A measure of top-down correlation
Technometrics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Management Option Rank Equivalence (MORE) - A new method of sensitivity analysis for decision-making
Environmental Modelling & Software
Environmental Modelling & Software
Short communication: Sensitivity analysis in fuzzy systems: Integration of SimLab and DANA
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands
Environmental Modelling & Software
Sobol' sensitivity analysis of a complex environmental model
Environmental Modelling & Software
A generic framework for regression regionalization in ungauged catchments
Environmental Modelling & Software
Environmental Modelling & Software
Using model-based geostatistics to predict lightning-caused wildfires
Environmental Modelling & Software
Environmental Modelling & Software
Sensitivity analysis of the MAGFLOW Cellular Automaton model for lava flow simulation
Environmental Modelling & Software
Review: Three complementary methods for sensitivity analysis of a water quality model
Environmental Modelling & Software
Estimating Sobol sensitivity indices using correlations
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
Proceedings of the Winter Simulation Conference
An efficient integrated approach for global sensitivity analysis of hydrological model parameters
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Application of a combined sensitivity analysis approach on a pesticide environmental risk indicator
Environmental Modelling & Software
Environmental Modelling & Software
Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
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Sensitivity analysis plays an important role in model development, calibration, uncertainty analysis, scenario analysis, and, hence, decision making. With the availability of different sensitivity analysis techniques, selecting an appropriate technique, monitoring the convergence and estimating the uncertainty of the sensitivity indices are very crucial for environmental modelling, especially for distributed models due to their high non-linearity, non-monotonicity, highly correlated parameters, and intensive computational requirements. It would be useful to identify whether some techniques outperform others with respect to computational requirements, reliability, and other criteria. This paper proposes two methods to monitor the convergence and estimate the uncertainty of sensitivity analysis techniques. One is based on the central limit theorem and the other on the bootstrap technique. These two methods are implemented to assess five different sensitivity analysis techniques applied to an environmental model. These techniques are: the Sobol' method, the Morris method, Linear Regression (LR), Regionalized Sensitivity Analysis (RSA), and non-parametric smoothing. The results show that: (i) the Sobol' method is very robust in quantifying sensitivities and ranking parameters despite a large number of model evaluations; (ii) the Morris method is efficient to rank out unimportant parameters at a medium cost; (iii) the non-parametric smoothing is reliable and robust in quantifying the main effects and low-order interactions while requiring a small number of model evaluations; finally (iv) the other two techniques, that is, LR and RSA, should be used with care.