Overview
This Special Issue of Socio-Environmental Systems Modeling brings together current advances in global sensitivity analysis (GSA) and its application to scientific, engineering, and policy-related modeling. GSA plays a vital role in model development, calibration, validation, robustness analysis, and decision making under uncertainty. Its use is increasingly recommended by regulatory bodies such as the U.S. Environmental Protection Agency, the Nuclear Regulatory Commission, and the European Commission. The six papers selected for this issue reflect both theoretical developments and applied innovations. Clouvel et al. provide a comparative study of variance-based importance measures in linear regression, bridging traditional statistics and sensitivity analysis. Duan et al. introduce a Shapley value approach for sensitivity analysis of models with dependent inputs based on derivative based global sensitivity measures. Katarina et al. perform GSA on a distributed hydrological model, combining principal component analysis with polynomial chaos expansion to handle spatio-temporal outputs. Lamboni introduces new kernel-based sensitivity indices that extend traditional sensitivity analysis methods to handle complex, non-linear, and structured model behaviors using multivariate weighted distributions and sensitivity functionals. Mamnun et al. apply GSA to an ocean biogeochemical model, offering insights into parameter influence on key marine processes. Sun et al. review convergence assessment methods of GSA within environmental modeling and discuss how to develop customized approaches for effective convergence assessment. Collectively, these works demonstrate the growing maturity and interdisciplinary relevance of global sensitivity analysis in socio-environmental modeling.
This Special Issue remains open to submissions.
Guest Editor
Giray Ökten is a Professor of Mathematics at Florida State University, where he also serves as Associate Chair for Graduate Studies. He holds a Ph.D. in Mathematics from Claremont Graduate University. His research spans numerical analysis, Monte Carlo methods, global sensitivity analysis, and financial mathematics. He serves as guest editor for this special issue on Sensitivity Analysis of Model Output.
Special Issue Papers
Clouvel, L., Iooss, B., Chabridon, V., Il Idrissi, M., & Robin, F. (2025). An overview of variance-based importance measures in the linear regression context: comparative analyses and numerical tests. Socio-Environmental Systems Modelling, 7, 18681. https://doi.org/10.18174/sesmo.18681
Duan, H., & Okten, G. (2023). Control variate Monte Carlo estimators based on sparse polynomial chaos expansions. Socio-Environmental Systems Modelling, 5, 18568. https://doi.org/10.18174/sesmo.18568
Lamboni, M. (2023). Kernel-based sensitivity indices for any model behavior and screening. Socio-Environmental Systems Modelling, 5, 18566. https://doi.org/10.18174/sesmo.18566
Mamnun, N., Völker, C., Krumscheid, S., Vrekoussis, M., & Nerger, L. (2023). Global sensitivity analysis of a one-dimensional ocean biogeochemical model. Socio-Environmental Systems Modelling, 5, 18613. https://doi.org/10.18174/sesmo.18613
Radišić, K., Rouzies, E., Lauvernet, C., & Vidard, A. (2023). Global sensitivity analysis of the dynamics of a distributed hydrological model at the catchment scale. Socio-Environmental Systems Modelling, 5, 18570. https://doi.org/10.18174/sesmo.18570
Sun, X., Jakeman, A. J., Croke, B. F., Roberts, S. G., & Jakeman, J. D. (2024). Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence. Socio-Environmental Systems Modelling, 6, 18678. https://doi.org/10.18174/sesmo.18678