Water storage variations in the U.S. Great Lakes watershed inferred from GPS and GRACE and connections with ENSO and NAO
Bibliographic record
Abstract
Assessing spatiotemporal water storage variability in the Great Lakes watershed (GLW) is critical for water resources in the US and Canada. Here, we assess terrestrial water storage (TWS) variations in the GLW by using data from global positioning system (GPS), GRACE satellites and a composite hydrological model (CHM), and examine the relationship between TWS and climate changes, such as ENSO, NAO etc. Observations show a surface water storage increase of ~175 km3 from 2010 to 2020, however, Earth's crust subsidence caused by this water increase alone cannot explain the GPS recorded vertical displacement, suggesting additional contributions need to be explored.The spatial pattern in annual change of GPS-inverted TWS agrees well with that from the CHM, indicating that the annual cycle mainly dominated by soil moisture and snow. GRACE underestimates the spatial patterns of long-term and seasonal TWS fluctuations w.r.t GPS. The peak in GPS-inverted TWS occurs in March, coinciding with those from GPS, however, one to two months ahead of that appear in the CHM. The magnitude of seasonal groundwater oscillation is ~60 km3 with peaking in September, coinciding with the Great Lakes surface water peak; however, the magnitudes and phases of groundwater storage vary markedly among the sub-regions in the GLW. ENSO and NAO have impacts on GLW TWS variations at interannual scale through effecting the regional precipitation and temperature. The high-density GPS stations in the GLW represent an independent tool to estimate high-resolution TWS changes and provide critical insights for understanding water storage variations.
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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 | 0.002 |
| 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) | 0.001 | 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".