Standardized precipitation evapotranspiration index (SPEI) for Canada: assessment of probability distributions
Bibliographic record
Abstract
Candidate probability distributions for the Standardized Precipitation Evapotranspiration Index (SPEI) for Canada were examined using the Canadian Gridded (CANGRD) temperature and precipitation dataset and CMIP5 projections. The probability distribution is a core component to the calculation of standardized values. For SPEI, a continuous probability distribution is fitted to the water balance time series so that the resulting transformed index follows a standard normal distribution. Selection of an appropriate distribution is therefore important as an inappropriate distribution may lead to biased values and subsequently affect the interpretation of SPEI results. Candidate distributions considered for SPEI included the generalized logistic (GLO), generalized extreme value (GEV), Pearson Type III (PE3) and normal (NOR) distributions. A range of goodness of fit tests were used to assess how well the distributions fit SPEI. Differences in CANGRD SPEI time series between three pairs of distributions, GLO-GEV, GLO-PE3, and GEV-PE3, showed that there were larger differences in SPEI values between GLO and the other two distributions than the difference between GEV and PE3. SPEI results fitted with GLO resulted in lower threshold exceedances of extreme SPEI (-/+2) than GEV and GLO. A comparison between CMIP5 SPEI values fitted with GLO (SPEI-GLO) and PE3 (SPEI-PE3) showed that there were seasonal and spatial variations in results, although the use of multi-model ensembles reduced these differences. As was the case for CMIP5 SPEI-GLO projections, projected annual changes in CMIP5 SPEI-PE3 show an increase in drying at the surface in central/southern Canada. Overall, the study recommends using PE3 or GEV for SPEI analysis for Canada.
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 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 | 0.001 | 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".