VISCOUS: A Variance-Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes
Bibliographic record
Abstract
Global Sensitivity Analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests, among others. Nevertheless, computationally efficient methodologies are sorely needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time is often too high and is typically beyond the availability for a comprehensive GSA. We overcome this computational barrier by developing an efficient variance-based sensitivity analysis using copulas. Our data-driven method, called VISCOUS, approximates the joint probability density function of the given set of input-output pairs using Gaussian mixture copula to provide a given-data estimation of the sensitivity indices. This enables our method to identify dominant hydrologic factors by recycling pre-computed set of model evaluations or existing input-output data, and thus avoids augmenting the computational cost. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of the proposed method. Our results confirm that VISCOUS and the original variance-based method can detect similar important and unimportant factors. However, while being robust, our method can substantially reduce the computational cost. The results here are particularly significant for, though not limited to, process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".