Performance of multivariate and multiscalar drought indices in identifying impacts on crop production
Bibliographic record
Abstract
Abstract The assessment of drought characteristics often depends on drought indices, geographic location, hydro‐climatic condition, and timescale. In this study, we examined the spatiotemporal characteristics of drought events using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Multivariate Standardized Drought Index (MSDI). We developed a novel framework using hydro‐climatic variables from a high‐resolution process‐based hydrologic model to understand factors that alter drought indices at various timescales, and their impact on crop yields in a large agricultural region of western Canada. These indices were used to quantify droughts for each month of the year by examining 1–12‐month drought timescales in 2255 sub‐basins, simulated in 17 main river basins across Alberta, for 1981–2017. Temporal variations of the Standardized Yield Residuals Series (SYRS) of three major cereal crops (spring wheat, barley, and canola) were analysed for 1981–2017. Drought characteristics resulting from two indices varied due to differences in the input variables and timescales. The MSDI‐based results showed more frequent droughts during the fall and winter for shorter timescales, while the SPEI‐based results showed more during spring and summer. Comparing drought frequencies at the decadal scale, we found more droughts during 1996–2005 than during 1986–1995 and 2006–2015. The spatial evolution of drought events based on the MSDI showed more sub‐basins with increasing dryness during the study period than did results from the SPEI. The relationship between detrended drought indices and the SYRS varied depending on timescale, geographic location, and growth stage of crops. Overall, both indices performed similarly for agricultural impact assessment; however, the MSDI performed better early in the growing season for wheat and barley, indicating high crop production sensitivity to soil moisture deficiency.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".