High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management
Bibliographic record
Abstract
Despite the recognized importance of reservoirs and dams, global datasets describing their characteristics and geographical distribution are largely incomplete. To enable advanced assessments of the role and effects of dams within the global river network and to support strategies for mitigating ecohydrological and socioeconomic costs, we introduce here the spatially explicit and hydrologically linked Global Reservoir and Dam database (GRanD). As of early 2011, GRanD contains information regarding 6862 dams and their associated reservoirs, with a total storage capacity of 6197 km 3 . On the basis of these records, we estimate that about 16.7 million reservoirs larger than 0.01 ha – with a combined storage capacity of approximately 8070 km 3 – may exist worldwide, increasing Earth's terrestrial surface water area by more than 305 000 km 2 . We find that 575 900 river kilometers, or 7.6% of the world's rivers with average flows above 1 cubic meter per second (m 3 s −1 ), are affected by a cumulative upstream reservoir capacity that exceeds 2% of their annual flow; the impact is highest for large rivers with average flows above 1000 m 3 s −1 , of which 46.7% are affected. Finally, a sensitivity analysis suggests that smaller reservoirs have substantial impacts on the spatial extent of flow alterations despite their minor role in total reservoir capacity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".