Assessing the water metabolism of coastal urban areas based on the water mass balance framework across time periods: A case study of Cape Town, South Africa
Bibliographic record
Abstract
The status of water resources in many coastal cities has become increasingly fragile. In a changing climate, the amount and intensity of rainfall have continued to decline, causing some coastal regions to experience severe drought and deteriorated water supply situation. This article presents a comprehensive study of the urban water metabolism of Cape Town, South Africa. A water mass balance framework was utilized to examine the city's water system across four distinct periods. In addition, the water-energy nexus before and after the drought crisis was evaluated. The key findings include a large amount of surface runoff in the Cape Town area that was not utilized before the drought crisis, resulting in a natural loss potential of water supply that is 2.54 times greater than water used for supply systems. During the drought, per capita water consumption dropped by a substantial 25%; at the same time, the total rate of water loss experienced a substantial decrease of approximately 21%. Since the pandemic, Cape Town's water resources policy has shifted to diversifying water sources, and the use of wastewater and natural water losses will be optimized by more than 32% in 2040 to achieve a water-sensitive city. Future research should explore the temporal and spatial dynamics of urban water flows, the impact of socioeconomic factors, and the integration of water system optimization models for enhanced urban water management. • The drought crisis and COVID-19 jointly affect Cape Town's water supply practices. • Cape Town works towards a water sensitive city to reduce the impact of uncertain events. • Desalination, groundwater abstraction, and wastewater reuse are alternative water sources. • Changes to Cape Town's water system have direct implications for energy system performance.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".