Drought in intermittent river and ephemeral stream networks
Bibliographic record
Abstract
Abstract Intermittent rivers and ephemeral streams (IRES), those watercourses that periodically cease to flow or dry, are the world's most widespread type of river ecosystem. Our understanding of the natural hydrology and ecology of IRES has greatly improved, but their responses to extreme events such as drought remain a research frontier. In this review, we present the state of the art, knowledge gaps and research directions on droughts in IRES from an ecohydrological perspective. We clarify the definition of droughts in IRES, giving recommendations to promote transferability in how ecohydrological studies characterize droughts in non‐perennial stream networks. Based on a systematic search of the literature, we also identify common patterns and sources of variation in the ecological responses of IRES to droughts and provide a roadmap for further research to enable improved understanding and management of IRES during those extreme hydrological events. Confusion in the terminology and the lack of tools to assess the hydrological responses of IRES to drought may have hindered the development of drought research in IRES. We found that 44% of studies confused the term drought with seasonal drying and that those that measure droughts in a transferable way are a minority. Studies on ecological responses to drought in IRES networks are still rare and limited to a few climatic zones and organisms and mainly explored in perennial sections. Our review highlights the need for additional research on this topic to inform IRES management and conservation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".