Analysing Deep Percolation Dynamics: A Lysimeter‐Based Study in a Cold Environment
Bibliographic record
Abstract
ABSTRACT Understanding deep percolation dynamics is crucial for effective groundwater management. This is particularly important in cold regions significantly affected by soil freezing and snow. The scarcity of direct measurements and the insufficient focus on cold regions has limited previous research. This study addresses these gaps by measuring deep percolation by placing four drainage lysimeters inside three large experimental plots in Québec, Canada. The study classified the 67 measured percolation events into two categories: those starting in the cold (36) and warm (31) seasons. Cold‐season events were shorter but had considerably higher peak and average intensities than warm‐season events. Cold‐season events were mostly triggered by rain‐on‐snow and significantly influenced by soil freezing. The study conducts partial correlation analyses to examine the effects of four variables on the volume of percolation events. The variables included the volume and intensity of input water (rainfall and snowmelt), antecedent soil moisture, and soil temperature. The results demonstrate that input water, influenced predominantly by seasonal precipitation and snowmelt, is the principal driver of variations in percolation volumes, with partial correlation coefficients (rp) of 0.90 and 0.59 concerning the warm‐season and cold‐season events, respectively. The results also show that the volume of warm‐season percolation events was negatively correlated to rainfall intensity, with rp = −0.52. The findings have important implications for improving hydrological modelling and water management in cold regions. They emphasise the need to consider the dominant roles of rainfall characteristics (volume and intensity) and snowmelt in driving the variation in deep percolation, particularly in the face of changing climatic patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".