Early Warning Indicators of Groundwater Drought in Mountainous Regions
Bibliographic record
Abstract
Abstract Aquifers in mountainous regions are susceptible to drought. However, the diverse hydroclimatology, the small and responsive aquifers, and the varied nature of interactions between groundwater and surface water lead to complex groundwater level responses that are challenging to interpret for understanding groundwater drought. In this study, generalized additive models (GAMs) are used to explore the sensitivity of summer groundwater levels to various climate and hydrological predictor variables (indicators) in each of a snowmelt‐ and rainfall‐dominated hydroclimatic regime in British Columbia, Canada. A sensitivity analysis explores individual seasonal predictor variables, station versus gridded climate data, teleconnection indices, and time series length. GAMs are then generated for different combinations of predictor variables to identify the best combination for each region. In the snowmelt‐dominated regime, maximum spring temperature, maximum snow water equivalent, and the winter Nino 3.4 index is the best combination of variables for predicting summer groundwater levels. In the rainfall‐dominated regime, maximum spring temperature, winter precipitation, and spring streamflow is the best combination. The unique combinations of predictor variables for each region can be used as early warning indicators for groundwater drought preparedness by water managers prior to the beginning of the summer. The Standardized Groundwater Level Index (SGI) is also effective at indicating which wells had pronounced responses to periods of drought in each region. However, the SGI differed among aquifers of similar type, suggesting other factors such as aquifer response mechanism and groundwater pumping may have an important influence on the SGI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".