An Uncertainty Quantification Framework for Simulation-based Flood Frequency Analysis
Bibliographic record
Abstract
Flood frequency analysis (FFA) is essential for flood risk management and infrastructure design, yet the uncertainty associated with flood quantile estimates is often poorly characterized or disregarded—especially under data-scarce conditions. Existing uncertainty quantification methods are frequently subjective, overly complex, or impractical for routine engineering use. We introduce a simulation-based uncertainty quantification framework—UQ-flood—that integrates a process-based hydrologic model, a stochastic weather generator, and a residual error model (REM). Designed for annual maxima, the REM accounts for model bias and residual variability, enabling the generation of probabilistic streamflow ensembles tailored to extreme event analysis. We apply UQ-flood to three Canadian watersheds with long streamflow records and contrasting hydroclimatic conditions. We compare its performance against traditional statistical FFA using the Generalized Extreme Value distribution and Bayesian inference. UQ-flood yields flood quantile estimates consistent with long-record statistical methods but with substantially narrower uncertainty bounds. Under short-record conditions (e.g., 30 years), UQ-flood maintains statistically consistent estimates, while statistical FFA produces wide, often impractical uncertainty intervals. Additional experiments reveal that omitting the REM introduces systematic bias in flood magnitude estimates. UQ-flood avoids parametric assumptions about flow distributions, circumvents hydrologic model biases, and is adaptable to data-limited conditions. By explicitly propagating uncertainty from hydrologic simulation to flood quantiles, UQ-flood offers a practical alternative for robust flood risk management, including applications in infrastructure design and floodplain mapping. We recommended integrating residual error models into continuous simulation frameworks to improve bias correction and uncertainty quantification in flood risk estimation.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".