Multi-Model Assessment of Climate Change Impacts on the Streamflow Conditions in the Kasai River Basin, Central Africa
Bibliographic record
Abstract
The Congo River Basin is the second-largest watershed globally, flowing through nine countries before reaching the Atlantic Ocean. The Kasai River Basin (KARB), containing about one-fourth of Congo’s freshwater resources, plays a strategic role in sustaining navigation, food production, and hydroelectricity generation in Central Africa. This study applies a multi-model framework suited for data-scarce regions to assess climate change impacts on water availability in the KARB. Using two conceptual hydrological models calibrated with four reanalysis datasets and fed with bias-corrected outputs from 19 climate models under two representative climate pathways (RCPs), we project changes in the mean annual discharge ranging from −18% to +3%, highlighting the sensitivity of impact assessments to model and input data choices. Additionally, streamflow signatures (Q10, Q50, Q90) are projected to decline by approximately 9%, 18%, and 13%, respectively, under RCP 8.5. Annual hydropower potential is estimated to decrease by 14% and 5% under RCPs 4.5 and 8.5, respectively. These findings provide actionable insights for water management practices in the KARB, including guiding the development of adaptive strategies to optimize water allocation, mitigate risks of scarcity, and support sustainable agricultural and industrial activities in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".