Non-stationary stochastic modelling of precipitation extremes in the changing climate
Why this work is in the frame
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Bibliographic record
Abstract
The increasing frequency and intensity of extreme weather events, driven by climate change, pose significant challenges to infrastructure reliability and safety. Although, the non-stationary (NS) versions of the extreme value models such as the NS generalized extreme value (GEV) models are being used to account for the non-stationarity in weather data over the past few decades in several regions, these models being high level models do not provide any specificity in terms of changes in frequency and intensity, and in addition the definition of the return period in the non-stationary context is derived in a heuristic way for such models and could be misleading for engineering design. To address this limitation, this study presents a stochastic process model for accounting for non-stationary changes in weather extremes affecting the reliability of structures. Since extreme precipitation events disrupt the operation of infrastructure systems and result in high economic losses, the effect of climate change is investigated using a non-homogeneous Poisson process (NHPP) model. First, some of the underlying issues of the non-stationary GEV models were assessed through stylized simulation studies. Then, the NHPP model was applied to the Coupled Model Inter-comparison Project (phase 6) precipitation data to demonstrate its application and analyze the projections of future extreme precipitation. The analytical results are compared with the non-stationary EV models. Upon analysis, for the SSP5-8.5 emission scenario, it was found that the median frequency of precipitation events will increase by 60% by 2100 and the mean precipitation magnitude by 5% over the same period, resulting in significant changes in the tail quantiles of the annual maximum value distribution, of the order of 20%–25% . The comparison based on the simulation studies and the analysis of precipitation data indicates that although in some cases, the quantiles predicted by the traditional EVDs, and that by NHPP can be close depending upon the underlying intensity distribution, but the waiting times and return periods in the non-stationary context are grossly over-estimated for the GEV-based models which fails to present the actual scenario and may affect the preparedness related to climate action.
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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 | 0.000 |
| 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