Assessing trade-offs and vulnerabilities to global changes in the Senegal River basin.
Bibliographic record
Abstract
West Africa is one of the regions of the globe that is expected to face massive global changes in the coming decades due to sustain population growth rates and climate change. Pressures exerted on water resources are likely to rise as water demands will increase to meet the water, food and energy demands from an ever-increasing, mostly urban, population, while supplies dwindle. The Senegal River basin is a mostly underdeveloped river basin with a significant hydroelectric and agricultural potential. Policy makers are facing a challenging decision-making problem to identify climate-relevant investments because: (i) the strong divergence of the climate models and the associated uncertainties about future water availability, (ii) conflicting visions regarding the future of the basin: the first emphasizes modern uses such as energy production and irrigated agriculture; the second focuses on traditional uses such as flood recession agriculture and fisheries. We propose a modeling framework to assess trade-offs and vulnerabilities to both climate and policy factors. First, we generate 1210 GCM-based hydrologic projections for the Senegal River basin from the CORDEX-Africa ensemble and the GR2M hydrological model. The projections are then clustered based on their hydrologic properties, using hydrologic attributes describing the flow regime of the Senegal River. For each cluster, the projection closest to the centroid is selected as the representative one. The next step involves the construction of alternative development and management scenarios of the river basin for the horizons 2050 and 2080. The development scenarios essentially assume different sets of dams and irrigation schemes. Management scenarios, on the other hand, assumed different priorities attached to the operating objectives (water uses). For each triple projection-development-management scenarios, a stochastic hydroeconomic model determines the optimal operating policies, which are then used in simulation over all GCM-based hydrologic projections belonging to the same cluster. The analysis of simulation results reveals three categories of water uses: (i) high climate-sensitive sectors (hydropower production and navigation), (ii) high allocation policy sensitive sectors (flood recession agriculture and fisheries), and (iii) fairly robust sectors (irrigated agriculture).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".