A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins
Bibliographic record
Abstract
The transboundary Imjin River basin, Korea. The primary aim is to propose and validate a novel framework for assessing the uncertainty in hydrological models, particularly rainfall–runoff models (RRMs), considering transboundary river basins with limited data accessibility. By utilizing an adaptive Markov chain Monte Carlo (MCMC) simulation method combined with three comprehensive uncertainty assessment measures, the developed framework focuses on evaluating the uncertainty inherent in RRMs. A key component of this framework is the delayed rejection adaptive Metropolis (DRAM) algorithm, which is employed to explore behavioral simulations defined by four likelihood functions (LFs). The proposed methodology was applied to the transboundary Imjin River basin using the Sejong University rainfall–runoff (SURR) model, a case study that involves a database of five-year extreme flood events. The application of this framework in the transboundary Imjin basin demonstrated its effectiveness in quantifying and addressing the uncertainty in RRM predictions. The integration of the DRAM algorithm with uncertainty indices provided a robust mechanism for evaluating and improving the reliability of RRM outputs for transboundary basins. Effects of LFs in blending with the DRAM algorithm were confirmed by uncertainty measures and the behavior of the upper and lower uncertainty bounds. These insights could provide an approach to develop more accurate and reliable water resource management strategies in global transboundary contexts. • Propose a novel framework to evaluate the RRM uncertainty for transboundary rivers. • The framework integrates adaptive MCMC method, LFs, and three uncertainty indexes. • Distance between 95PCI and observation was first used in RRM uncertainty analyses. • Hydrologic stations with dissimilar input cases were analyzed for their uncertainty.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".