Climate change projections and trends simulated from the CMIP5 models for the Lake Tana sub-basin, the Upper Blue Nile (Abay) River Basin, Ethiopia
Bibliographic record
Abstract
Climate change is mainly refers to the long-term fluctuations in the weather parameters of a large area with statistical significance. Earth's climate change is either due to natural variability or human activity, in combination. The Lake Tana basin has been studied to project future climate change using the Statistical DownSclaing Model (SDSM). The SDSM was used to downscale both temperature and precipitation from Canadian Earth System Model version 2 (CanESM2) Global Climate Models (GCMs) for Bahir Dar, Dangila, Debre Tabor and Gonder synoptic weather stations. The Manna Kendall non-parametric test was then used to examine both the baseline and projected precipitation and temperature trends. The historical or baseline data for downscaling purpose was obtained from National Meteorological Agency, Ethiopia (NMA) while large-scale predictor variables were obtained under Climate Model Intercomparison Project Phase 5 (CMIP5). Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 radiative forcing scenarios have been derived from the CanESM2 model. The performance of SDSM have been validated by using various statistical methods, such as RMSE, NSE, R and R2. The SDSM results are not reliablefor projecting the precipitation as compared to maximum and minimum temperatures. These findings indicated that maximum temperature tend to increase from 1.38 °C to 3.59 °C under RCP4.5 radiative forcing scenario by the year 2080s, while minimum temperature is projected to increase up to 5.92 °C under RCP8.5 by the end of the 21st century in the Lake Tana sub-basin. On the other hand, precipitation projection did not show consistent pattern over the basin. For instance, precipitation is projected to increase up to 255 mm in the northern and central parts of the basin, however, but relatively lower result (up to 200 mm) from the RCP8.5 radiative forcing scenarios by year 2080s. By considering Mann-Kendal trend test, the baseline and projected mean annual maximum and minimum temperatures show increasing trend, while baseline rainfall shows increasing trend, but projected rainfall shows a decreasing trend at Dangila and Debre Tabor stations. Whereas, an increasing trend noticed at Bahir Dar and Gonder stations. Therefore, basin or watershed scale climate change adaptation and mitigation strategies have be developed to minimize the negative impacts of climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".