Combining Multiple Groundwater Hydrograph Analyses to Characterize Aquifer Dynamics and Drivers in Complex Hydrogeological Settings
Bibliographic record
Abstract
Groundwater level fluctuations reveal key aspects of aquifer behavior, including recharge timing, storage dynamics, and sensitivity to hydroclimatic forcing. However, in geologically heterogeneous and seasonally variable regions, interpreting these fluctuations remains challenging, and we lack standard approaches for characterizing aquifers based on groundwater hydrographs. In this study, we analyze 13 years (2011–2023) of daily groundwater level data from 40 provincial observation wells across Nova Scotia, Canada, to characterize spatial and temporal groundwater dynamics. We extracted 17 hydrograph-derived indices for characterizing the hydrographs and aquifers and apply time series decomposition to quantify the magnitude, frequency, and timing of groundwater fluctuations.Most wells exhibited consistent seasonal patterns, with a median annual level range of 0.7 m and a dominant periodicity near 365 days. Seasonal components accounted for an average of 54% of the total variance and were the primary mode of variability in 80% of wells. The timing of annual groundwater maxima clustered in either spring or autumn–winter, depending on whether recharge was predominantly snowmelt- or rainfall-driven. Hydrograph clustering revealed four behavioral groups linked to differences in geology, well depth, and precipitation responsiveness.Recharge estimation using the Water Table Fluctuation Method was applied to 14 seasonally dynamic wells, yielding mean annual recharge rates of 180–250 mm/year. Winter recharge (January–April) accounted for 40% of total recharge but only 32% of annual precipitation, highlighting the efficiency of snowmelt-driven recharge. These findings demonstrate the efficacy of hydrograph-based analysis for understanding aquifer function and seasonal recharge dynamics across complex hydrogeological settings with changing snow-rain partitioning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".