Changes of the streamflow of northern river basins of Siberia and their teleconnections to climate patterns
Bibliographic record
Abstract
Abstract The Arctic rivers contribute more than one‐third of the total freshwater streamflow into the Arctic Ocean and play an essential role in the heat and mass circulation of the Arctic atmosphere/ocean system. As the Arctic is warming faster than the global average, the streamflow from Arctic basins increases. This study analyzed the streamflow of the three largest Siberian rivers: the Lena, Yenisei, and Ob', at multiple temporal scales. Results show that the annual streamflow of each river basin exhibits statistically significant increasing trends, while the seasonal streamflow of sub‐basins generally decreases in the summer but increases in the winter. Both autocorrelation and long‐term persistency are often found in the streamflow time series, which indicates significant changes in the large‐scale climatological environment. Therefore, wavelet coherence between the streamflow and large‐scale climate patterns, including the El Niño–Southern Oscillation (ENSO), North Pacific pattern (NP), Arctic Oscillation (AO), and the Pacific/North America Pattern (PNA), have been conducted. NP and ENSO are found to have positive relationships with the precipitation and the ratio of potential evapotranspiration over the precipitation. AO and the PNA are found to have positive relationships with the streamflow of the Ob' and Yenisei rivers at decadal and multidecadal scales. This study demonstrates that the existence of nonstationarities within the Siberian streamflow as the combined impact of climate change alters the hydroclimatological and terrestrial environment of Siberia. These findings provide new insights into the mechanisms underlying the hydrologic changes to warming trends and oscillations of climate patterns, which contribute to our understanding and the prediction of streamflow of these northern rivers.
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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".