Sensitivity Analysis Methods

  • Yanjun GanEmail author
  • Qingyun Duan
Reference work entry


Sensitivity analysis (SA) is an important tool for assessing and reducing uncertainties in computer-based models. This chapter presents a comprehensive review of some commonly used SA methods, including gradient-based, variance-based, and regression-based methods. Features and applicability of those methods are described and illustrated with some examples. Merits and limitations of different methods are explained, and the criteria of choosing appropriate SA methods for different applications are suggested.


Uncertainty quantification Sensitivity analysis Uncertainty analysis Parameter estimation Design of experiment Sampling Parameter screening Variance decomposition 



This study was supported by the National Natural Science Foundation of China (41505092) and National Key Research and Development Program of China (2017YFC1404000).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina

Section editors and affiliations

  • Dmitri Kavetski
    • 1
  • Kuolin Hsu
    • 2
  • Yuqiong Liu
    • 3
  1. 1.School of Civil, Environmental and Mining Engineering, University of AdelaideAdelaideAustralia
  2. 2.Civil & Environmental Engineering, The Henry Samueli School of Engineering, University of CaliforniaIrvineUSA
  3. 3.NASA Goddard Space Flight CenterWashington D.C.USA

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