Local- and Large-Scale Hydrologic Forecast Merging through Time Series Features–Based Dynamic Weights Estimation Framework
Bibliographic record
Abstract
Abstract Hydrologic forecast merging has the potential to enhance forecast accuracy by reducing uncertainties related to model structures and the spatial scale of river basins. This study explores the benefits of merging local- and large-scale forecasts to improve hydrologic predictions. Using the Repositionable Aerial Vane Environmental Network (RAVEN) modeling platform, we applied the Hydrologiska Byrans Vattenbalansavdelning–Environment Canada (HBV-EC) model in a semidistributed manner over the large Moose River basin (MRB), Northern Ontario, Canada, as the large-scale model, while three conceptual models [Génie Rural à 4 Paramètres Journalier (GR4J), the hydrological model (HYMOD), and SAC-SMA] were calibrated for two small local subbasins within the MRB. Model calibration was performed using 10 years (2012–21) of Canadian Precipitation Analysis (CaPA) data and the dynamically dimensioned search algorithm. Streamflow forecasts were generated using the Global Deterministic Prediction System dataset in real-time forecasting mode. To merge forecasts, we implemented a time series feature (TSF)-based dynamic weighting (TSF-W) approach within a Bayesian model averaging (BMA) framework and assessed performance over different lead times. Results showed that while local models performed better overall [Nash–Sutcliffe efficiency (NSE) > 0.65] than the large-scale model (NSE < 0.50), the latter captured certain hydrograph characteristics more effectively. The TSF-W merged forecasts outperformed the best local-scale model, particularly for low-flow (by 10%–80%) and high-flow (by 5%–28%) conditions and for extended lead times. These findings highlight the advantages of merging forecasts from models operating at different spatial scales using the TSF-W approach, providing operational hydrologists with more accurate and reliable forecasts for improved decision-making.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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".