Linking hydrological variations at local scales to regional climatic teleconnection patterns
Bibliographic record
Abstract
Interactions between the land surface and the atmosphere play essential roles in hydrological variations at local scales. Variations of regional climate patterns over preceding years have key effects on the seasonal water and moisture conditions in the following year. The linkage between regional climate and local hydrology is challenging due to scale differences, both spatially and temporally. In this study, multiple hydroclimatic phases were identified to relate climatic teleconnection patterns to hydrological processes in a small headwater basin within Reynolds Creek Experiment Watershed, Idaho, USA. A singular spectrum analysis and a combination of hydrological observations and outputs from a physically based hydrological model were used for this purpose. Results showed that a positive phase of North Atlantic Oscillation (NAO) is more influential than a positive phase of the Pacific North American (PNA) pattern on the observed annual runoff and the modeled rain on snow runoff in the study area. Specifically, we found a 43% and 26% shift below normal in annual runoff and rain on snow runoff from NAO and a 29% and 9% below normal from PNA. More frequent rain on snow events were observed under a positive phase of Antarctic Oscillation, leading to a 45% increase in the rain on snow runoff, which accounts for one-third of the mean annual runoff. A high runoff-to-precipitation ratio was observed in the study area under negative phases of Arctic Oscillation and Sea Surface Temperature in the Niño 3.4 region of the Equatorial Pacific Ocean. A switch in the phase of the teleconnection patterns of NAO and PNA in 2012 was concomitant with a transition from wet to dry conditions in the basin, suggesting the importance of the regional teleconnections in affecting snow and runoff regimes at local scales. The identified hydroclimatic phases can be implemented in operational models to improve uncertainties in hydrological forecasts, climate projections, and water resources planning.
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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 | 0.001 | 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".