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Change in Stream Flow of Gumara Watershed, Upper Blue Nile Basin, Ethiopia under Representative Concentration Pathway Climate Change Scenario

2020· preprint· en· W3122440410 on OpenAlexaboutno aff
Gashaw Gismu Chakilu, Szegedi Sandor, Túri Zoltán

Bibliographic record

VenuePreprints.org · 2020
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDownscalingClimate changeEnvironmental scienceWatershedRepresentative Concentration PathwaysTributaryStream flowSWAT modelHydrology (agriculture)Soil and Water Assessment ToolClimate change scenarioStructural basinDrainage basinClimate modelStreamflowClimatologyGeographyGeology

Abstract

fetched live from OpenAlex

Climate change plays a pivotal role in the hydrology of tributaries in the upper Blue Nile basin. This study was designed to reveal the extent to which climate change impacts on stream flow of the Gumara watershed under the Representative Concentration Pathway (RCP) climate change scenario. The study considered the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios using the second generation Canadian Earth System Model (CanESM2). The Statistical Downscaling Model (SDSM) was used for calibration and projection of future climatic data of the study area. Soil and Water Assessment Tool (SWAT) model was used for simulation of the future stream flow of the watershed. Result showed that the average temperature will be increasing by 0.84oC, 2.6oC and 4.1oC in the end of this century under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively. The change in monthly rainfall amount showed a fluctuating trend in all scenarios but the overall annual rainfall amount is projected to increase by 8.6%, 5.2% and 7.3% in RCP 2.6, RCP 4.5, and RCP 8.5 respectively. Overall, this study revealed that, due to climate change, the stream flow of the watershed is found to be increasing by 4.06%, 3.26%, and 3.67% under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.120
GPT teacher head0.324
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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