Rainfall Accuracy Considerations Using Radar and Rain Gauge Networks for Rainfall-Runoff Monitoring
Bibliographic record
Abstract
Components of urban drainage during wet weather affecting water quality in receiving waters are stormwater and overflows from sanitary or combined sewers. A common element affecting each of these components is the spatial distribution of rainfall over contributing areas. Knowing quantities of stormwater arriving at inlets, infiltrating into sanitary sewers, and the inflow into combined sewers is critical to successful hydraulic model calibration and sewer system design. Accuracy and representativeness of the spatial and temporal distribution of rainfall over contributing areas is an important determinant of model accuracy. It is not always feasible to install sufficient rain gauges to measure spatially representative rainfall over a metropolitan sewer district at the scale of sewersheds. Nor is it feasible to install streamflow monitoring stations or sample priority pollutants in every impacted watershed. Thus the combination of radar and rain gauges to characterize the distribution of rainfall offers technical advantages for monitoring both rainfall and runoff in urban areas. Evaluation of a 55-event series, the median accuracy, as measured by gauge-radar comparison, has a median average difference of 8%. Gauge network density requirements should take into account the variability of precipitation, distribution over sewershed areas, and local or climatological trends caused by terrain or large water bodies. Runoff measured by streamflow is used to validate the radar to gauge correction and to test the influence of random and systematic error in the radar input. Because simulated runoff is dependent on the rainfall input
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".