Abstract
The water and energy transfer of land surface is complex due to its large spatial and temporal variability. The modeling and simulation is an important means to study land water and energy transfer, but most selection and analysis of model parameters are empirical and qualitative. This paper has proposed a method of quantitatively identifying the most influential parameters of Common Land Model through Sobol’ sensitivity analysis. Considering sensible heat flux as the model output, the first order and total sensitivity indices of 25 model input parameters are estimated using an improved Sobol’ method. The simulated results are resampled using a bootstrapping method and the corresponding sensitivity indices are calculated. Confidence intervals for the bootstrapping sensitivity indices are estimated by using a percentile method. The results show that the parameters phi0 and porsl are the most important parameters, followed by ref(2,1), tran(2,1) and bsw. Five out of 25 parameters need to have an accurate evaluation, while the other parameters are fixed to a certain value. The sensitivity indices of parameters phi0 and porsl are decreasing after precipitation, while the sensitivity indices of parameters tran(2, 1) and ref(2, 1) are increasing after precipitation.
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References
Choi M, Lee SO, Kwon H (2010) Understanding of the common land model performance for water and energy fluxes in a farmland during the growing season in Korea. Hydrol Process 24(8):1063–1071. doi:10.1002/hyp. 7567
Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrol Process 24(9):1133–1148. doi:10.1002/hyp. 7568
Crucifix M, Betts RA, Cox PM (2005) Vegetation and climate variability: a GCM modelling study. Clim Dyn 24(5):457–467. doi:10.1007/s00382-004-0504-z
Dai Y, Zeng X, Dickinson RE (2001) The common land model: technical documentation and user’s guide
Dai Y, Zeng X, Dickinson RE, Baker I, Bonan GB, Bosilovich MG, Denning AS, Dirmeyer PA, Houser PR, Niu G, Oleson KW, Schlosser CA, Yang Z-L (2003) The common land model. Bull Am Meteorol Soc 84(8):1013–1023. doi:10.1175/bams-84-8-1013
Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, New York
Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52(1):1–17
Ivanovska S, Atanassov E, Karaivanova A (2005) A superconvergent Monte Carlo method for multiple integrals on the grid. LNCS 3516:735–742
Karaivanova A, Dimov I, Ivanovska S (2001) A Quasi-Monte Carlo method for integration with improved convergence. LNCS 2179:158–165
Kucherenko S, Rodriguez-Fernandez M, Pantelides C, Shah N (2009) Monte Carlo evaluation of derivative-based global sensitivity measures. Reliab Eng Syst Saf 94(7):1135–1148. doi:10.1016/j.ress.2008.05.006
Liang X, Dai Y (2008) A sensitivity study of the common land model on soil texture and soil brightness. Clim Environ Res (in Chinese) 13(5):585–597
Liang X, Wood EF, Lettenmaier DP, Lohmann D, Boone A, Chang S, Chen F, Dai Y, Desborough C, Dickinsonh RE, Duan Q, Ek M, Gusev YM, Habets F, Irannejad P, Koster R, Mitchell KE, Nasonova ON, Noilhan J, Schaake J, Schlosser A, Shao Y, Shmakin AB, Verseghy D, Warrach K, Wetzel P, Xue Y, Yang Z-L, Zeng Q-C (1998) The project for intercomparison of land-surface parameterization schemes (PILPS) phase 2(c) Red-Arkansas river basin experiment: 2. Spatial and temporal analysis of energy fluxes. Glob Planet Chang 19:137–159
Luo S, Lu S, Zhang Y, Hu Z, Ma Y, Li S, Shang L (2008) Simulation analysis on land surface process of BJ site of Central Tibetan Plateau using CoLM. Plateau Meteorol (in Chinese) 27(2):259–271
Mölders N (2005) Plant- and soil-parameter-caused uncertainty of predicted surface fluxes. Mon Weather Rev 133(12):3498–3516
Moulin S, Bondeau A, Delecolle R (1998) Combining agricultural crop models and satellite observations: from field to regional scales. Int J Remote Sens 19:1021–1036
Nossent J, Elsen P, Bauwens W (2011) Sobol’ sensitivity analysis of a complex environmental model. Environ Model Softw 26(12):1515–1525. doi:10.1016/j.envsoft.2011.08.010
Prihodko L, Denning AS, Hanan NP, Baker I, Davis K (2008) Sensitivity, uncertainty and time dependence of parameters in a complex land surface model. Agric For Meteorol 148(2):268–287. doi:10.1016/j.agrformet.2007.08.006
Rosero E, Yang Z-L, Wagener T, Gulden LE, Yatheendradas S, Niu G-Y (2010) Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season. J Geophys Res 115(D3):doi:10.1029/2009jd012035
Rosolem R, Gupta HV, Shuttleworth WJ, Zeng X, de Gonçalves LGG (2012) A fully multiple-criteria implementation of the Sobol’ method for parameter sensitivity analysis. J Geophys Res 117(D7):doi:10.1029/2011jd016355
Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297
Saltelli A, Tarantola S (2002) On the relative importance of input factors in mathematical models: safety assessment for nuclear waste disposal, vol 97. vol 459. American Statistical Association, Alexandria, VA, ETATS-UNIS
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270. doi:10.1016/j.cpc.2009.09.018
Sobol’ IM (1976) Uniformly distributed sequences with an additional uniform property. USSR Comput Maths Math Phys 16:236–242 (in English)
Sobol’ IM (1990) On sensitivity estimation for nonlinear mathematical models. Matematicheskoe Modelirovanie: 2(1):112–118
Sobol’ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55(1–3):271–280
Sobol’ IM (2007) Global sensitivity analysis indices for the investigation of nonlinear mathematical models. Matematicheskoe Modelirovanie 19(11):23–24, in Russian
Sobol’ IM, Tarantola S, Gatelli D, Kucherenko SS, Mauntz W (2007) Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliab Eng Syst Saf 92(7):957–960. doi:10.1016/j.ress.2006.07.001
Sridhar V (2002) Validation of the NOAH-OSU land surface model using surface flux measurements in Oklahoma. J Geophys Res 107(D20):doi:10.1029/2001jd001306
Tang Y, Reed P, Wagener T, Werkhoven KV (2006) Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrol Earth Syst Sci Discuss 3:3333–3395
Xin Y, Bian L, Zhang X (2006) The application of CoLM to Arid region of Northwest China and Qinghai-Xizang Plateau. Plateau Meteorol (in Chinese) 25(4):567–574
Yang J (2011) Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environ Model Softw 26(4):444–457. doi:10.1016/j.envsoft.2010.10.007
Acknowledgments
We thank the anonymous reviewers for their valuable suggestions to improve this paper. This work is jointly supported by the Research Foundation for the Excellent State Key Lab from the National Natural Science Foundation of China (No. 41023001), the National Basic Research Program of China (No. 2011CB707103), the National Natural Science Foundation of China (No. 41171313) and the Ph.D. Research Fund of Wuhan University (NO. 2012619020202).
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Communicated by: H. A. Babaie
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Xu, J., Shu, H., Jiang, H. et al. Sobol’ sensitivity analysis of parameters in the common land model for simulation of water and energy fluxes. Earth Sci Inform 5, 167–179 (2012). https://doi.org/10.1007/s12145-012-0105-z
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DOI: https://doi.org/10.1007/s12145-012-0105-z