Groundwater recharge over the past 100 years: Regional spatiotemporal assessment and climate change impact over the <scp>Saguenay‐Lac‐Saint‐Jean</scp> region, Canada
Bibliographic record
Abstract
Abstract Proper knowledge of potential groundwater recharge (PGR) and its spatiotemporal distribution are essential for sustainable groundwater management, especially within the context of climate change. Here, a robust GIS‐based water budget framework was developed to estimate PGR at a regional scale and map its spatial distribution. This framework is demonstrated over the Saguenay‐Lac‐Saint‐Jean region (13 200 km2) of Quebec (Canada). The PGR mapping process was based on a model incorporating water budget components. The vertical inflows (VI) include water amounts from rainfall and snowmelt, whereby the latter was assessed using HYDROTEL model. VI were combined with the maximum and minimum temperatures to estimate actual evapotranspiration (AET), while the surface runoff (RuS) was assessed using the curve number method. Field observations of annual variation in temperatures and the water budget components, over a period of 100 years (1910–2009), were used to provide a comprehensive overview of the effects of climate change on PGR. The last 10 years of the observation period (i.e., 2000–2009) indicate that 6% of the study area has PGR rates of 35%–50%. PGR rates of 20%–35% occur in 58% of the study area, while 36% have PGR of 5%–20%. The trend analysis of temperature time series reveals an average of 1.1 ± 0.6°C increase over 100 years. Also, an increase in the water budget components is observed. Despite the increasing trends of RuS and AET, PGR still showed an increasing trend with an average increase of 0.7 ± 0.4 mm/year over the past 100 years. This observation indicates that the increase in VI was enough to compensate for the increases in AET and RuS. This finding of an increasing PGR in the study area provides useful information for future studies focusing on predicting long‐term PGR evolution and for the development of efficient long‐term groundwater management strategies.
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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 | 0.002 | 0.006 |
| 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".