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Record W1999540521 · doi:10.1088/1748-9326/4/3/035003

Simulated response of groundwater to predicted recharge in a semi-arid region using a scenario of modelled climate change

2009· article· en· W1999540521 on OpenAlexaffabout
M. W. Toews, D. M. Allen

Bibliographic record

VenueEnvironmental Research Letters · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGroundwater rechargeEnvironmental scienceEvapotranspirationClimate changeHydrology (agriculture)PrecipitationIrrigationAridGroundwaterWater balanceReturn flowClimate modelWater resource managementGeographyGeologyAquiferFlow (mathematics)EcologyMeteorology

Abstract

fetched live from OpenAlex

Groundwater systems in arid regions will be particularly sensitive to climate change owing to the strong dependence of rates of evapotranspiration on temperature, and shifts in the precipitation regimes. In agricultural areas, such changes in climate may require increased irrigation, putting stress on existing water supplies. In this study, a regional-scale numerical groundwater model was developed for the Oliver region of the south Okanagan, British Columbia, Canada, to simulate the impacts of future predicted climate change on groundwater. In future time periods (the 2050s and 2080s), the most noticeable change in the water budget is the increased contribution of recharge to the annual water budget, estimated at 1.2% (2050s) and 1.4% (2080s) of the total annual budget relative to the current conditions. This increase is related primarily to increases to irrigation return flow resulting from higher irrigation needs under warmer temperatures and a longer growing season. Increases in recharge and irrigation return flow will result in higher water tables with future climate conditions, particularly in the irrigation districts. Median value increases in groundwater level of up to 0.7 m by the 2080s are estimated.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.064
GPT teacher head0.308
Teacher spread0.244 · 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

Citations53
Published2009
Admission routes2
Has abstractyes

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