A Review of Flood Management Considering the Impacts of Climate Change
Bibliographic record
Abstract
Recent work on climatic change indicates that the frequency and severity of flooding in many parts of the world could increase due to major changes in the hydroclimatic regime and a continuing rise in mean sea level. Changes in the magnitude and intensity of precipitation and the timing of runoff will increase riverine flooding, including the occurrence of midwinter ice-jam floods in northern rivers. Higher sea levels will increase the likelihood of coastal flooding and problems with urban infrastructure draining to tidal estuaries. Unless action is taken to lessen the vulnerability of human settlements, flood damages will increase. Adaptation strategies are needed that identify and direct development away from flood-prone areas, and incorporate infrastructure design criteria that take a changing climate into account. In this paper, a methodological approach to developing strategies for flood management is presented. After considering the occurrence and potential consequences of floods, and the importance and means of flood management, the impacts of climate change on flood mitigation are considered. Key elements of a generic adaptive strategy for floodplain management are then proposed, and, finally, the implementation of a flood management program is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".