Examining ground and surface water changes in response to environmental variables, land use dynamics, and socioeconomic changes in Canada
Bibliographic record
Abstract
Canada's abundant and high-quality water resources support a growing human population, as well as thriving industrial and agricultural economies. However, recent intense drought conditions have raised concerns for current water resource availability. Patterns of long-term ground and surface water (GSW) changes, and their response to environmental conditions, land-use dynamics, and socioeconomic changes are not well-understood across this large and diverse country. To address this crucial gap, we identified regions of ground and surface water (GSW) changes in all the Provinces of Canada between 2002 and 2016 from the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets. We explored the relationships between GSW changes and environmental, socioeconomic, and land-use dynamics over time. We found that all the Provinces of Canada gained a net 4.46 mm Liquid Water Equivalent (LWE) per year, equivalent to a total increase of 66.9 mm LWE. GSW increases were significantly associated with the normalized difference vegetation index and evapotranspiration rates. In contrast, GSW declines were significantly related to deforestation rate, urban expansion, and economic development (median household income). Despite apparent widespread post-drought recovery detected from 2002 to 2016, the rapid GSW declines were also observed in almost all of Western Canada and part of Ontario, amounting to a net loss of 66.13 mm. This indicates that a pronounced drought had emerged. It is anticipated that Canada will be experiencing more frequent and severe droughts under ongoing climate change and increasing demand for water resources.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".