Role of Hydrologic Modeling Goals on Evaluation of Sobol’ Sensitivity Indices for Model Parameters

Abstract Submission: Sensitivity analysis plays a crucial role in hydrologic modeling studies by supporting calibration, uncertainty evaluation, and potential model refinement. In particular, global sensitivity analysis (GSA) methods, which explore parameter impacts across their entire distribution, offer a more holistic understanding of model behavior. Among these, variance-based methods such as Sobol’ analysis are widely recognized for their efficacy in decomposing the output variance into contributions from individual parameters and their interactions.

Sobol’ sensitivity analysis quantifies the relative influence of model parameters through the computation of first and higher-order sensitivity indices, which are useful for identifying influential parameters and enhancing model robustness. However, the analysis is affected by the choice of the model, dimensionality of the parameter space, data availability, and the purpose of the modeling exercise. These factors act in combination to confound sensitivity analyses and subsequent inferences. In this study, authors explore the relative roles of these factors by using the HEC-HMS model over several watersheds and multiple rainfall events, and offer recommendations for efficient evaluation of Sobol’ sensitivity indices. The role of modeling goals in estimating model sensitivity is highlighted through these examples.

Learning Objectives/Expected Outcome (Optional) : Understand the importance of sensitivity analysis in hydrologic modeling, specifically its role in calibration, uncertainty assessment, and model improvement.