HUP-BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting
Bibliographic record
Abstract
Uncertainty quantification and providing probabilistic streamflow forecasts are of particular interest for water resource management. The hydrologic uncertainty processor (HUP) is a well-known Bayesian approach used to quantify hydrologic uncertainty based on observations and deterministic forecasts. This uncertainty quantification is model-specific; however, utilizing information from multiple hydrologic models should be advantageous and should lead to better probabilistic forecasts. Using seven, structurally different, conceptual models, this study first aims at evaluating the effects of implementing different hydrologic models on HUP performance. Second, using the concepts of the Bayesian Model Averaging (BMA) approach, a multimodel HUP-based Bayesian postprocessor (HUP-BMA) is proposed where the combination of posterior distributions derived from HUP with different hydrologic models are used to better quantify the hydrologic uncertainty. All postprocessing approaches are applied for medium-range daily streamflow forecasting (1–14 days ahead) in two watersheds located in Ontario, Canada. The results indicate that the HUP forecasts for short lead-times are negligibly affected by implementing different hydrologic models, while with increasing lead-time and flow magnitude, they significantly depend on the quality of the deterministic forecast. Moreover, the superiority of the proposed HUP-BMA method over HUP is demonstrated based on various verification metrics in both watersheds. Additionally, HUP-BMA outperformed the original BMA in quantifying hydrologic uncertainty for short lead-times. However, by increasing lead-time, considering the effects of initially observed flow on HUP-BMA formulation may not be beneficial. So, its modified version unconditioned on initial observations is preferred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".