Impacts of Climate Change on Hydrological Regimes in the Congo River Basin
Bibliographic record
Abstract
Surface water resources are essential for a wide range of human activities, such as municipal water supply, fishing, navigation, irrigation, and hydropower. Their regime is also linked to environmental sustainability, water-related risks, human health, and various ecosystem services. Global warming is expected to modify surface water availability, quality, and distribution and therefore affect water use productivity as well as the incidence of water-related risks. Thus, it is important for communities to plan and adapt to the potential impacts of climate change. The Congo River Basin, home to 75 million people, is subject to recurrent flood and drought events, which are expected to worsen as a result of climate change. This study aims to assess future modifications of the hydrological regime of the Congo River and the socio–economic impacts of these projected changes for three future periods: 2011–2041, 2041–2070, and 2071–2100. A Soil and Water Assessment Tool (SWAT) model of the Congo River Basin was developed, calibrated, and validated using daily rainfall observations combined with daily time series of precipitation, temperatures, relative humidity, solar radiation, and wind speed derived from the WFDEI (Watch Forced Era Interim) reanalysis data set. The outputs of ten Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) were statistically downscaled to obtain future climate time series, considering two Representative Concentration Pathways: RCP8.5 and RCP4.5. The calibrated model was used to assess changes in streamflow in all reaches of the Congo River. Results suggest relative changes ranging from −31.8% to +9.2% under RCP4.5 and from −42.5% to +55.5% under RCP 8.5. Larger relative changes occur in the most upstream reaches of the network. Results also point to an overall decrease in discharge in the center and southern parts of the basin and increases in the northwestern and southeastern parts of the basin under both emission scenarios, with RCP8.5 leading to the most severe changes. River discharge is likely to decrease significantly, with potential consequences for agriculture, hydropower production, and water availability for human and ecological systems.
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 | 0.001 | 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) | 0.001 | 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".