Dendrochronological Reconstruction of June Drought (PDSI) from 1731–2016 for the Western Mongolian Plateau
Bibliographic record
Abstract
Droughts are a recurrent phenomenon in the semiarid forest-steppe ecotone of Mongolia with negative consequences for tree growth and agricultural management. In order to better cope with the uncertainty of a changing climate, the study of historical drought and its effects on forests could provide useful insights into ecosystem responses to climate variability. Siberian larch (Larix sibirica) is a dominant tree species in Western Mongolia that provides a valuable source of proxy data in the form of annual rings. We used dendrochronological techniques to establish a standard master chronology and analyze the relationship between annual ring widths and climatic factors. Correlation analyses revealed that the strongest associations of tree-ring index (TRI) values for the master chronology to direct climate variables were June temperature (r = −0.36, p < 0.01) and precipitation (r = 0.39, p < 0.01). The master chronology was strongly correlated to Palmer Drought Severity Index (PDSI) for June (r = 0.606, p < 0.001), and this variable (PDSIJun) was chosen for reconstruction. A simple linear regression of PDSIJun based on TRI explained 35.4% of the total variance for the period 1965–2016 and based on this model the PDSIJun changes from 1731–2016 were reconstructed. Split–sample calibration–verification tests were conducted to evaluate the quality of the model used for climate reconstruction. In the past 286 years, the number of non-drought years (PDSI > 0) was low, with a frequency of only 14.1% of the total reconstruction years. Extreme drought (PDSIJun < −2.70) occurred frequently in the 19th and early and late 20th centuries. Multi-taper method (MTM) spectra and wavelet analysis showed that the reconstructed PDSI series had high and low frequency periods (2.4–3.3 and 85–92 years). Our findings provide an understanding of the drought history of the semiarid forest-steppe ecotone of western Mongolia.
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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 | 0.000 | 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".