Response of Hydrological Drought to Meteorological Drought under the Influence of Large Reservoir
Bibliographic record
Abstract
Based on monthly streamflow and precipitation data from 1960 to 2010 in the Jinjiang River Basin of China, Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to represent meteorological and hydrological drought, respectively. The response of hydrological drought to meteorological drought under the influence of Shanmei reservoir was investigated. The results indicate that SPI and SSI have a decreasing trend during recent several decades. Monthly scales of SSI series have a significant decreasing trend from November to the following February and a significant increasing trend from May to July at Shilong hydrological station. There are three significant periodic variations with a cycle of 6-7 years, 11-12 years, and 20-21 years for annual scales of SSI series. SPI series have the same periodic variations before the 1980s, but they have not been synchronous with SSI since the 1980s at Shilong due to influences of Shanmei reservoir, especially at the periodic variations of 20-21 years. The variation of the lag time of hydrological drought in response to meteorological drought is significant at the seasonal scale. The lag time of hydrological drought to meteorological drought extends one month on average in spring, summer, and autumn but about three months in winter.
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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 | 0.000 | 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) | 0.001 | 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".