Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management
Bibliographic record
Abstract
Abstract Droughts have significant environmental and socio‐economic impacts in Australia. This emphasizes Australia's vulnerability to climate variability and limitations of adaptive capacity. Two drought indices are compared for their potential utility in resource management. The Rainfall Deciles‐based Drought Index is a measure of rainfall deficiency while the Soil‐Moisture Deciles‐based Drought Index is a measure of soil‐moisture deficiency attributed to rainfall and potential evaporation. Both indices were used to assess future drought events over Australia under global warming attributed to low and high greenhouse gas emission scenarios (SRES B1 and A1F1 respectively) for 30‐year periods centred on 2030 and 2070. Projected consequential changes in rainfall and potential evaporation were based on results from the CCCma1 and Mk2 climate models, developed by the Canadian Climate Center and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) respectively. A general increase in drought frequency associated with global warming was demonstrated by both indices for both climate models, except for the western part of Australia. Increases in the frequency of soil‐moisture‐based droughts are greater than increases in meteorological drought frequency. By 2030, soil‐moisture‐based drought frequency increases 20–40% over most of Australia with respect to 1975–2004 and up to 80% over the Indian Ocean and southeast coast catchments by 2070. Such increases in drought frequency would have major implications for natural resource management, water security planning, water demand management strategies, and drought relief payments. Copyright © 2007 Royal Meteorological Society
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".