A global-scale two-layer transient groundwater model: Development and application to groundwater depletion
Bibliographic record
Abstract
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 as well as evaporation in areas with shallow water tables. Lateral flows between basins can be a significant part of the basins water budget, but most global-scale hydrological models do not consider surface water-groundwater interactions and do not include a lateral groundwater flow component. In this study we simulate groundwater head fluctuation and groundwater storage changes in both confined and unconfined aquifer systems using a global-scale high-resolution (5 arc-minutes) groundwater model by deriving new estimates of the distribution and thickness of confining layers. Inclusion of confined aquifer systems (estimated 6 % to 20 % of the total aquifer area) changes timing and amplitude of head fluctuations, as well as flow paths and groundwater-surface water interactions rates. Also, timing and magnitude of groundwater head fluctuations are better estimated when confining layers are included. Groundwater flow paths within confining layers are shorter then 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 are simulated crossing catchment boundaries, thereby supporting water budgets of neighboring catchments or aquifer systems. The two-layer transient groundwater model is used to identify hotspots of groundwater depletion resulting in an estimated global groundwater depletion of 6700 km3 over the 1960–2010, 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".