Moving Water on a Malleable Planet - Large Scale Inter-basin Hydrological Transfers Now and in the Future
Bibliographic record
Abstract
Humans have been reorganizing the land surface components of the hydrological cycle for some time. One of the more hydrologically important changes are diversions of river water from one watershed to another. These are often initiated to mitigate water shortages in neighboring drainage basins or to increase the output of hydroelectric energy production. We describe a database of macro-scale inter-basin hydrological transfers covering all parts of the globe. The focus is on large-scale changes in the flow of water from one drainage basin to another or between sub-basins within the same watershed. Current counts from the database show several hundred identified diversions across five continents. Engineering works under construction as well as those that have been planned or proposed were included. Large projects now under construction in China (South-North Water Transfer Project) and planned in India (Himalayan and Peninsular components of the National River Linking Project) represent some of largest human created movements of water on the planet. We also explore the implications of the more speculative plans that have emerged over the last half century such as the Northern River Reversal, designed to deliver water from Siberia to Central Asia, and the North American Water and Power Alliance (NAWAPA) project linking high latitude rivers in Alaska and Canada to areas of the American southwest. The database has allowed us to explore, through hydrological modeling, the global impact of these engineering works. We also explore scenarios of changes in future water resources by looking at the inter-basin transfers against a suite of anticipated climate change from model output driven by the IPCC AR5 data set.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".