Modeling Urban Hydrological Processes and Management Scenarios at Different Temporal and Spatial Scales
Bibliographic record
Abstract
The impact of urbanisation on the hydrological cycle has been discussed for several decades.Urbanisation is frequently said to cause increasing flood peaks and volumes.While this statement is certainly true for volumes, it has not been generally proven concerning flood peaks.Unfortunately, the literature is dominated by subjective opinions rather than by scientifically-based, objective evidence.To cope with increasing floods, politicians, the public and scientists argue that local end-of-pipe storm water management practices, rather than source control measures, can largely contribute to the reduction of flood peaks.However, agreement has been reached on the effect of urbanisation, depending on the size of the river basin and related characteristics such as location of urbanisation, duration and intensity of precipitation and so forth.The following numbers are based on German land use statistics.Small basins up to 10 km 2 in area if developed completely have a degree of imperviousness that might reach 50%.The effective impervious area of large basins, such as the River Rhine or River Elbe, is comparatively small, reaching 2 to 3 %.In large river basins about 12% of the land surface is typically developed, with a maximum of 50% being impervious.However, only 50% are directly connected to urban drainage systems.In the opinion of the author, it is most important to Ostrowski, M.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".