Present and Future Losses of Storage in Large Reservoirs Due to Sedimentation: A Country-Wise Global Assessment
Bibliographic record
Abstract
Reservoir sedimentation is often seen as a site-specific process and is usually assessed at an individual reservoir level. At the same time, it takes place everywhere in the world. However, estimates of storage losses globally are largely lacking. In this study, earlier proposed estimates of sedimentation rates are applied, for the first time, to 47,403 large dams in 150 countries to estimate cumulative reservoir storage losses at country, regional, and global scales. These losses are estimated for the time horizons of 2022, 2030, and 2050. It is shown that 6316 billion m3 of initial global storage in these dams will decline to 4665 billion m3 causing a 26% storage loss by 2050. By now, major regions of the world have already lost 13–19% of their initially available water storage. Asia-Pacific and African regions will likely experience relatively smaller storage losses in the next 25+ years compared to the Americas or Europe. On a country level, Seychelles, Japan, Ireland, Panama, and the United Kingdom will experience the highest water storage losses by 2050, ranging between 35% and 50%. In contrast, Bhutan, Cambodia, Ethiopia, Guinea, and Niger will be the five least affected countries losing less than 15% of storage by 2050. The decrease in the available storage by 2050 in all countries and regions will challenge many aspects of national economies, including irrigation, power generation, and water supply. The newly built dams will not be able to offset storage losses to sedimentation. The paper is an alert to this creeping global water challenge with potentially significant development implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".