Sponge City: Using the “One Water” Concept to Improve Understanding of Flood Management Effectiveness
Bibliographic record
Abstract
Urban flood-related issues are substantial in China, arising from rapid construction of megacities over decades with insufficient flood control, all being made worse by climate change. Since Sponge City (SC) options are primarily effective at maintaining the water balance at the surface, flooding continues. In response, the One Water concept is used to demonstrate the need to respond to dimensions beyond SC and shown herein to have potential to reduce the impacts of major storms (e.g., suppress 100-year flooding to a 25-year flooding equivalent). However, climate change causes more intense storms, indicating the intensity of the 100-year storm will increase by ~0.23% annually over the next 70 years. Hence, given sufficient SC options, the 100-year storms may only be effectively reduced to a 50-year storm, at best by SC options. “One Water” is utilized as a concept to demonstrate structured thinking about how each dimension of the hydrologic cycle can be employed to consider the degree of interconnection, allowing improved assessment of various components of the hydrologic cycle and SC options. Examples are used to demonstrate how the concept of One Water links the array of components of the hydrologic cycle together, generating a holistic view of urban water resource security.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".