Increasing warm-season evaporation rates across European lakes under climate change
Bibliographic record
Abstract
Abstract Lakes represent a vital source of freshwater, accounting for 87% of the Earth’s accessible surface freshwater resources and providing a range of ecosystem services, including water for human consumption. As climate change continues to unfold, understanding the potential evaporative water losses from lakes becomes crucial for effective water management strategies. Here we investigate the impacts of climate change on the evaporation rates of 23 European lakes and reservoirs of varying size during the warm season (July–September). To assess the evaporation trends, we employ a 12-member ensemble of model projections, utilizing three one-dimensional process-based lake models. These lake models were driven by bias-corrected climate simulations from four General Circulation Models (GCMs), considering both a historical (1970–2005) and future (2006–2099) period. Our findings reveal a consistent projection of increased warm-season evaporation across all lakes this century, though the magnitude varies depending on specific factors. By the end of this century (2070–2099), we estimate a 21%, 30% and 42% average increase in evaporation rates in the studied European lakes under RCP (Representative Concentration Pathway) 2.6, 6.0 and 8.5, respectively. Moreover, future projections of the relationship between precipitation (P) and evaporation (E) in the studied lakes, suggest that P-E will decrease this century, likely leading to a deficit in the availability of surface water. The projected increases in evaporation rates underscore the significance of adapting strategic management approaches for European lakes to cope with the far-reaching consequences of climate change.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".