Possible climate change/variability and human impacts, vulnerability of drought-prone regions, water resources and capacity building for Africa
Bibliographic record
Abstract
This review article discusses the climate, water resources and historical droughts of Africa, drought indices, vulnerability, impact of global warming and land use for drought-prone regions in West, southern and the Greater Horn of Africa, which have suffered recurrent severe droughts in the past. Recent studies detected warming and drying trends in Africa since the mid 20th century. Based on the Fourth Assessment Report of the Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project Phase 5 (CMIP5), both northern and southern Africa are projected to experience drying, such as decreasing precipitation, runoff and soil moisture in the 21st century and could become more vulnerable to the impact of droughts. The daily maximum temperature is projected to increase by up to 8°C (RCP8.5 of CMIP5), precipitation indices such as total wet day precipitation (PRCPTOT) and heavy precipitation days (R10 mm) could decrease, while warm spell duration (WSDI) and consecutive dry days (CDD) could increase. Uncertainties of the above long-term projections, teleconnections to climate anomalies such as ENSO and the Madden-Julian Oscillation, which could also affect the water resources of Africa, and capacity building in terms of physical infrastructure and non-structural solutions are also discussed. Given that traditional climate and hydrological data observed in Africa are generally limited, satellite data should also be exploited to fill the data gap for Africa in the future.Editor D. Koutsoyiannis; Associate editor N. Ilich
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".