Mapping groundwater storage variations with GRACE: a case study in Alberta, Canada
Bibliographic record
Abstract
The applicability of the Gravity Recovery and Climate Experiment (GRACE) to adequately represent broad-scale patterns of groundwater storage (GWS) variations and observed trends in groundwater-monitoring well levels (GWWL) is examined in the Canadian province of Alberta. GWS variations are derived over Alberta for the period 2002–2014 using the Release 05 (RL05) monthly GRACE gravity models and the Global Land Data Assimilation System (GLDAS) land-surface models. Twelve mean monthly GWS variation maps are generated from the 139 monthly GWS variation grids to characterize the annual GWS variation pattern. These maps show that, overall, GWS increases from February to June, and decreases from July to October, and slightly increases from November to December. For 2002–2014, the GWS showed a positive trend which increases from west to east with a mean value of 12 mm/year over the province. The resulting GWS variations are validated using GWWLs in the province. For the purpose of validation, a GRACE total water storage (TWS)-based correlation criterion is introduced to identify groundwater wells which adequately represent the regional GWS variations. GWWLs at 36 wells were found to correlate with both the GRACE TWS and GWS variations. A factor f is defined to up-scale the GWWL variations at the identified wells to the GRACE-scale GWS variations. It is concluded that the GWS variations can be mapped by GRACE and the GLDAS models in some situations, thus demonstrating the conditions where GWS variations can be detected by GRACE in Alberta.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".