Urbanization and stream ecosystems: the role of flow hydraulics towards an improved understanding in addressing urban stream degradation
Bibliographic record
Abstract
Catchment urbanization is widely recognised as a primary driver of stream degradation by increasing stormwater runoff, which causes major changes to key ecosystem processes. Reinstating the “natural” hydrogeomorphic conditions is central in designing successful, self-sustaining restoration actions; however, addressing urban stream degradation by re-establishing the hydrogeomorphic conditions remains a challenge, and comparatively limited measurable progress has been observed, particularly in achieving ecological objectives. This review articulates that stream restoration goals might be better achieved when management measures take a broader approach that considers anticipated hydraulic condition effects that liaise relationships between flow and ecology. The study argues that fluvial systems are characterised by complex and dynamic ecosystem processes primarily governed by the hydraulic conditions (e.g., velocity, depth, shear stress); thus, as the practice of addressing urban stream restoration becomes increasingly common, it is critical to explore and understand the anticipated response of the hydraulic conditions. It describes how hydraulic regime consideration provides further opportunity for a holistic approach to urban stream management given their capacity to account for multiple ecological and geomorphic objectives. This review suggests that developing suitable flow–biota–ecosystem processes nexus is critical to addressing urban stream degradation, and hydraulic consideration in restoration actions provides an important step towards that. It discusses opportunities to evolve management actions to achieve restoration goals by highlighting how the management of the two key levers (addressing altered flow regime and morphology) to improve the hydraulic conditions can help to address the urban stream disturbance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".