Global sensitivity analysis of yield output from the water productivity model

Vanuytrecht, Eline; Raes, Dirk; Willems, Patrick

doi:10.1016/j.envsoft.2013.10.017

Cited by 152 publications

(116 citation statements)

References 40 publications

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“…However, to the authors' knowledge this choice seems to be motivated mainly by the need of keeping the total number of model evaluations limited rather than by a formal assessment of the reliability of the results. For example, Campolongo and Saltelli (1997) show that, with r ¼ 10, the confidence bounds of the sensitivity indices obtained by bootstrapping are so large that factor ranking is essentially meaningless; Vanuytrecht et al (2014) compute the EET sensitivity indices using an increasing number of samples and conclude that r ¼ 25 is sufficient to discriminate between influential and non-influential factors (screening) while it is still not sufficient to stabilize factor ranking.…”

Section: Choose the Sample Sizementioning

confidence: 99%

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Pianosi

Wagener

Rougier

et al. 2014

Vulnerability, Uncertainty, and Risk

255

416

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a b s t r a c tSensitivity 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.

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Section: Choose the Sample Sizementioning

confidence: 99%

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Pianosi

Wagener

Rougier

et al. 2014

Vulnerability, Uncertainty, and Risk

255

416

View full text Add to dashboard Cite

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“…(1) is to use the eFAST method, first developed by Saltelli et al (1999) and widely used since Koehler and Owen, 1996;Queipo et al, 2005;Saltelli et al, 2008;Vanuytrecht et al, 2014;Vu-Bac et al, 2015). A multi-dimensional Fourier transformation of the simulator f allows a variance-based decomposition that samples the input space along a curve defined by…”

Section: The Extended Fast Methodsmentioning

confidence: 99%

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

et al. 2018

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Abstract. Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input -the percentage of the total variability in the output attributed to the changes in that input -by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5-40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator-PCA and GAM methods accurately estimated the SIs of the ∼ 2000 methane lifetime outputs but were on average 24 and 37 times faster, respectively.

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“…The latter can be significant since low-impact modelled outputs may be converted to fixed values or dropped to simplify the model, reducing the required computing power. It may also be possible to concentrate on high-impact parameters during calibration or when offering guidance to the design of experimental programs for more efficient model coding [1,23,27]. SA methods can be categorised as either local (LSA) or global (GSA).…”

Section: Introductionmentioning

confidence: 99%

“…With this simplification, the difficulties mentioned above should be easier to overcome. In general terms, SA contains mathematical approaches used to quantify the relative influence of each input parameter on the model's output variability [22,23]. It allows for an objective assessment of model structure and coherence, and also a quantitative evaluation of the influence of each parameter on model performance [1,3].…”

Section: Introductionmentioning

confidence: 99%

Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

Petropoulos

Ireland

Griffiths

et al. 2015

Water

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In today's changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earth's terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalisation of any physically based model. A Global Sensitivity Analysis (GSA) was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs) for the model inputs, when examining sensitivity of key quantities simulated by SimSphere at different output times, were examined. The development of topographic model input parameters (e.g., slope, aspect, and elevation) were derived within a Geographic Information System (GIS) before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, fractional vegetation cover, vegetation height and surface moisture availability in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values OPEN ACCESSWater 2015, 7 2102 of the output variance decomposition, but it did not seem to change the relative importance of each input parameter. Sensitivity Analysis (SA) results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling and educational tool. Furthermore, this study is very timely given on-going efforts towards the development of operational products based on the synergy of SimSphere with Earth Observation (EO) data. In this context, results also provide additional support for the potential applicability of the assimilation of spatial analysis data derived from GIS and EO data into an accurate modelling framework.

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Global sensitivity analysis of yield output from the water productivity model

Cited by 152 publications

References 40 publications

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

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