Statistical Modeling of Daily Precipitation Process in the Context of Climate Change
Bibliographic record
Abstract
Information on the variability of precipitation process is essential for the planning, design and management of various water resources systems. Furthermore, recent assessment reports on climate change have indicated a worldwide increase in the frequency of extreme storm events for the late 20th century because of global warming. Consequently, research on developing innovative approaches for limiting and adapting climate change impacts on water infrastructures is highly critical due to the substantial investments involved. Global Climate Models (GCMs) have been commonly used in various studies for assessing these potential impacts. However, outputs from these GCMs (generally greater than 200 km) are considered too coarse and hence are not suitable for climate change impact studies at a given site or over a catchment area. As a result, several downscaling techniques have been proposed to downscale these GCM outputs to the precipitation series at a given location of interest. Nevertheless, there is still no general agreement about which downscaling method is the best approach for describing accurately the observed precipitation characteristics at a given site in the climate change context, depending mainly on the study objectives and the climatology of the study area. The present study is therefore carried out in order to develop appropriate methods for improving the accuracy of precipitation estimation at a local site in the context of a changing climate. This study therefore proposes a new statistical model, herein referred to as SDGAM, using the Generalized Additive Models (GAM) to address the shortcomings of existing downscaling methods. The feasibility and accuracy of the proposed new approach were evaluated using the observed daily precipitation records available at two rain-gauge stations located in Quebec Province, and the National Center for Environmental Prediction (NCEP) re-analysis data that are interpolated for two GCMs (Canadian CanESM2 and UK HadCM3). Results of this numerical application have indicated that the proposed SDGAM model was able to describe well many features of the daily precipitation process, including its amounts, occurrence frequency, intensity, and extremes. In addition, it has been demonstrated that the suggested SDGAM model could provide more accurate results than the popular Statistical Downscaling Model (SDSM) in the modeling of the daily precipitation process based on both numerical and graphical performance criteria. Finally, the proposed SDGAM can generate daily precipitation series for future periods under different climate change scenarios: RCP2.6, RCP4.5 and RCP8.5 for CanESM2, as well as A2 and B2 for HadCM3. These generated precipitation series are useful for various climate change impact studies in practice
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".