Hydropower versus irrigation—an analysis of global patterns
Why this work is in the frame
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Bibliographic record
Abstract
Numerous reservoirs around the world provide multiple flow regulation functions; key among these are hydroelectricity production and water releases for irrigation. These functions contribute to energy and food security at national, regional and global levels. While reservoir operations for hydroelectricity production might support irrigation, there are also well-known cases where hydroelectricity production reduces water availability for irrigated food production. This study assesses these relationships at the global level using machine-learning techniques and multi-source datasets. We find that 54% of global installed hydropower capacity (around 507 thousand Megawatt) competes with irrigation. Regions where such competition exists include the Central United States, northern Europe, India, Central Asia and Oceania. On the other hand, 8% of global installed hydropower capacity (around 79 thousand Megawatt) complements irrigation, particularly in the Yellow and Yangtze River Basins of China, the East and West Coasts of the United States and most river basins of Southeast Asia, Canada and Russia. No significant relationship is found for the rest of the world. We further analyze the impact of climate variables on the relationships between hydropower and irrigation. Reservoir flood control functions that operate under increased precipitation levels appear to constrain hydroelectricity production in various river basins of the United States, South China and most basins in Europe and Oceania. On the other hand, increased reservoir evaporative losses and higher irrigation requirements due to higher potential evaporation levels may lead to increased tradeoffs between irrigation and hydropower due to reduced water availability in regions with warmer climates, such as India, South China, and the Southern United States. With most reservoirs today being built for multiple purposes, it is important for policymakers to understand and plan for growing tradeoffs between key functions. This will be particularly important as climate mitigation calls for an increase in renewable energy while agro-hydrological impacts of climate change, population and economic growth and associated dietary change increase the need for irrigated food production in many regions round the world.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it