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Record W4404123180 · doi:10.1504/ijesd.2025.10067722

Simultaneous effects of climate and land use change on watershed hydrological processes

2024· article· en· W4404123180 on OpenAlexaboutno aff
Babak Ebrahimi, Mohammad Amin Asadi, Zahra Nouri, Ali Talebi

Bibliographic record

VenueInternational Journal of Environment and Sustainable Development · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedEnvironmental scienceClimate changeLand useLand use, land-use change and forestryWater resource managementWatershed managementHydrology (agriculture)Environmental resource managementGeologyEcologyComputer science

Abstract

fetched live from OpenAlex

This research aims to investigate simultaneous effects of climate change and land use change on runoff and real and potential evapotranspiration in Mehrgerd Watershed in South Western Iran. To this end, land use maps were produced for years 1987, 2002, and 2017. Then, 2032 map was predicted. Future projections of the Canadian earth system model (CanESM2) model based on representative concentration pathway 8.5 (RCP8.5) emission scenario were used. The projections were downscaled by statistical downscaling model (SDSM) to simulate future climate of the watershed during 2017-2032. Model of soil and water assessment tool (SWAT) was employed to simulate watershed's hydrological processes. R2 and NSE for calibration were 0.73, 0.69, respectively. The values for validation were 0.71 and 0.58, respectively. The results showed the contribution of climate change to runoff and real and potential evapotranspiration was 76%, 74%, and 90%, respectively. Furthermore, the land use change contribution to the mentioned components was 24%, 26%, and 10%, respectively. Therefore, effects of climate change on runoff and real and potential evapotranspiration was more significant than that of land use 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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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