Managing city-scale slow-onset disasters: Learning from Cape Town's 2015–2018 drought disaster planning
Bibliographic record
Abstract
Disaster planning for slow-onset city-wide shocks will be become increasingly necessary, particularly as cities face increasingly severe climate hazards. This paper provides unique insight into the disaster planning and management that was undertaken by the City of Cape Town government in response to its most severe hydrological drought on record. It describes how risk was understood and why decisions were made on key elements of the plan, including trigger points, risk prioritisation and mitigation, and the location and design of points of distribution of water rations for the public. Reflecting upon the authors’ experience and interviews with senior City officials who worked on the drought disaster planning and response, the paper extracts five key lessons learnt that have since been applied during the COVID-19 pandemic: (i) the need for cross-functional planning and response skills, (ii) the need for integrated, up-to-date and scale-appropriate data; (iii) the importance of scenario-based simulations, communication and rapid costing to enable the rapid scaling-up of a response; (iv) the value of being able to use outsourced expert capacity effectively; and (v) the application of previously used disaster management and planning experience to build resilience in cities. These lessons, captured in a visual framework, help reflect on capabilities required for responding to future city-scale disasters. The paper provides an informative case study for other cities and risk managers, and will be particularly useful for global South contexts that face drought and other slow-onset disasters, most recently illustrated by the COVID-19 pandemic.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".