Spatiotemporal groundwater modeling for hazard analyses in the San Francisco Bay region
Bibliographic record
Abstract
Many hazards, including precipitation-induced landslides and coseismic liquefaction, are strongly influenced by the variability of groundwater levels. Simplified or coarse-resolution groundwater models are available for regional-scale studies of infrastructure networks; however, these models often do not consider spatial and temporal variations observed within wells and may not provide sufficient local resolution for critical hazard studies. We extend a conventional, physics-based groundwater model to include spatial and temporal variability based on Gaussian process (GP) interpolation to better understand the local and temporal variation of groundwater between well observations. In this probabilistic model, the physics-based groundwater elevation model serves as an ergodic function and the GP interpolation serves as a model of the well observation residuals. We demonstrate the applicability and accuracy of the model by developing phreatic groundwater estimates for an approximately 10,000 km2 area surrounding San Francisco Bay, California, USA. The resulting model accurately estimates the seasonally variable groundwater depth in a blind holdout dataset within 1.11 m with 90% confidence. The model is well constrained with a standard deviation of approximately 1.1 m near wells, but the uncertainty increases dramatically in mountainous terrain where well observations are limited. The model results also indicate that the average seasonal variability is typically modest relative to non-seasonal events, but nonetheless could have significant impacts on hazard evaluations such as earthquake-induced liquefaction or shallow slope instability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".