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Record W3217290390 · doi:10.1016/j.envc.2021.100385

Climate change projections and trends simulated from the CMIP5 models for the Lake Tana sub-basin, the Upper Blue Nile (Abay) River Basin, Ethiopia

2021· article· en· W3217290390 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Challenges · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersBangalore UniversityMinisterstwo Edukacji i Nauki
KeywordsDownscalingCoupled model intercomparison projectRepresentative Concentration PathwaysClimatologyEnvironmental sciencePrecipitationClimate changeClimate modelRadiative forcingForcing (mathematics)Structural basinMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.052
GPT teacher head0.248
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it