Assessing the Impact of Climate Change on an Ungauged Watershed in the Congo River Basin
Bibliographic record
Abstract
This study assesses the impact of climate change on streamflow characteristics in the Lualaba River Basin (LRB), an important yet ungauged watershed in the Congo River Basin. Two conceptual hydrological models, HBV-MTL and GR4J, were calibrated using the reanalysis datasets and outputs of Generalized Circulation Models (GCMs) under CMIP6 during the historical period. The hydrological models were fed with outputs of GCMs under shared socioeconomic pathways (SSPs) 2-45 and 5-85, moderate- and high-radiative future scenarios. The results demonstrate that hydrological models successfully simulate observed streamflow, but their performance varies significantly with the choice of climate data and model structure. Interannual streamflow (Q) percentiles (10, 50, 90) were used to describe flow conditions under future climate. Q10 is projected to increase by 33% under SSP2-45 and 44% under SSP5-85, suggesting higher flow conditions that are exceeded 90% of the time. Q50 is also expected to rise by almost the same rate. However, a considerably higher Q90 is projected to increase by 56% under the moderate- and 80% under the high-radiative scenario. These indicate the overall higher water availability in this watershed to be used for energy and food production and the need for flood risk management.
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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 | 0.002 |
| 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) | 0.000 | 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".