High‐resolution projections of evapotranspiration and water availability for Europe under climate change
Bibliographic record
Abstract
Evapotranspiration plays an essential role in estimating water balance, runoff and effective precipitation. To determine historical and projected water availability for Europe, we contribute high‐resolution (1 km) estimates of monthly and annual potential evapotranspiration (ET0) and actual evapotranspiration (AET0). In the ET0 calculation, the monthly and annual heat index I and annual α parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach. The variables were estimated for a climate normal period that largely precedes an anthropogenic warming signal (1961–1990), and for two CMIP5 multi‐model future projections (2011–2040 and 2041–2070). We project widespread and relatively uniform ET0 increases of around 50–100 mm by the 2020s and 75–125 mm by the 2050s for most of Europe. These values imply important changes that may affect runoff and groundwater recharge. AET0 was identified as important driver of water availability with more regional variability. Spatial mapping of changes relative to the normal baseline imply that all except northern parts of Europe are vulnerable to water deficits, with pronounced decrease expected in southern Europe. We provide high‐resolution maps and data as an important tool for future natural resources management and climate change mitigation planning.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".