Quantifying Groundwater's Contribution to Regional Environmental‐Flows in Diverse Hydrologic Landscapes
Bibliographic record
Abstract
Abstract Increasing recognition of the importance of ecosystem services in water resources management has accelerated the development and application of environmental‐flows requirements for lotic ecosystems. However, most environmental‐flows management focuses on water infrastructure, such as dams or diversions, without explicitly taking groundwater into account and ignoring the importance of groundwater environmental flow contribution. In this study two methods for estimating groundwater environmental flow contributions are presented: (a) a groundwater‐centric method (based on the Sustainability Boundary Approach), which proposes that high levels of ecological protection are maintained if 90% of groundwater discharge is preserved, and (b) a surface water‐centric method (novel method), which quantifies groundwater environmental flow contributions from streamflow using region‐specific streamflow sensitivity metrics and local environmental‐flows policies. The two methods were tested in British Columbia, Canada, which has a diverse, complex, and highly coupled groundwater‐surface water system. The two methods gave comparable results in various hydro‐geoclimatic settings. Although British Columbia was used as a case study, this framework can be implemented across various spatial and temporal scales for different regions and globally, in data‐scarce, hydrologically complex landscapes. Application of these methods can aid in a robust and holistic assessment of environmental‐flows, taking into account the often‐missing groundwater component.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".