Integrated modelling of the impacts of hydropower projects on the water-food-energy nexus in a transboundary Himalayan river basin
Bibliographic record
Abstract
The sustainable development goals (SDGs) and the Paris agreement target a global cleaner energy transition with wider adaptation, poverty reduction and climate resilience benefits. Hydropower development in the transboundary Koshi river basin in the Himalayan region presents an intervention that can support the SDGs whilst meeting the regional commitments to the Paris agreement. This study aims to quantify the benefits of proposed water resource development projects in the transboundary basin (4 storage and 7 run-of-the-river hydropower dams) in terms of hydroelectric power generation, crop production and flood damage reduction. A hydro-economic model is constructed by soft coupling hydrological and crop growth simulation models to an economic optimization model. The model assesses the potential of the interventions to break the vicious cycle of poverty and water, food, and energy insecurity. Unlike previous studies, the model (a) incorporates the possibility of using hydropower to pump groundwater for irrigation as well as flood regulation and (b) quantifies the resilience of the estimated benefits under future climate scenarios from downscaled general circulation models affecting both river flows and crop growth. The results show significant potential economic benefits generated from electricity production, increased agricultural production, and flood damage control at the transboundary basin scale. The estimated annual benefits are around USD 2.3 billion under the baseline scenario and USD 2.4 billion under a future (RCP 4.5) climate scenario, compared to an estimated annual investment cost of USD 0.7 billion. The robustness of the estimated benefits illustrates the climate resilience of the water resource development projects. Contrary to the commonly held view that the benefits of these proposed projects are limited to hydropower, the irrigation and flood regulation benefits account for 40 percent of the total benefits. The simulated scenarios also show substantial irrigation gains from the construction of the ROR schemes, provided the generated power is also used for groundwater irrigation. The integrated modelling framework and results provide useful policy insights for evidence-based decision-making in transboundary river basins around the globe facing the challenges posed by the water-food-energy nexus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".