Water Resources Planning Under Deep Uncertainty and Multiple Criteria–An Example in the Senegal River Basin
Bibliographic record
Abstract
Abstract Water resources planning must take into account a range of interconnected and uncertain factors, including climate, technology, economic conditions, environmental concerns, and political systems. Conventional approaches to water resources planning often fall short when dealing with deep uncertainty—particularly uncertainties driven by climate change. This study introduces a novel framework for planning new water infrastructure using Engineering Options Analysis (EOA) to identify the most promising and adaptive development pathways. The framework differentiates between deep and statistical uncertainties and evaluates alternatives across thousands of potential future scenarios using Net Present Value (NPV) analysis and the Monte Carlo Temporal Analytic Hierarchy Process (MC‐TAHP). To enhance decision‐making, the framework incorporates two key managerial options—delay and cancellation—enabling the identification of pathways that are both robust and flexible. The approach is applied to the Senegal River Basin, where it successfully identifies three flexible development pathways. The findings show that integrating flexibility into planning can substantially improve system robustness by hedging against the risks of undesirable outcomes while maintaining opportunities for high performance. For instance, adding flexibility to KBG development pathway (the sequential construction of the Koukoutamba, Boureya, and Gourbassi dams) simultaneously reduces downside risk by raising the 10th percentile aggregated performance score from 0.19 to 0.35, and enhances upside potential by increasing the 90th percentile score from 0.89 to 0.96.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".