Bibliographic record
Abstract
Abstract. Groundwater recharge (GWR) is a strategic hydrologic variable, and its estimate is necessary to implement sustainable groundwater management. This is especially true in a global warming context that highly impacts key winter conditions in cold and humid climates. For this reason, long-term simulations are particularly useful for understanding past changes in GWR associated with changing climatic conditions. However, GWR simulation at the regional scale and for long-term conditions is challenging, especially due to the limited availability of spatially distributed calibration data and due to generally short observed time series. The objective of this study is to demonstrate the relevance of using a water budget model to understand long-term transient and regional-scale GWR in cold and humid climates where groundwater observations are scarce. The HydroBudget model was specifically developed for regional-scale simulations in cold and humid climate conditions. The model uses commonly available data such as runoff curve numbers to describe the study area, precipitation and temperature time series to run the model, and river flow rates and baseflow estimates for its automatic calibration. A typical case study is presented for the southern portion of the Province of Quebec (Canada, 36 000 km2). With the model simultaneously calibrated on 51 gauging stations, the first GWR estimate for the region was simulated between 1961 and 2017 with very little uncertainty (≤ 10 mm/yr). The simulated water budget was divided into 41 % runoff (444 mm/yr), 47 % evapotranspiration (501 mm/yr), and 12 % GWR (139 mm/yr), with preferential GWR periods during spring and winter (44 % and 32 % of the annual GWR, respectively), values that are typical of other cold and humid climates. Snowpack evolution and soil frost were shown to be a key feature for GWR simulation in these environments. One of the contributions of the study was to show that the model sensitivity to its parameters was correlated with the average air temperature, with colder watersheds more sensitive to snow-related parameters than warmer watersheds. Interestingly, the results showed that the significant increase in precipitation and temperature since the early 1960s did not lead to significant changes in the annual GWR but resulted in increased runoff and evapotranspiration. In contrast to previous studies of past GWR trends in cold and humid climates, this work has shown that changes in past climatic conditions have not yet produced significant changes in annual GWR. Because of their relative ease of use, water budget models are a useful approach for scientists, modelers, and stakeholders alike to understand regional-scale groundwater renewal rates in cold and humid climates, especially if they can be easily adapted to specific study needs and environments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.174 | 0.102 |
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".