Patterns of Intra-Seasonal Groundwater Recharge Inferred from Streamflow and Explained by Vadose Zone Storage Deficits
Bibliographic record
Abstract
In forested, seasonally dry watersheds, winter rains commonly replenish water storage deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface has hindered efforts to include the role of storage deficits, primarily generated by plant-water uptake, in moderating groundwater recharge. Here, we compare groundwater recharge inferred from the storage-discharge relationship with independent, distributed estimates of vadose zone storage deficits across 12 undisturbed California watersheds, thereby tracking the evolution of the deficit-recharge relationship without intensive field instrumentation. We find accrued deficits during the dry season (driven by evapotranspiration) alone insufficiently explain differences in the wet season partitioning of rainfall due to the inter-storm behavior of the deficit during the subsequent wet season. Tracking the deficit at the storm event-scale within the wet season reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis, and may improve estimates of the rainfall required to generate recharge and streamflow on a per-storm basis. Our findings demonstrate the potential for existing public datasets to better capture water partitioning within the subsurface using a combined deficit-recharge approach, though our analysis is currently limited to basins with select characteristics.
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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 | 0.000 | 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".