47. Roderick, M. L., G. D. Farquhar, S. L. Berry, and, I. R. Noble, (2001), On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation, Oecologia, 129 (1), 21-30. (Pubitemid 32943758)
48. 2 fluxes at Niwot Ridge, Colorado, Global Change Biol., 12 (2), 240-259.
49. Sacks, W. J., D. S. Schimel, and, R. K. Monson, (2007), Coupling between carbon cycling and climate in a high-elevation, subalpine forest: A model-data fusion analysis, Oecologia, 151 (1), 54-68. (Pubitemid 46180111)
50. Saltelli, A., (2002), Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 145 (2), 280-297. (Pubitemid 34524628)
51. Santaren, D., P. Peylin, N. Viovy, and, P. Ciais, (2007), Optimizing a process-based ecosystem model with eddy-covariance flux measurements: A pine forest in southern France, Global Biogeochem. Cycles, 21, GB2013, doi: 10.1029/2006GB002834.
52. Sellers, P. J., Y. Mintz, Y. C. Sud, and, A. Dalcher, (1986), A simple biosphere model (SIB) for use within general-circulation models, J. Atmos. Sci., 43 (6), 505-531.
53. Smith, J. E., and, L. S. Heath, (2001), Identifying influences on model uncertainty: An application using a forest carbon budget model, Environ. Manage., 27 (2), 253-267. (Pubitemid 32236703)
54. Sobol, I. M., (1993), Sensitivity estimates for nonlinear mathematical models, Math. Model. Comput. Exp., 1 (4), 407-414.
55. Tang, Y., P. Reed, T. Wagener, and, K. van Werkhoven, (2007a), Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11 (2), 793-817. (Pubitemid 46204653)
56. Tang, Y., P. Reed, K. van Werkhoven, and, T. Wagener, (2007b), Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis, Water Resour. Res., 43, W06415, doi: 10.1029/2006WR005813.
57. Tarantola, S., M. Nardo, M. Saisana, and, D. Gatelli, (2006), A new estimator for sensitivity analysis of model output: An application to the e-business readiness composite indicator, Reliab. Eng. Syst. Safe, 91 (10-11), 1135-1141. (Pubitemid 44026671)
58. Updegraff, K., P. R. Zimmerman, P. Kozak, D. G. Chen, and, M. Price, (2010), Estimating the uncertainty of modeled carbon sequestration: The GreenCert (TM) system, Environ. Modell. Software, 25 (12), 1565-1572.
59. Verbeeck, H., R. Samson, F. Verdonck, and, R. Lemeur, (2006), Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: A Monte Carlo analysis, Tree Physiol., 26 (6), 807-817. (Pubitemid 43998336)
60. Williams, M., P. A. Schwarz, B. E. Law, J. Irvine, and, M. R. Kurpius, (2005), An improved analysis of forest carbon dynamics using data assimilation, Global Change Biol., 11 (1), 89-105. (Pubitemid 40134047)
61. Xu, T., L. White, D. Hui, and, Y. Luo, (2006), Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction, Global Biogeochem. Cycles, 20, GB2007, doi: 10.1029/2005GB002468.
62. Yu, G. R., X. F. Wen, Q. K. Li, L. M. Zhang, C. Y. Ren, Y. F. Liu, and, D. X. Guan, (2005), Seasonal patterns and environmental control of ecosystem respiration in subtropical and temperate forests in China, Sci. China Ser. D, 48 (Supp.1), 93-105.
63. Yu, G. R., X. F. Wen, X. M. Sun, B. D. Tanner, X. H. Lee, and, J. Y. Chen, (2006), Overview of ChinaFLUX and evaluation of its eddy covariance measurement, Agric. For. Meteorol., 137 (3-4), 125-137. (Pubitemid 44163023)
64. Zhang, W. J., H. M. Wang, F. T. Yang, Y. H. Yi, X. F. Wen, X. M. Sun, G. R. Yu, Y. D. Wang, and, J. C. Ning, (2011), Underestimated effects of low temperature during early growing season on carbon sequestration of a subtropical coniferous plantation, Biogeosciences, 8 (6), 1667-1678.
65. Zhang, L., G. Yu, F. Gu, H. He, L. Zhang, and, S. Han, (2012), Uncertainty analysis of modeled carbon fluxes for a broad-leaved Korean pine mixed forest using a process-based ecosystem model, J. For. Res., 17 (3), 268-282.
66. 2 measurements, Ecosystems, 11 (2), 250-269.
67. Zobitz, J., A. Desai, D. Moore, and, M. Chadwick, (2011), A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC), Oecologia, 167 (3), 599-611.