Recent Changes in Groundwater and Surface Water in Large Pan-Arctic River Basins
Bibliographic record
Abstract
Surface and groundwater in large pan-Arctic river basins are changing rapidly. High-quality estimates of these changes are challenging because of the limits on the data quality and time span of satellite observations. Here, the term pan-Arctic river refers to the rivers flowing to the Arctic Ocean basin. In this study, we provide a new evaluation of groundwater storage (GWS) changes in the Lena, Ob, Yenisei, Mackenzie and Yukon River basins from the GRACE total water storage anomaly product, in situ runoff, soil moisture form models and a snow water equivalent product that has been significantly improved. Seasonal Trend decomposition using Loess was utilized to obtain trends in GWS. Changes in surface water (SW) between 1984 and 2019 in these basins were also examined based on the Joint Research Centre Global Surface Water Transition data. Results suggested that there were great GWS losses in the North American river basins, totaling approximately −219 km3, and GWS gains in the Siberian river basins, totaling ~340 km3, during 2002–2017. New seasonal and permanent SWs are the primary contributors to the SW transition, accounting for more than 50% of the area of the changed SW in each basin. Changes in the Arctic hydrological system will be more significant and various in the case of rapid and continuous changes in permafrost.
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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 | 0.002 |
| 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) | 0.000 | 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".