Seasonal variations and long-term trends of groundwater over the Canadian landmass
Bibliographic record
Abstract
Abstract Detailed knowledge of groundwater storage improves the understanding and management of water resources. Observations from Gravity Recovery and Climate Experiment (GRACE) satellites have provided data on global terrestrial-water-storage (TWS) changes since 2002. Combining GRACE-TWS and land-surface model (LSM) estimates of soil water, snow-water equivalent and surface-water storage provides a method to quantify groundwater storage ( W ground ). This study examines the W ground seasonal variations and trends for Canada’s landmass during the period 2003–2016 using GRACE-TWS and the Canadian LSM EALCO (Ecological Assimilation of Land and Climate Observations) model. The results show the study region has a maximum seasonal variation (Δ W ground ) of 118 mm (volume equivalent 700 km 3 ), with the maximum/minimum W ground appearing in July/April. Eastern Canada has relatively large Δ W ground values, up to 400 mm in Newfoundland. The Prairie region has the smallest value (<50 mm). The western and central regions show the maximum/minimum W ground mostly in spring/fall. In contrast, eastern Canada has the maximum/minimum W ground mostly in fall/spring. South Ontario and the Prairie area show the maximum/minimum W ground in summer/winter. Additionally, the W ground trends over the 14-year study period present large spatial variability, with increasing trends of up to 10 mm/year in eastern Canada and decreasing trends (similar magnitudes) in the west. The increasing trend largely offsets the decreasing trend in the study area, and the overall W ground for the region does not show a significant trend during 2003–2016. Comparison of W ground with groundwater well measurements present similar long-term trends but with a phase difference in seasonal variations.
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.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".