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Record W3198848115 · doi:10.3390/hydrology8030134

Modeling Impact of Climate Change on Surface Water Availability Using SWAT Model in a Semi-Arid Basin: Case of El Kalb River, Lebanon

2021· article· en· W3198848115 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHydrology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Guelph
FundersAmerican University of Beirut
KeywordsStreamflowEnvironmental scienceClimate changeSoil and Water Assessment ToolRepresentative Concentration PathwaysSWAT modelHydrology (agriculture)AridSurface runoffDrainage basinWater resourcesClimate modelClimatologyDownscalingGeographyGeology

Abstract

fetched live from OpenAlex

Assessing the impact of climate change on streamflow is crucial for depicting the vulnerability of water resources and for identifying proper adaptation measures. This study used the Soil and Water Assessment Tool (SWAT) to simulate the impact of climate change on the streamflow of El Kalb river, a major perennial river in Lebanon. The model performance was tested for monthly flow at two stations under a nine-year calibration period (2003–2011) and a four-year validation period (2012–2015). The model results indicated satisfactory precision in fitting observed and simulated flow using various acceptable statistical indices. Future projections of climate change were obtained for three Representative Concentration Pathways (RCPs) (2.6, 4.5, and 8.5). The model indicated that the average annual discharge of El Kalb River in the near future (2021–2040) will decrease by around 28–29% under the three RCP scenarios. End-of-century projections (2081–2100) indicated that the flow will decrease by 23%, 28%, and 45% under RCP 2.6, RCP 4.5, and RCP 8.5, 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.750

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.283
Teacher spread0.245 · 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