Quantifying water evaporation from large reservoirs: Implications for water management in water-stressed regions
Bibliographic record
Abstract
Dam reservoirs are at the core of local water storage and supply, especially in water-stressed regions of the world with acute water shortage problems. However, evaporative losses from these reservoirs and their storage efficiency are often overlooked in water budgeting. We offer a mechanistic approach that combines physically-based modeling with remote sensing information of reservoir characteristics to reliably predict evaporative losses from dam reservoirs. The developed framework is used to predict evaporative water losses from potential dam reservoirs in different basins worldwide. We apply this framework to 10 of the largest dam reservoirs in the world's water-stressed regions to quantify evaporative water losses. Our analysis, spanning from 2000 to 2020, reveals considerable variations in annual evaporation rates in the reservoirs located in water-deprived regions exceeding 3200 mm/year during the study period with the total evaporative loss reaching 26.5 km 3 /year. The evaporative water loss accounts up to 15.8% of the storage capacity in one of the dam reservoirs, posing significant challenges for water allocation and conservation strategies, with notable economic and environmental consequences in regions already suffering from water scarcity. • A mechanistic framework was developed to quantify evaporation from dam reservoirs. • Evaporation accounts for up to 15.8% of the storage capacity of the dam reservoirs in water-stressed regions. • The study improves water accounting and management in regions with acute water scarcity.
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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 | 0.001 |
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 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".