Pacific Ocean <scp>SST</scp> and <scp>Z<sub>500</sub></scp> climate variability and western U.S. seasonal streamflow
Bibliographic record
Abstract
ABSTRACT The current study focuses on evaluating the relationship between the Pacific Ocean climate variability and western US streamflow for six hydrologic regions of the western United States, as defined by United States Geological Survey: Rio Grande, Upper Colorado, Lower Colorado, Great Basin, Pacific Northwest, and California. The singular‐valued decomposition (SVD) technique was applied on data for 50 years (1960–2010) of sea‐surface temperatures (SST), geopotential height index of 500 mbar (Z500), and 90 unimpaired western US streamflows; the results established a spatio‐temporal association for each major hydrologic region in the western United States with Pacific Oceanic variability. An approach using a 3‐ to 9‐month lead time was utilized, i.e. the previous year's July to August SST/Z500, the previous year's October to December SST/Z500 to predict streamflow for current year spring–summer (April to September), spring (April to June), and summer (July to September) seasons. Significant regions in the Pacific were identified that influence hydrology of the western United States. The traditional El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation regions were identified along with regions over eastern Russia, Canadian British Columbia, and the ‘Hondo’ region along the east coast of Japan. Z500 showed pronounced association with 3‐month lead time streamflow, whereas SST had better association with 6‐month lead time streamflow. The SVD results showed improvement in correlation values of smaller spatial regions over larger regions, and a lagged response of adjacent hydrologic regions to the same physical indicators. The results obtained in this study could be helpful in improving the current forecasting models for water management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".