Baseflow Yield Coefficient: A Simple Hydrological Index for Water Yield and Hydrological Regulation
Bibliographic record
Abstract
The rise of the ecosystem services concept has brought some characteristics of the water cycle to the attention of a broader audience who are not necessarily intrinsically familiar with hydrological processes. When referring to water supply, the term “hydrological regulation” (or streamflow buffering) is frequently used by non-hydrologists, yet they are often lost in the intricacies of the processes that drive it leading to confusion and misunderstandings. It is not uncommon that several water security challenges that require the conservation or enhancement of hydrological regulation end up prioritizing actions that aim at increasing water yield instead. Here, I present a simple index named “baseflow yield coefficient” (BYC), which is calculated as the ratio between baseflow (or dry season flow) and precipitation for a given period of time. Although quite simple, this might be a powerful tool to quantify both water yield and hydrological regulation and to provide an accessible and transparent variable that addresses the aforementioned issue. By using this index, I aim to guide the conversation to achieving more effective water security investments while, at the same time, seek to prevent having to revive the misunderstanding between water yield and hydrological regulation, so that we can move directly on to more relevant matters.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".