Better use and management of levees: reducing flood risk in a changing climate
Bibliographic record
Abstract
Many nations rely on dykes and levees to mitigate flood risk. However, a myriad of problems has prompted views that levees are ultimately maladaptive and should be used as a measure of last resort. This leads to questions not only about the place of levees in future flood risk management, but also whether anything can be done to reduce their impacts. A detailed review of flood events from Australia, China, the Netherlands, and the USA was used to develop a case study for each country. Case studies present existing levee problems, future flood threats, and national strategies to address them. These were used as a basis to analyse the transferability of adaptive flood approaches. While many countries are attempting to restore floodplain storage, thereby reducing their reliance on levees, others are increasing their investment in levee construction. This review explores factors that affect the transferability of adaptive approaches, including issues, such as problem recognition, affordability, and program delivery. It was found that countries vary in their ability to recognise levee problems, and the level at which decisions are made influences the likelihood of adaptive solutions being adopted. Analysis suggests that federal systems face particular challenges and their capacity to adopt adaptive approaches may be impaired if institutional barriers are not addressed. Regardless of the overall approach to manage flood risk, the experiences of all case study countries offer some broadly applicable lessons for improving the use and management of levees, reducing their adverse impacts, and improving the integration of natural flood mitigation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".