Extreme drought affects lake water quality, quantity, morphometry: Evidence from China’s largest fresh water lake under the 2022 global drought
Bibliographic record
Abstract
• Extreme drought decreased the water area and quantity of Poyang Lake by 78.4% and 90.63%. • Lake shoreline decreased by 2923.70 km and morphometric indices varied greatly affected by drought. • Drought event changed water quality by meteorology, water–rock interaction and human activities. Extreme drought poses a significant threat to humanity. In the summer of 2022, the world experienced the worst drought in recent years, with a precipitation deficit and an abnormal high temperature, profoundly affecting human life and the aquatic environment. However, the drought influence on large freshwater lakes remains unclear. In this study, we selected China’s largest freshwater lake (Poyang Lake) as the research object and investigated the lake water area, quantity, lake morphology and water quality in 2018 (normal season) and 2022 (extreme drought period). Results showed that standardized precipitation index (SPI), standardized runoff index (SRI) and standardized precipitation-evapotranspiration index (SPEI) reached moderate to severe drought in the summer of 2022. From 2018 to 2022, lake water area decreased (1789.62 km 2 ), water quantity reduced (15.40 × 10 9 m 3 ) and lake shoreline decreased (2923.70 km). The shoreline development index, size ratio and energy factor decreased by 4.87, 198.53 m and 963.60, specifically. The dynamic ratio, relative depth and Schindler’s ratio increased by 1457.10, 0.04 and 13.48 m −1 , respectively. The water chemical indicators varied significantly in two years and the water hydrochemical types changed from SO 4 ·Cl − Ca·Mg type and HCO 3 − Ca·Mg type to SO 4 ·Cl − Ca·Mg type from 2018 to 2022. Water-rock interaction, alternating cation adsorption and anthropogenic influence on water quality represented different patterns in two periods. Our findings demonstrate significant differences in water resources and quality between common and extreme drought conditions in China’s largest fresh water lake, which can inform research on climate change effects on international large freshwater lakes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".