Struggling over water, losing it through evaporation: The case of Afghanistan and Iran
Bibliographic record
Abstract
Prolonged droughts and rising water demand have worsened water disputes in the transboundary Helmand basin, shared by Afghanistan and Iran. While both countries have built water storage reservoirs to mitigate water shortages, evaporative losses from these reservoirs reduce their effectiveness. This issue intensifies challenges over water shortages in the region without reliable monitoring data. In this study, reanalysis and remote sensing data was used to calculate the rate of water evaporation from the major water reservoirs located in Helmand basin. Additionally, globally available moisture trajectory datasets were used to analyze where the evaporated water from these major storage reservoirs eventually falls as precipitation. Our main focus was on quantifying how much of this water precipitates outside the Helmand Basin. Our results indicate that evaporative losses of blue water from reservoirs in this transboundary river basin have reached to 284 million cubic meters in 2023. Additionally, our results indicate the presence of a teleconnection, whereby a significant portion of the water evaporated from these reservoirs is transported and then precipitates outside the Helmand Basin, reaching up to an annual average of 92%. The largest portion of this evaporated water was received as precipitation by India, Pakistan, Afghanistan and China, accounting for 25%, 19%, 16% and 6%, respectively. This study provides a real-world example of how improved water intelligence and transparency, achieved through remote sensing data and modelling, can support water diplomacy and conflict resolution in transboundary basins. • Evaporative water losses from major reservoirs in transboundary Helmand basin, shared by Afghanistan and Iran, are quantified. • A significant portion of the water evaporated from these reservoirs is transported and then precipitates outside the Helmand Basin. • A real-world example of how data analytics, remote sensing and modelling, can support water diplomacy and conflict resolution in transboundary basins is shown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".