Climate Change Impacts and Flood Control Measures for Highly Developed Urban Watersheds
Bibliographic record
Abstract
Flooding and overflow are recurring problems in several Brazilian cities, which usually face disorderly development. The causes vary, and include increased impervious surface areas, deficiency/inefficiency of drainage structures and lack of maintenance, siltation of rivers, channel obstructions, and climatic factors. In this paper, we present an analysis of mitigation measures to minimize flooding in a watershed located in the core of the city of São Paulo, the biggest city with the highest gross domestic product (GDP) in Brazil. Observed rainfall records and existing intensity duration frequency (IDF) curves for the region are used to obtain design storms. To account for climate change, the equidistance quantile matching method for updating IDF curves under climate change, a well-known procedure, was applied to the existing historical data. Several different global climate models (GCMs) and one regional model were applied to obtain and update rainfall design storms. The GCMs and future scenarios used were from Intergovernmental Panel on Climate Change—IPCC Assessment Report 5 (AR5) and two future projections—representative concentration pathway (RCP) 4.5 and 8.5. Spatially distributed reservoirs combined with low-impact development (LID) measures were used to evaluate different design storm scenarios combined with return periods of 25 and 100 years as well as the updated IDF under climate change for RCP 4.5 and RCP 8.5. Results show that the proposed changes to the drainage system can help reduce the risk and damage of flooding. The climate change scenarios, however, impose a significant threat and need immediate attention from city planners and stakeholders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".