Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes
Bibliographic record
Abstract
Abstract The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".