Mapping groundwater storage variations with GRACE: a case study in Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it