Global assessment and hotspots of lake drought
Bibliographic record
Abstract
Many lakes have exhibited substantial variability in recent years, making “lake drought” a growing concern. However, there is no established framework for identifying and studying lake droughts. Here, we propose a reliable definition for it and provide a global assessment of over 160,000 lakes (≥1 km2) using monthly area data from 1985 to 2018. Our findings show that 15.7% of lakes have experienced statistically significant increasing trends in drought frequency (p < 0.05), with hotspots in the Southern United States at 52.7% and Southeast Australia at 70.4%. Furthermore, we identify two severe lake drought events in the Southern United States (2012–2014) and Southeast Australia (2007–2010), posing dramatic threats to water supplies, biodiversity, and ecological health. Rising trends in lake drought are driven by increasing temperature, vapor pressure deficit, and factors associated with the lake water cycle, such as precipitation deficit, increased evaporation, and excessive water withdrawal. During 1985–2018, over 15% of lakes experienced increasing drought frequency, with hotspots in the southern US and southeast Australia posing significant threats to water supplies, biodiversity, and ecological health, according to a global assessment of over 160,000 lakes using monthly lake area data.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".