Water-energy-food planning and operations framework for river basins with a case study on the Blue Nile
Bibliographic record
Abstract
Water infrastructure is essential to achieving several Sustainable Development Goals (SDGs), including SDG 2 on zero hunger, SDG 6 on water and sanitation, and SDG 7 on affordable and clean energy. However, important tradeoffs and synergies need to be navigated across these goals as both water and resources for infrastructure investments are limited. In transboundary river basins, such tradeoffs can transcend countries, creating a complex, interconnected system of water-energy-food linkages. With increasing pressures on the Blue Nile’s water resources from population and economic growth and climate change, an analytical framework for joint planning of these essential human development goals at a fine temporal resolution and considering multi-national priorities can enhance the potential to achieve water, energy, and food security. In this study, we develop and apply a framework for water resources planning in the Blue Nile using four steps: (1) understanding the water-energy-food nexus management landscape through stakeholder engagement and literature review; (2) developing a detailed daily simulator that captures nexus components and objectives at a fine temporal scale; (3) linking the simulator to an Artificial intelligence-based search algorithm to design efficient agricultural and dam operation portfolios considering national and sectoral priorities; and (4) presenting the results using interactive visualization tools to facilitate dialogue and support decisions. Our Blue Nile water management results show that operating the Grand Ethiopian Renaissance Dam for baseload reduces variation in Blue Nile flows and supports irrigation development in Sudan. The results also identify efficient operation plans for large dams on the Blue Nile for alternative cropping patterns in expanded irrigation areas in Sudan that minimize tradeoffs across water, energy, and food objectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".