A global-scale two-layer transient groundwater model: Development and application to groundwater depletion
Bibliographic record
Abstract
Groundwater is the world’s largest accessible source of freshwater to satisfy human water needs. Moreover, groundwater buffers variable precipitation rates over time, thereby effectively sustaining river flows in times of droughts and evaporation in areas with shallow water tables. In this study, building on previous work, we simulate groundwater head fluctuations and groundwater storage changes in both confined and unconfined aquifer systems using a global-scale high-resolution (5′) groundwater model by deriving new estimates of the distribution and thickness of confining layers. Inclusion of confined aquifer systems (estimated 6–20% of the total aquifer area) improves estimates of timing and amplitude of groundwater head fluctuations and changes groundwater flow paths and groundwater-surface water interaction rates. Groundwater flow paths within confining layers are shorter than paths in the underlying aquifer, while flows within the confined aquifer can get disconnected from the local drainage system due to the low conductivity of the confining layer. Lateral groundwater flows between basins are significant in the model, especially for areas with (partially) confined aquifers were long flow paths crossing catchment boundaries are simulated, thereby supporting water budgets of neighboring catchments or aquifer systems. The developed two-layer transient groundwater model is used to identify hot-spots of groundwater depletion. Global groundwater depletion is estimated as 7013 km 3 (137 km 3 y − 1 ) over 1960–2010, which is consistent with estimates of previous studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".