Enhancing drought monitoring in Southern Alberta: A comparative evaluation of drought indices for regional applicability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Southern Alberta is highly prone to frequent and severe droughts, posing significant risks to its agricultural, socioeconomic, and ecological systems. This study evaluates the performance of eight meteorological drought indices; Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Self Calibrating Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI), Effective Drought Index (EDI), China Z-index (CZI), Deciles Index (DI), and Percent of Normal Precipitation (PNP) across multiple timescales in terms of their drought monitoring skills. Using climate data from 37 meteorological stations, we evaluated each index’s ability to capture drought events and validated their performance against streamflow data, groundwater levels, and historical drought records from the Canadian Drought Monitor (CDM). The precipitation-based indices (SPI, CZI, DI, PNP) showed strong inter-correlation (> 0.90), while temperature-inclusive indices (SPEI and RDI) better aligned with hydrological responses. Among all, SPEI-9 showed the highest correlation with streamflow (0.50–0.69) and groundwater (0.42–0.47), particularly during summer and fall. The Cohen’s kappa analysis indicated substantial agreement (0.63–0.82) between SPEI, SPI, and RDI at longer timescales. Furthermore, SPEI and SPI effectively captured the major droughts in 2001-2002, 2009–2010 and 2015. While EDI and PDSI had weaker hydrological correlations (< 0.63), they provided valuable complementary insights. Overall, SPEI-9 is recommended as the most robust index for drought monitoring in Southern Alberta, with SPI, EDI, and PDSI offering additional support for comprehensive drought assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it