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Record W2893362563 · doi:10.2136/vzj2017.10.0176

A Coupled Soil Water Balance Model for Simulating Depression‐Focused Groundwater Recharge

2018· article· en· W2893362563 on OpenAlexafffundabout
Saskia Noorduijn, Masaki Hayashi, Getachew Adem Mohammed, Aaron A. Mohammed

Bibliographic record

VenueVadose Zone Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsGolder Associates (Canada)University of Calgary
FundersAlberta Innovates
KeywordsGroundwater rechargeSnowmeltHydrology (agriculture)Environmental scienceWater balanceDepression-focused rechargeSurface runoffGroundwaterInfiltration (HVAC)AridWater contentGeologyAquiferGeographyEcology

Abstract

fetched live from OpenAlex

Core Ideas Ephemeral ponds in depressions are the foci of groundwater recharge in the Canadian Prairies. Freeze–thaw processes influence snowmelt runoff and depression‐focused recharge. A new water balance model was developed to represent these processes. The water balance model successfully simulated the observed soil processes. This model will provide a tool to estimate recharge in the prairie landscape. In arid and semiarid environments, focused infiltration of rain and snowmelt water under topographic depressions is an important mechanism of groundwater recharge. Quantifying the aggregated recharge from numerous small depressions is a major challenge in water resource management. Building on field‐based investigations into the surface water–groundwater interaction of individual depressions and their catchments (i.e., uplands) in the Canadian Prairies, we have developed a simple water balance model to simulate groundwater recharge considering the hydrological coupling of a depression–upland system. The model is based on the Versatile Soil Moisture Budget (VSMB), which has been widely used in the Canadian Prairies to simulate soil moisture conditions. We evaluated the new model, VSMB Depression‐Upland System (VSMB‐DUS), using field data consisting of an artificial flooding experiment and long‐term monitoring of a depression in Alberta, Canada. The model captured surface water level, soil moisture, and groundwater responses to the artificial flooding with reasonable accuracy and represented the interannual variability of recharge fluxes during a 5‐yr period (2007–2011), including dry and wet years. Simulated annual recharge varied between 12 and 45 mm, and annual precipitation varied between 453 and 597 mm during the 5‐yr period. The VSMB‐DUS tends to over‐ or underestimate snowmelt runoff in individual years; however, simulated recharge was only slightly affected by the errors in snowmelt runoff estimation. Due to its computational efficiency and robust algorithms, the VSMB‐DUS will provide a useful tool for estimating aggregated recharge in a large‐scale model grid cell containing hundreds of depression‐upland systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.746

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.234
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations21
Published2018
Admission routes3
Has abstractyes

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