Correlation of drought indices with climatic and socio-economic factors in San Diego, USA
Bibliographic record
Abstract
Droughts have become more severe and prolonged in recent years, causing significant social and environmental impacts. Analysing drought conditions with various climatic and socio-economic factors is critical for effective drought monitoring. This study investigated the correlation between drought severity and climatic and socio-economic changes in a study area – San Diego County, CA, USA. The drought severity was quantified using three drought indices: standardised precipitation index, standardised precipitation evapotranspiration index and standardised streamflow index. These indices were also used to describe the variation of the drought severity in the study area. The study employed Spearman’s correlation coefficient analysis to evaluate the correlations between the drought indices and climatic and socio-economic factors. The results showed that the drought indices indicated similar variations of the droughts in the study area. However, the severity, duration and frequency of the droughts varied with the timescales in the drought index analysis. Additionally, this study found that the drought conditions based on the three indices had strong negative correlations with precipitation, soil moisture, agricultural area and forest area, which were more pronounced as the timescale increased. The findings will provide insights into effective drought monitoring and management, particularly targeting severe droughts.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".