Modeling Hydrological Inflow Persistence Using Paleoclimate Reconstructions on the Québec‐Labrador (Canada) Peninsula
Bibliographic record
Abstract
Abstract Annual inflow forecasts are often based on historical time series, where every year is considered equally likely to reoccur. This process ignores the persistence of dry/wet conditions often observed in time series, behavior that is of utmost importance for hydroelectric energy producers. However, the modeling of persistence properties is challenging when only short time series are available for calibration. Here, we use Gaussian hidden Markov models to describe the regime‐switching behavior, where the next year's inflow depends on the current estimated regime. For four large hydropower reservoirs on the Québec‐Labrador Peninsula, a Gaussian hidden Markov model is calibrated on both a 30‐year observational record and a 190‐year paleoclimatic inflow reconstruction. Each reconstruction is a composite of three reconstruction methods drawing on five different tree‐ring proxies (ring widths, minimal wood density, maximal wood density,δ13C, andδ18O). The calibration on the reconstructed series finds two hydrological regimes, while the calibration on the observed data has only one regime for three out of four watersheds. Yearly hindcasts with the two calibrated Gaussian hidden Markov models suggest that for all four watersheds, extending the time series with reconstructions improves the model's predictive accuracy. This approach does not explicitly account for the differing accuracy of the observational and reconstructed time series or compare hidden Markov models to other models of persistence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".