Assessment of Water Footprints of Consumption and Production in Transboundary River Basins at Country-Basin Mesh-Based Spatial Resolution
Bibliographic record
Abstract
Water is unevenly distributed globally. This uneven distribution is the reason behind the differences among geographical areas in terms of their water footprint of consumption and production. This gives the global trade of goods a unique feature. This characteristic of the water footprint might be used to address water scarcity and conflicts because water availability also has the same trend. Transboundary river basins are freshwater resources with a high probability of water scarcity and conflict because the water is claimed by multiple sovereign countries. In order to design sharing mechanisms for transboundary river basins that incorporate virtual water concept, it is key to identify the virtual water balance of country-basin units. A study addressing this research gap is not yet available. This article identified and discussed net virtual water importer and exporter sub-basins of transboundary rivers at a country-basin mesh based spatial resolution. The results of our study show that out of the 565 country-basin units surveyed in this article 391, 369, and 461 are net gray, green, and blue virtual water importers respectively. These sub-basins covers 58.37%, 47.52% and 57.52% of the total area covered by transboundary river basins and includes 0.65, 1.9, and around 2 billion people, respectively. The results depict that not only the water endowment of sub-basins is a determining factor for their water footprint of consumption and production, but also their social, economic, and demographic profiles. Furthermore, the water footprint of consumption and production within most of the country-basin units have a global feature. Hence, sustainable water management schemes within border-crossing basins should take into account not only the local but also the global water footprints of consumption and production. This can offer more options for sharing transboundary river basins water capital, thereby minimizing the probability of water scarcity and water conflicts.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".