Incorporating decadal climate variability information in the operation and design of water infrastructure
Bibliographic record
Abstract
Abstract The high thermal and mechanical inertia of the oceans results in slow changes in sea surface temperatures (SSTs). Changes in SSTs, in turn, can impact atmospheric circulation including water vapor transport, precipitation, and temperatures throughout the world. The Pacific Decadal Oscillation (PDO), the tropical Atlantic SST gradient variability, and the West Pacific Warm Pool are patterns of natural climate variability that tend to persist over decadal time periods. There are current efforts to produce decadal climate predictions, but there is limited understanding if this information can be used in water resources management. Understanding the current state of decadal climate variability (DCV) phenomena and the probability of persisting in that state may be useful information for water managers. This information could improve forecasts that aid operations and short-term planning for reservoir management, domestic and industrial water supplies, flood risk management, energy production, recreation, inland navigation, and irrigation. If conditions indicate a higher likelihood of drought, reservoir managers could reduce flood storage space and increase storage for conservation purposes. Improved forecasts for irrigation could result in greater efficiencies by shifting crops and rotational crop patterns. The potential benefits of using a forecast must be balanced against the risk of damages if the forecast is wrong. Seasonal forecasts using DCV information could also be used to inform drought triggers. If DCV indices indicate that the climate has a higher probability of dry conditions, drought contingency plans could be triggered earlier. Understanding of DCV phenomena could also improve long-range water resources planning. DCV can manifest itself in relatively short-term hydrologic records as linear trends that complicate hydrologic frequency analysis, which has traditionally assumed that hydrologic records are stationary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".