Using a water budget model to anticipate the impact of climate change on groundwater recharge at the regional scale in cold and humid climates - example of southern Quebec (Canada)
Bibliographic record
Abstract
In cold and humid climates, rivers and superficial water bodies are often fed by groundwater with relatively constant inflows that are most visible during the summer (limited net precipitation) and the winter (limited runoff and infiltration). The harsh winter – short growing season succession could be drastically affected by climate change. Although water is abundant, extreme low flows are expected in the near future, most likely due to warmer summer temperatures, increased summer PET and possible lower summer precipitation. It is thus crucial to provide stakeholders with scenarios of future groundwater recharge (GWR) to anticipate the impacts of climate change on groundwater resources at the regional scale. This study aims to test the contributions of a superficial water budget model to estimate the impact of climate change on the regional GWR. The methodology is tested in a forested and agricultural region of southern Quebec, located between the St. Lawrence River and the Canada-USA border, and between the Quebec-Ontario border and Quebec City (36,000 km²). Scenarios of GWR for the region are simulated with the HydroBudget model, performing a transient-state spatialized superficial water budget, and 12 climate scenarios (RCP 4.5 and 8.5, 1951-2100 period). The model was previously calibrated in the study area for the 1961-2017 period and provides spatially distributed runoff, actual evapotranspiration, and GWR fluxes at a 500 x 500 m resolution with a monthly time step. Climate scenarios show warming of the annual temperature from +2 to +5°C and up to 20% increase of annual precipitation at the 2100 horizon compared to the 1981-2010 reference period. By the end of the century, the number of days above 0°C could double between November and April, dividing by almost two the quantity of snow during winter. The clear trends of warming temperature leads to a clear actual evapotranspiration (AET) increase while the increasing variability in annual precipitation translates into more variable annual runoff and GWR. Although no annual GWR decrease is simulated, an increase of winter GWR (up to x2) is expected, linked to warmer winters and unfrozen soils, followed by a decrease for the rest of the year, linked to a longer growing season producing higher AET rates. Although simple in its simulation process, the use of a superficial water budget model simulating soil frost provides new insights into the possible future trends in the different hydrologic variables based on a robust understanding of past condition. Aside from providing scenarios of spatialized GWR (also runoff and AET) at the 2100 horizon for a large region, this study shows that a simple water budget model is an appropriate and affordable tool to provide stakeholders with useful data for water management in a changing climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".