Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

被引:21
作者
Liu, Junzhi [1 ,2 ,3 ,4 ]
Liu, Zhangcong [1 ,3 ,4 ]
Zhu, A-Xing [1 ,3 ,4 ,5 ]
Shen, Fang [1 ,3 ,4 ]
Lei, Qiuliang [2 ]
Duan, Zheng [6 ]
机构
[1] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China
[2] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Nonpoint Source Pollut Control, Beijing, Peoples R China
[3] State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China
[4] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
[5] Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
[6] Tech Univ Munich, Chair Hydrol & River Basin Management, Munich, Germany
基金
中国国家自然科学基金;
关键词
Parameter sensitivity; Extended FAST; Range of parameter variation; Climate condition; CO2; level; CROPPING SYSTEMS; SIMULATION-MODEL; UNCERTAINTY; AUTOCALIBRATION; METHODOLOGY; PARAMETERS; AUSTRALIA; FRAMEWORK; SOFTWARE; DROUGHT;
D O I
10.1016/j.scitotenv.2018.09.254
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. The +/- 30% and +/- 50% perturbations of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g. RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main conclusions were consistent between the results obtained from the +/- 30% perturbation and those obtained the +/- 50% perturbation in this study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters. (c) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:953 / 968
页数:16
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共 40 条
  • [31] Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework
    Soundharajan, B.
    Sudheer, K. P.
    [J]. PADDY AND WATER ENVIRONMENT, 2013, 11 (1-4) : 59 - 71
  • [32] Global warming over the period 1961-2008 did not increase high-temperature stress but did reduce low-temperature stress in irrigated rice across China
    Sun, Wen
    Huang, Yao
    [J]. AGRICULTURAL AND FOREST METEOROLOGY, 2011, 151 (09) : 1193 - 1201
  • [33] Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation
    Tan, Junwei
    Cui, Yuanlai
    Luo, Yufeng
    [J]. EUROPEAN JOURNAL OF AGRONOMY, 2017, 91 : 54 - 62
  • [34] Global sensitivity analysis of outputs over rice-growth process in ORYZA model
    Tan, Junwei
    Cui, Yuanlai
    Luo, Yufeng
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2016, 83 : 36 - 46
  • [35] Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments
    Tao, Fulu
    Roetter, Reimund P.
    Palosuo, Taru
    Hernandez Diaz-Ambrona, Carlos Gregorio
    Ines Minguez, M.
    Semenov, Mikhail A.
    Kersebaum, Kurt Christian
    Nendel, Claas
    Specka, Xenia
    Hoffmann, Holger
    Ewert, Frank
    Dambreville, Anaelle
    Martre, Pierre
    Rodriguez, Lucia
    Ruiz-Ramos, Margarita
    Gaiser, Thomas
    Hohn, Jukka G.
    Salo, Tapio
    Ferrise, Roberto
    Bindi, Marco
    Cammarano, Davide
    Schulman, Alan H.
    [J]. GLOBAL CHANGE BIOLOGY, 2018, 24 (03) : 1291 - 1307
  • [36] Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method
    Wang, Jing
    Li, Xin
    Lu, Ling
    Fang, Feng
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2013, 48 : 171 - 182
  • [37] Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
    Yang, Jing
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2011, 26 (04) : 444 - 457
  • [38] Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model
    Zadeh, Farkhondeh Khorashadi
    Nossent, Jiri
    Sarrazin, Fanny
    Pianosi, Francesca
    van Griensven, Ann
    Wagener, Thorsten
    Bauwens, Willy
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2017, 91 : 210 - 222
  • [39] Testing the simulation capability of APSIM-ORYZA under different levels of nitrogen fertiliser and transplanting time regimes in Korea
    Zhang, Xike
    Lee, Jae-Hong
    Abawi, Yahya
    Kim, Young-ho
    McClymont, David
    Kim, Hee-Dong
    [J]. AUSTRALIAN JOURNAL OF EXPERIMENTAL AGRICULTURE, 2007, 47 (12) : 1446 - 1454
  • [40] Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters
    Zhao, Gang
    Bryan, Brett A.
    Song, Xiaodong
    [J]. ECOLOGICAL MODELLING, 2014, 279 : 1 - 11