Comparing Global Violations of Environmentally Critical Groundwater Discharge Thresholds
Bibliographic record
Abstract
Abstract Groundwater is a crucial resource to support surface water bodies via groundwater discharge. In this study, we applied two methods of estimating global environmentally critical groundwater discharge, defined as the flux of groundwater to streamflow necessary to maintain a healthy environment, from 1960 to 2010: the Presumptive Standard stipulates that a standard proportion of groundwater discharge should be maintained at all timesteps, while the Q* is a low‐flow index that focuses on critical periods. We calculated these critical flow thresholds using simulated natural groundwater discharge, and estimated violations of the thresholds when human‐impacted groundwater discharge dropped too low. Our global assessment of the frequency and severity of violations over all timesteps in our study period showed that the Presumptive Standard estimated more frequent and severe violations than the Q*, but that the spatial patterns were similar for both methods. During low‐flow periods, when the relative importance of groundwater to support streamflow is greatest, both methods estimated similar magnitudes of violation frequency and severity. We further compared our results to a method of estimating environmentally critical streamflow, Variable Monthly Flow, which does not explicitly consider groundwater. From the differences in violation frequency between these groundwater‐centric and surface water‐centric methods, we evaluated the influence of including groundwater contributions to streamflow in environmental flow assessments. Our results show that including groundwater in such assessments is particularly important for regions with high groundwater demands in the drier climates of the world, while it is less important for regions with low groundwater demands and more humid climates.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".