Climate Change Trend Analysis and Future Projection in Guguf Watershed, Northern Ethiopia
Bibliographic record
Abstract
Abstract According to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristic condition such as precipitation, air temperature, humidity, wind, sunshine, cloud cover and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from Ethiopia National Meteorological Agency (NMA). Download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987–2018 were considered as a base period whereas the period from 2019–2100 were considered as future periods. Historically, slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for RCP4.5 scenario and 66% (2073–2100) for RCP4.5 scenario in the Belg. Precipitation decreases in Kiremt (Jun–September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategie.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".