Statistical Modeling of Precipitation Processes in the Context of Climate Change
Bibliographic record
Abstract
Understanding the spatial and temporal variations of the precipitation process is essential for the planning, design, and management of various water resources systems. Recent years, climate change impacts on precipitation have been considered as one of the most critical issues for water resources management worldwide. Hence, it is essential to establish the linkage between the large- scale climate variables in the atmosphere with the precipitation characteristics at a local site of interest for impact and adaptation studies. The present study is therefore carried out to develop appropriate methods for improving the accuracy of precipitation estimation at a gauged or ungauged local site in the context of a changing climate. This study can be divided into five main parts.The first part of this research aims to develop a new statistical downscaling (SD) model for describing the linkage between large-scale climate predictors and observed daily precipitation characteristics at a local site. The proposed SD model, referred hereafter SDGAM, is based on the Generalized Additive Modeling (GAM) method. The feasibility and accuracy of the SDGAM are assessed using the National Center for Environmental Prediction (NCEP) re-analysis data and the observed daily precipitation data available for the 1961–2000 period at ten gauged sites located in Canada.The second part of this research is to propose a new statistical downscaling approach based on the combination of the spatial downscaling method to link large-scale climatic variables provided by Global Climate Models (GCMs) to daily extreme precipitations at a local site using the SDGAM and the temporal downscaling procedure to describe the relationships between daily extreme precipitations with sub-daily extreme precipitations using the scaling GEV distribution and the scaling behavior of the empirical Probability Weighted Moments. IDF relations were then constructed for historical period of 1961-2000 and future periods of 2030s, 2060s and 2090s for different Representative Concentration Pathways (RCP).The third part of this research aims to estimate daily precipitation series for ungauged sites in Vietnam. Initially, daily rainfall series data of 155 stations across Vietnam were employed to identify different homogeneous rainfall regions using the Principal Component Analysis (PCA) method. Daily precipitation series at ungauged sites were then estimated using a proposed two- stage interpolation method to describe the persistence in rainfall occurrences and amounts for the identified rainfall homogenous regions.The fourth part of this research is to investigate the presence of trends in daily annual maximum precipitation series using the historical rainfall records available from a network of 175 high- quality stations across Canada and the downscaled regional gridded data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The trends were computed for two different periods: historical period from 1950 to 2005 and future period from 2006 to 2100. The final part of this research is to perform a detailed analysis of the variability in time and in space of the daily annual maximum rainfalls and extreme temperatures over the Montreal region for the present and future climates using the data from two different sources: the Pacific Climate Impacts Consortium (PCIC) and the NEX-GDDP. More specifically, the evaluation was based on the climate simulation outputs from ten different Global Climate Models (GCMs) downscaled (i) by PCIC to a regional 1/12-degree grid using the BCCAQ and BCSD methods; and (ii) by NASA to a regional 1/4-degree grid. Historical data for the 1961-1990 period and future projections for the 2006 – 2100 period were also used for this evaluation
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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 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".