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Record W1926205133 · doi:10.1029/2003gb002098

Response of the water balance to climate change in the United States over the 20th and 21st centuries: Results from the VEMAP Phase 2 model intercomparisons

2004· article· en· W1926205133 on OpenAlexaboutno aff
Wendy S. Gordon, J. S. Famiglietti

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersOak Ridge Institute for Science and EducationU.S. Forest ServiceU.S. Geological SurveyUniversity of Texas at AustinU.S. Department of EnergyElectric Power Research InstituteU.S. Department of AgricultureNational Aeronautics and Space Administration
KeywordsEnvironmental scienceSurface runoffWater balancePrecipitationEvapotranspirationClimate changeClimate modelForcing (mathematics)Hydrology (agriculture)Radiative forcingWater cycleAtmospheric sciencesClimatologyMeteorologyGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Using the VEMAP Phase 2 data set, we tested the hypothesis that changes in climate would result in changes in the water balance as projected by four terrestrial ecosystem models: BIOME‐BGC, Century, LPJ, and MC1. We examined trends in runoff and actual evapotranspiration (AET), changes in runoff in relation to changes in precipitation, and differences in runoff ratios as produced by these models for 13 United States watersheds. Observed climate data were used as inputs for simulations covering 1895–1993. From 1994 to 2100, the Canadian Centre for Climate Modeling and Analysis (CGCM1) and the Hadley Centre for Climate Prediction and Research (HADCM2) general circulation models provided climate forcing. Runoff and AET trends were significantly positive in the majority of 13 watersheds examined. Percentage changes in runoff exceeded the underlying changes in precipitation and this amplification increased over time. Calculated runoff ratios showed model variability and differences based on the two GCM scenarios.

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 score0.149
Threshold uncertainty score0.983

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 science0.0010.000
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations36
Published2004
Admission routes1
Has abstractyes

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