The pronounced seasonality of global groundwater recharge
Bibliographic record
Abstract
Abstract Groundwater recharged by meteoric water supports human life by providing two billion people with drinking water and by supplying 40% of cropland irrigation. While annual groundwater recharge rates are reported in many studies, fewer studies have explicitly quantified intra‐annual (i.e., seasonal) differences in groundwater recharge. Understanding seasonal differences in the fraction of precipitation that recharges aquifers is important for predicting annual recharge groundwater rates under changing seasonal precipitation and evapotranspiration regimes in a warming climate, for accurately interpreting isotopic proxies in paleoclimate records, and for understanding linkages between ecosystem productivity and groundwater recharge. Here we determine seasonal differences in the groundwater recharge ratio, defined here as the ratio of groundwater recharge to precipitation, at 54 globally distributed locations on the basis of 18 O/ 16 O and 2 H/ 1 H ratios in precipitation and groundwater. Our analysis shows that arid and temperate climates have wintertime groundwater recharge ratios that are consistently higher than summertime groundwater recharge ratios, while tropical groundwater recharge ratios are at a maximum during the wet season. The isotope‐based recharge ratio seasonality is consistent with monthly outputs from a global hydrological model (PCR‐GLOBWB) for most, but not all locations. The pronounced seasonality in groundwater recharge ratios shown in this study signifies that, from the point of view of predicting future groundwater recharge rates, a unit change in winter (temperate and arid regions) or wet season (tropics) precipitation will result in a greater change to the annual groundwater recharge rate than the same unit change to summer or dry season precipitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".