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Record W4385725197 · doi:10.1029/2023wr034920

On Groundwater Recharge in Variably Saturated Subsurface Flow Models

2023· article· en· W4385725197 on OpenAlexaff
Chengcheng Gong, Peter G. Cook, René Therrien, Wenke Wang, Philip Brunner

Bibliographic record

VenueWater Resources Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGroundwater rechargeWater tableDepression-focused rechargeEvapotranspirationGroundwaterVadose zoneSubsurface flowGroundwater modelCapillary fringeHydrology (agriculture)Groundwater flowGeologyEnvironmental scienceAquiferGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Groundwater models that simulate only saturated flow use groundwater recharge as an input parameter. In contrast, variably saturated subsurface flow models, including integrated surface and subsurface hydrologic models, can jointly simulate the movement of water in the saturated and unsaturated zones. Instead of recharge, they require climate data such as precipitation and potential evapotranspiration. Given that the latter models represent hydrological processes operating throughout the unsaturated zone and at the water table, one might expect that recharge can be readily extracted from them. In this paper, we demonstrate that it is not the case. When the commonly used definitions of groundwater recharge are implemented in variably saturated subsurface flow models, they do not yield meaningful results. Above all, the problems occur because of the storage dynamics in the capillary fringe above the water table. Despite this difficulty, variably saturated subsurface flow models can provide the information required for water resources management directly.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.012

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 head0.065
GPT teacher head0.304
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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