Climate Change and Urban Hydrology: Research Needs in the Developed and Developing Worlds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although opinion polls indicate the public continues to be uncertain about climate change, the scientific community generally has reached consensus that increasing anthropogenically-sourced greenhouse gases have contributed substantially to rising global temperature over the second half of the twentieth century. This chapter takes the position that global warming and attendant changes in the precipitation regime have started and likely will intensify over the next century and explores these issues in relation to urban hydrology research needs for both the developed and developing worlds. Most research on climate change and water resources has focused on river flooding and drought at the watershed scale, irrigation demands, and impacts due to sea level rise. Assessment of urban drainage and sanitation infrastructure impacts and resiliency under climate change scenarios have received much less attention. Urban hydrologic impacts are broadly defined in this chapter and are discussed under eight categories: i) system resiliency and adaptation; ii) storm frequency and runoff; iii) water and sediment quality; iv) health impacts; v) water use and reuse; vi) sea level rise; vii) greenhouse gas emissions; and viii) urban heat islands. Adaptation measures to improve urban hydrologic resiliency are explored, with a focus on low impact development (LID) technologies, water reuse, land use planning, green buildings, and political will. Research needs in hydrologic science and engineering include: continued improvement of General Circulation Models (GCMs), particularly in the area of spatial downscaling; the need to further link GCM outputs and stormwater/ sewer modeling efforts (for both water quantity and quality); reconsideration
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it