No Direct Linkage Between Event‐Based Runoff Generation and Groundwater Recharge on the Maimai Hillslope
Bibliographic record
Abstract
Abstract Hillslope hydrological investigations in humid regions to date have focused mostly on runoff generation during events. The few papers that have also examined groundwater recharge processes associated with subsurface stormflow production have found strong linkages between episodes of runoff and recharge to the aquifer. But the range of climate, vegetation, and geological conditions examined thus far has been limited. Here we explore how geologic characteristics, timing of subsurface stormflow, and hydroclimatic conditions relate to the timing of bedrock groundwater recharge at the well‐studied Maimai watershed. We hypothesized that recharge would be determined by subsurface stormflow frequency in this system with high rainfall and little seasonality of the hydrologic response. Unexpectedly, isotopic analysis and noble gas measurements indicated that recharge occurred almost exclusively during winter months despite previous work at Maimai showing subsurface stormflow occurs in all seasons and rainfall‐runoff ratios are high year‐round. A sprinkler and dye experiment conducted directly on open bedrock identified groundwater recharge mechanisms and rates, and a simple empirical recharge model suggests almost 90% of recharge occurred from only 55% of annual precipitation. We found no correlation between the timing and magnitude of groundwater recharge and total precipitation, direct runoff or subsurface stormflow. The catchment effectively converted rainfall to runoff during all seasons, but the unfractured low permeability bedrock (2.3 × 10 −8 m/s) required long durations of extended catchment wetness for appreciable recharge to occur—a condition satisfied only during winter months with lower evapotranspiration. These findings suggest the need to better understand the geologic controls of recharge in headwaters.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".