Sensitivity analysis of environmental models: A systematic review with practical workflow
By Francesca Pianosi, Keith Beven, Jim Freer, Jim W. Hall, Jonathan Rougier, David B. Stephenson, Thorsten Wagener. Environmental Modelling & Software, 2014
1,428 citations recorded by the source corpus.
Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.We provide a systematic review of existing approaches, which can support users in the choice of an SA method.We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.We give best practice examples from the literature and highlight trends and gaps for future research.
Subfields: Computational Theory and Mathematics, Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b8e732f29380c3f4c7b86b93e8d82ff22fa40324
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Environmental Engineering
- Fanny Sarrazin
- Francesca Pianosi
- Thorsten Wagener
- Thomas V. Schuler
- John F. Burkhart
- Aynom T. Teweldebrhan
- Hoshin V. Gupta
- Jasper A. Vrugt
In Statistics and Probability
- Fanny Sarrazin
- Thorsten Wagener
- Francesca Pianosi
- Thomas V. Schuler
- John F. Burkhart
- Aynom T. Teweldebrhan
- K. Liechti
- Simpal Kumar
Citations in the corpus
- Global Sensitivity Analysis of environmental models: Convergence and validation (2016, methodology): “SA is a diagnostic tool that can guide model calibration and veri fi cation, support the prioritization of efforts for uncertainty reduction, or help with model-based decision-making (Norton, 2015; Pianosi et al., 2016; Song et al., 2015).”
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, neutral): “…as documented in the recent reviews by Norton (2015), Iooss and Lemaître (2015), Wei et al. (2015), Razavi and Gupta (2015), Borgonovo and Plischke (2016), Pianosi et al. (2016), Borgonovo et al. (2017), Borgonovo (2017), Ghanem et al. (2017), Gupta and Razavi (2017) and Saltelli et al. (2019).”
- Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “An all-at-a-time (AAT) sampling method (e.g., Pianosi et al., 2016) was employed.”
- Tree vitality indicators revealed a rapid response of beech forests to the 2018 drought (2021, methodology): “All potential explanatory variables were standardized to allow the estimated regression coefficients to function as sensitivity measures (Pianosi et al., 2016).”