An application of the statistical downscaling model (SDSM) to simulate precipitation data in the Iraqi Western Desert
Bibliographic record
Abstract
Hundreds of people around the world have been affected by the global phenomenon of climate change, which has become in the current century one of the most important environmental issues, especially in arid semi-arid regions (ASARs).Global and regional water resource systems will be significantly affected by climate change.In the proposed study area, the main objective is to predict precipitation amounts on the Iraqi desert's western reaches (Wadi Houran) in the future.Climate change data were downscaled using the statistical downscaling model (SDSM 4.2).In this study two types of rain data were applied; first; daily field data were obtained from the Iraqi meteorological and seismic monitoring station (Al-Rutba station) for 30 years (1990_ 2020).The second type of data obtained from the Canadian Earth Model of the second generation (CanEsM2).The data for the NCEP were obtained from the Canadian Climate Data and Scenarios This is only one program that provides daily forecast variables applied straight to the SDSM program.It was developed by the Canadian Center for Climate Modeling and Analysis (CCCMA) of the Canadian Ministry of Environment.It was prepared for CMIP5 primarily as a contribution to the fifth report of the Intergovernmental Panel on Climate Change (AR5) where the outputs of (Can ESm2) are downloaded for three climate scenarios (RCP2.5,RCP 4.5, RCP 8.5).Future years for forecasting rain from (2011-2100).For the period where the model was calibrated from and validated (1991 -2005).After model calibration and SDSM performance validation, future precipitation simulations were performed separately for all three RCP scenarios.In addition, the baseline of future precipitation data from (2011_2100) was compared with the baseline precipitation index from (1990_2020) and it was found that in the RCP 2.6 scenario there is a decrease in precipitation (35%) in the 1980s (2071 _ 2100) and in the RCP 4.5 scenario, where rain was (35%) in the forties (2041-2070), and a significant decrease in rain In the RCP 8.5 scenario this was observed , It was observed that in the scenario of RCP 8.5, the strongest decrease in rainfall occurs in the future years in the eighties from (2071-2100) by 37%.Where it leads to an increase in desertification and because of the lack of rainfall
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".