Groundwater Responses to Deluge and Drought in the Fraser Valley, Pacific Northwest
Bibliographic record
Abstract
Extreme weather events are reshaping hydrological cycles across the globe, yet our understanding of the groundwater response to these extremes remains limited. Here we analyze groundwater levels across the South Coast of British Columbia (BC) in the Pacific Northwest with the objective of determining groundwater responses to atmospheric rivers (ARs) and drought. An AR catalogue was derived and associated to local rainfall defining extreme precipitation. Droughts were quantified using dry day metrics, in conjunction with the standardized precipitation index (SPI). From September to January, approximately 40% of total precipitation is contributed by ARs. From April to September, more than 50% of days receive no precipitation, with typically 26 consecutive dry days. We used the autocorrelation structure of groundwater levels to quantify aquifer memory characteristics and identified two distinct clusters. Cluster 1 wells respond to recharge from local precipitation, primarily rainfall, and respond rapidly to both ARs during winter recharge and significant rainfall deficits during summer. Cluster 2 wells are also driven by local precipitation, and are additionally influenced by the Fraser River’s large summer freshet, briefly providing a secondary recharge mechanism to South Coast aquifers. Accordingly, groundwater recessions are offset to later in the summer, contingent on the Fraser River, mediating drought. The results suggest that groundwater memory encapsulates multiple hydrogeological factors, including boundary conditions, influencing the response outcome to extreme events.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".