Modelling eutrophication in lake ecosystems: A review.

Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.

Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost

Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China

Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs

An efficient Bayesian analytical framework was developed to address the challenges of uncertainty analysis and assess the parameter identification problems of complex water quality models with high-dimensional parameter space. The inclusion of a multi-chain Markov Chain Monte Carlo method and comprehensive global sensitive analysis (GSA) guarantees the results to be robust. A high-frequency synthetic data case study was conducted in the EFDC water quality module including 54 parameters. The comprehensive GSA identified 39 completely or partially sensitive parameters for reducing dimensionality, among which only nine were identifiable without significant bias. The fundamental causes of the parameter identification problem could be traced to the cognitive limitations of the real water quality assessment process instead of data scarcity. The framework is powerful for exploring these limitations, generating reminders for model users to use Bayesian estimates in future forecasts, and providing directions for model developers to perfect a model in future work.

Bayesian framework of parameter sensitivity, uncertainty, and identifiability analysis in complex water quality models

Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake

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Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake