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Record W3197288434 · doi:10.1016/j.ejrh.2021.100896

Effects of climate change on terrestrial water storage and basin discharge in the lancang River Basin

2021· article· en· W3197288434 on OpenAlexaff
Sadia Bibi, Qinghai Song, Yiping Zhang, Yuntong Liu, Muhammad Aqeel Kamran, Liqing Sha, Wenjun Zhou, Shusen Wang, Palingamoorthy Gnanamoorthy

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsStructural basinEvapotranspirationClimate changeEnvironmental sciencePlateau (mathematics)PrecipitationDrainage basinHydrology (agriculture)MonsoonClimatologyWater storageDischargeGeologyGeomorphologyOceanographyMeteorologyGeography

Abstract

fetched live from OpenAlex

Lancang River Basin (upper reaches of the Mekong River basin within China). Complex terranes and diverse climates are a bottleneck for understanding the hydrology of rivers originating from the Tibetan Plateau. This study deals with the impact of climate change on water storage in the Lancang River Basin, which is governed by the South Asian monsoon system. We evaluated the spatiotemporal distribution of multi-source precipitation, evapotranspiration, and terrestrial water storage (TWS) to understand the hydrological system in the region. We provide evidence of climate change impacts on TWS and basin discharge over an upstream region of the transboundary river system. The Five Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation System (GLDAS) TWS display analogous seasonal distribution, even though the amounts differ between them. The GRACE and GLDAS TWS exhibited a significant negative trend in the basin from 2002 to 2016. However, the Center for Space Research (CSR-M) at the University of Texas and the Jet Propulsion Laboratory (JPL-M) mascon solutions concede more severe and much wider TWS reduction than the three spherical harmonic (SH) solutions. In addition, a downward trend was observed for basin discharge over 15 years as a response to climate change (decreased precipitation and increased evapotranspiration). Furthermore, we identified a 2-month time lag between precipitation and TWS, which could be a response to climatic factors along with aquifer properties in a karst dominated region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.173

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 science0.0000.000
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 head0.043
GPT teacher head0.253
Teacher spread0.209 · 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

Citations35
Published2021
Admission routes1
Has abstractyes

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