Flood hazard response to scenarios of rainfall dynamics and land use and land cover change in an urbanized river basin in Accra, Ghana
Bibliographic record
Abstract
Understanding of flood hazard response to multiple scenarios of relevant determinants in urban centers is a precondition for proactive disaster risk management. Yet, studies on flood response to the effects of multiple agents in Sub-Saharan Africa countries is rare. This study simulates peak runoff and water flow rate to land use and land cover (LULC), and scenarios of rainfall intensity of different durations and return periods. An urban flood-prone Odaw River catchment of Accra, Ghana was studied for the possible hazard responses to variations in LULC and rainfall intensities and amounts. Landsat data for 2000, 2011 and 2020 were classified and analyzed for changes in LULC. Rainfall intensity was estimated for different durations; and 2, 10 and 25 years of return periods, using Intensity-Duration-Frequency (IDF) model. The rational and the successive flow routing hydrological models were used to simulate peak runoff and flow rate, respectively. Built-up area increased in coverage from 40% in 2000 to 65% in 2020, whereas woodland reduced from 10.5% to 4.0% for the same period. The peak runoff was highest in the built-up areas, and directly proportional to rainfall intensities and return periods. Replacing a given amount of woodland by equal amount of built-up area increased peak discharge by 3.5 times. Runoff peaked 30 mins after onset of rainfall for the 10-yr and 25-yr return periods, but peaked 1 hr for the 2-yr return period. We recommend that flood risk reduction strategies maintain substantial amount of woody vegetation and grassland, and provide quick early warning when extremely high rainfall intensity is anticipated. Land use planning organization should consider a range of flood hazard intensities, including rare and extreme events in their decisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".