Losing water through evaporation from water reservoirs in water-stressed regions: The case of Iran-Afghanistan
Bibliographic record
Abstract
The rising demand for water in the transboundary Helmand basin is causing heightened tensions between Afghanistan and Iran concerning the Helmand River with serious environmental, socio-economics and political implications. This intensifies the long-existing transboundary water conflicts between the two countries. To overcome water shortages during dry spells, water reservoirs and storage infrastructure have been constructed in a region experiencing extremely hot and dry climate conditions. Water evaporation from these reservoirs diminishes their storage efficiency. This makes quantification and prediction of water evaporation from these reservoirs a crucial step for water management, accountability and transboundary cooperation in the river basin. In this study, we used satellite remote sensing information of the large water reservoirs in the Helmand basin combined with physically-based modelling approaches (Aminzadeh et al., 2024) to obtain reliable estimates of evaporative losses from the main storage infrastructures. Our results suggest that a considerable amount of water loss in the region stems from the evaporation of water in major water storage infrastructure within the basin, particularly from the man-made reservoirs located on the Iranian side of the basin in a very water-deprived region. Our results indicate 491 million cubic meters of water was evaporated from the reservoirs in 2020 accounting for 11% of their total storage capacity and 8.2% of the water demands in the basin. Our findings improve water accounting and management in the Helmand basin. Additionally, they underscore the key role of effective water storage infrastructures in managing limited freshwater resources which could improve water security. Aminzadeh, M., Friedrich, N., Narayanaswamy, S.G., Madani, M. Shokri, N. (2024). Evaporation loss from small agricultural reservoirs: An overlooked component of water accounting, Earth’s Future (Accepted).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".