Uncertainty in model estimates of global groundwater depth
Bibliographic record
Abstract
Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".