Assessing the impacts of climate change on climatic extremes in the Congo River Basin
Bibliographic record
Abstract
and is home to 75 million people. A significant part of the population is exposed to recurrent floods and droughts, and climate change is likely to worsen these events. Climate change studies of the Congo River basin have so far focused on annual and seasonal precipitation, but little attention was paid to extreme climatic events. This study aims to assess future changes in rainfall-induced flash floods and drought regimes in the Congo basin from the present day to 2100, using four selected extreme climatic indices as proxies to these two natural disasters. The indices are the total annual precipitation (PCPTOT), the number of days where rainfall is above 20 mm (PCP20), the standardized precipitation index (SPI), and the standardized precipitation-evapotranspiration index (SPEI). The indices were calculated with the statistically downscaled output of eleven Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) under two Representative Concentration Pathways: RCP 8.5 (high emissions scenario) and RCP 4.5 (moderate emissions scenario). Precipitation and temperature simulated by the RCMs were statistically downscaled using quantile mapping, while wind speed, solar radiation, and relative humidity were projected using K-nearest neighbor downscaling. The evolution of the indices was then assessed between the reference period (1976-2005) and three future periods (2011-2040, 2041-2070, and 2071-2100). Multimodel average results suggest that (i) independently of the scenario and period, PCPTOT and SPI will increase in the north, east, and western extremities of the basin and decrease in the basin's center. (ii) The maximum increase (+ 24%) and decrease (- 6%) in PCPTOT were both projected under RCP 8.5 in the 2071-2100 period. (iii) PCP20 will increase independently of the period and scenario. Under RCP 8.5, in the 2071-2100 period, PCP20 will increase by 94% on average over the whole watershed. (iv) The SPEI results suggest that in all periods and scenarios, the rise in evapotranspiration due to higher temperatures will offset annual precipitation increases in the north, east, and western extremities of the basin. Increased evaporation will exacerbate the decrease in annual precipitation in the center, leading to increased drought frequency in the entire basin. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-022-03326-x.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".