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A multi-objective hierarchical calibration procedure for land surface/ecosystem models. (English) Zbl 1278.93042

Summary: Land surface and ecosystem processes affect climate and weather in a range of time scales, from seconds to thousands of years. Land Surface/Ecosystem Models (LSEMs) calculate the fluxes between the biosphere and the atmosphere and other ecosystem dynamics processes. In this study, we develop a calibration procedure, based on the theory of ecosystems hierarchy, to optimize all processes simulated by an LSEM. The procedure is implemented on Optis, a software for hierarchical multi-objective calibration of LSEMs. Optis is based on the multi-objective genetic algorithm, Non-dominated Sorted Genetic Algorithm-II (NSGA-II). The calibration is hierarchically performed from the fastest process (radiative fluxes) to the slowest process (carbon allocation), optimizing nine model outputs. The procedure demonstrated to be efficient, with the nine-objective model optimization reaching about 80% of the performance achieved by the mono-objective optimization. This calibration methodology allows a better global performance of the model, as all simulated variables are optimized.

MSC:

93A30 Mathematical modelling of systems (MSC2010)
92D40 Ecology
93B40 Computational methods in systems theory (MSC2010)

Software:

Optis
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References:

[1] DOI: 10.1126/science.275.5299.502 · doi:10.1126/science.275.5299.502
[2] DOI: 10.1002/joc.893 · doi:10.1002/joc.893
[3] DOI: 10.1029/2007GL029271 · doi:10.1029/2007GL029271
[4] DOI: 10.1029/2007GL030612 · doi:10.1029/2007GL030612
[5] M.C. Fu, F.W. Glover, and J. April,Simulation Optimization: A Review, New Developments, and Applications, inProceedings of the 2005 Winter Simulation Conference, Orlando, FL, 4–7 December 2005, M. Kuhl, N. Steiger, F. Armstrong, and J. Joines, eds., Institute of Electrical and Electronics Engineers, Piscataway, NJ, 2005, pp. 83–95
[6] Trudinger CM, J. Geophys. Res. 112 (02027) pp 17– (2007)
[7] DOI: 10.1016/0022-1694(70)90255-6 · doi:10.1016/0022-1694(70)90255-6
[8] DOI: 10.1029/WR012i003p00477 · doi:10.1029/WR012i003p00477
[9] DOI: 10.1080/02626667709491716 · doi:10.1080/02626667709491716
[10] DOI: 10.1007/BF00939380 · Zbl 0792.90065 · doi:10.1007/BF00939380
[11] DOI: 10.1029/91WR02985 · doi:10.1029/91WR02985
[12] DOI: 10.1029/97WR03495 · doi:10.1029/97WR03495
[13] DOI: 10.1016/S0022-1694(97)00107-8 · doi:10.1016/S0022-1694(97)00107-8
[14] DOI: 10.1175/1525-7541(2002)003<0181:CALSMO>2.0.CO;2 · doi:10.1175/1525-7541(2002)003<0181:CALSMO>2.0.CO;2
[15] DOI: 10.1016/S0022-1694(00)00279-1 · doi:10.1016/S0022-1694(00)00279-1
[16] DOI: 10.1016/S0309-1708(02)00092-1 · doi:10.1016/S0309-1708(02)00092-1
[17] Vrugt JA, Water Resour. Res. 39 (8) pp SWC5.1– (2003)
[18] DOI: 10.5194/hess-10-289-2006 · doi:10.5194/hess-10-289-2006
[19] DOI: 10.1016/j.jhydrol.2007.05.014 · doi:10.1016/j.jhydrol.2007.05.014
[20] DOI: 10.1029/96GB02692 · doi:10.1029/96GB02692
[21] DOI: 10.1080/00401706.1991.10484804 · doi:10.1080/00401706.1991.10484804
[22] DOI: 10.1109/4235.996017 · Zbl 05451853 · doi:10.1109/4235.996017
[23] A. Saltelli, K. Chan, and E.M. Scott (eds.),Sensitivity Analysis, Wiley & Sons, Chichester, 2000 · Zbl 0961.62091
[24] DOI: 10.1021/cr040659d · doi:10.1021/cr040659d
[25] DOI: 10.1175/1520-0477(1981)062<0599:JAQMP>2.0.CO;2 · doi:10.1175/1520-0477(1981)062<0599:JAQMP>2.0.CO;2
[26] Willmott CJ, Phys. Geogr. 2 (2) pp 184– (1981)
[27] DOI: 10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2 · doi:10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2
[28] DOI: 10.1029/JC090iC05p08995 · doi:10.1029/JC090iC05p08995
[29] DOI: 10.3354/cr030079 · doi:10.3354/cr030079
[30] DOI: 10.1029/1998WR900018 · doi:10.1029/1998WR900018
[31] DOI: 10.1016/j.jhydrol.2005.07.031 · doi:10.1016/j.jhydrol.2005.07.031
[32] C.R. Reeves (ed.),Modern Heuristic Techniques for Combinatorial Problems, McGraw-Hill, New York, 1995
[33] DOI: 10.1016/S0377-2217(01)00123-0 · Zbl 1002.90060 · doi:10.1016/S0377-2217(01)00123-0
[34] Goldberg DE, Genetic Algorithms in Search, Optimization and Machine Learning (1989)
[35] DOI: 10.1162/106365600568158 · Zbl 05412910 · doi:10.1162/106365600568158
[36] DOI: 10.1162/106365600568202 · Zbl 05412936 · doi:10.1162/106365600568202
[37] DOI: 10.1126/science.1091165 · doi:10.1126/science.1091165
[38] M.C.A. Senna,Fração da radiação fotossinteticamente ativa absorvida pela floresta tropical amazônica: Uma comparação entre estimativas baseadas em modelagem, sensoriamento remoto e medições de campo, Ph.D. diss., Universidade Federal de Viçosa, 2004
[39] DOI: 10.1007/BF00131542 · doi:10.1007/BF00131542
[40] DOI: 10.1162/evco.1994.2.3.221 · Zbl 05412883 · doi:10.1162/evco.1994.2.3.221
[41] Praditwong K, CEC 2007. IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, 25–28 September 2007 pp 3959–
[42] R.C. Purshouse and P.J. Fleming,Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-criterion Optimization, inProceedings of the EMO, Faro, Portugal, 8–11 April 2003, pp. 16–30
[43] O’Neill RV, SCOPE 35 – Scales and Global Change: Spatial and Temporal Variability in Biospheric and Geospheric Processes pp 29– (1988)
[44] DOI: 10.1016/0304-3800(95)00121-2 · doi:10.1016/0304-3800(95)00121-2
[45] DOI: 10.1016/j.ecolmodel.2008.02.008 · doi:10.1016/j.ecolmodel.2008.02.008
[46] DOI: 10.1016/S0168-1923(02)00109-0 · doi:10.1016/S0168-1923(02)00109-0
[47] H.M.A. Imbuzeiro,Calibração do modelo IBIS na floresta amazônica usando múltiplos sítios, M.S. thesis, Universidade Federal de Viçosa, 2005
[48] DOI: 10.1029/2009JG001179 · doi:10.1029/2009JG001179
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