Effective impervious area for runoff in urban watersheds
Bibliographic record
Abstract
Abstract Effective impervious area (EIA), or the portion of total impervious area (TIA) that is hydraulically connected to the storm sewer system, is an important parameter in determining actual urban runoff. EIA has implications in watershed hydrology, water quality, environment, and ecosystem services. The overall goal of this study is to evaluate the application of successive weighted least square (WLS) method to urban catchments with different sizes and various hydrologic conditions to determine EIA fraction. Other objectives are to develop insights on the data selection issues, EIA fraction, EIA/TIA ratio, and runoff source area patterns in urban catchments. The successive WLS method is applied to 50 urban catchments with different sizes from less than 1 ha to more than 2000 ha in Minnesota, Wisconsin, Texas, USA as well as Europe, Canada, and Australia. The average, median, and standard deviation of EIA fractions for the 42 catchments with residential land uses are found to be 0.222, 0.200, and 0.113, respectively. These values for the EIA/TIA ratio in the same 42 catchments are 0.50, 0.48, and 0.21, respectively. While the EIA/TIA results indicate the importance of EIA, 95% prediction interval of the mean EIA/TIA is found to be 0.07 to 0.93, which shows that using an average value for this ratio in each land use to determine EIA from TIA in ungauged urban watersheds can be misleading. The successive WLS method was robust and is recommended for determining EIA in gauged urban catchments. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".