Water storage paradox of reservoir expansion and evaporative losses in the MENA region
Bibliographic record
Abstract
Abstract Prolonged droughts and population growth have increased the demand for efficient water storage globally. Small agricultural reservoirs support local water demands, but high evaporation rates particularly in dry regions undermine their storage effectiveness. Integrating fine-resolution Sentinel-2 imagery and physical modeling, we created an annual dataset of small agricultural reservoirs (< 0.1 km 2 ) in the Middle East and North Africa (MENA) and quantified their associated evaporative losses from 2016 to 2023. We identified over 133,700 reservoirs, peaking in 2020, providing a combined surface area of 1,408 km 2 . The largest cumulative areas are located in Türkiye (309 km 2 ), Pakistan (234 km 2 ), Iran (168 km 2 ), Iraq (108 km 2 ), and Egypt (64 km 2 ). Small agricultural reservoirs offer a storage capacity of 1,243 million cubic meters, accounting for up to 16% of irrigation and livestock water use in most MENA countries. Annual evaporative losses from these reservoirs may potentially exceed 2,400 million cubic meters with hotspots of cumulative evaporation corresponding to regions with the highest reservoir surface area, including southern Pakistan, southwestern Iran, and southeastern Iraq. Our analysis suggests strong climatic and anthropogenic influences on the expansion of reservoirs and their storage efficiency emphasizing the need for mitigation strategies to improve agricultural water security in water-stressed regions.
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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 | 0.001 | 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 | 0.000 |
| 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".