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Record W3147458797 · doi:10.5194/hess-2021-71

Simulation of long-term spatiotemporal variations in regional-scalegroundwater recharge: Contributions of a water budget approach insouthern Quebec

2021· article· en· W3147458797 on OpenAlexfundaboutno aff
Emmanuel Dubois, Marie Larocque, Sylvain Gagné, Guillaume Meyzonnat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersQuébec Ministère du Développement Durable, de l’Environnement et de la Lutte Contre les Changements Climatiques
KeywordsGroundwater rechargeBaseflowHydrology (agriculture)Environmental scienceScale (ratio)Context (archaeology)GroundwaterSurface runoffPrecipitationHydrological modellingStreamflowClimatologyAquiferGeologyMeteorologyGeographyDrainage basinCartography

Abstract

fetched live from OpenAlex

Abstract. Groundwater recharge (GWR) is recognized to be a strategic hydrologic variable, necessary to estimate when implementing sustainable groundwater management, especially within a global change context. However, its simulation at the regional scale and for long-term conditions is challenging, especially due to the limited availability of spatially-distributed calibration data and to the rather short observed time series. The use of a superficial water budget model to estimate recharge is appropriate for this task. A reliable regional-scale estimate of GWR that can be updated relatively easily using widely-available data is essential for the implementation of long-term water use policies and is clearly lacking in southern Quebec (Canada; 36 000 km2). This study aims to test the ability of a spatially-distributed water budget model, automatically calibrated with river flow rates and baseflow estimates, to simulate GWR at a regional-scale from 1961 to 2017 in southern Quebec (monthly time step, 500 m × 500 m spatial resolution). The novelty of this work lies in the simulation of the first regional-scale GWR estimate for southern Quebec and in the development of a robust approach to implement a superficial water budget model at the regional-scale and for a long period. The HydroBudget model was specifically developed by a team at Université du Québec à Montréal for regional-scale simulation and cold climate conditions, and uses parsimonious input data (distributed precipitation, temperature, and runoff curve numbers). The model was regionally calibrated with river flows and baseflows (recursive filter on river flow data), and the automatic calibration procedure of the R package caRamel allowed a satisfying calibration quality (KGE = 0.72) to be reached. Across the study area and based on the exceptionally long spatialized time series, the simulated water budget was divided into 41 % runoff (444 mm/yr), 47 % actual evapotranspiration (501 mm/yr), and 12 % potential groundwater recharge (139 mm/yr). This partitioning was influenced by precipitation, temperature, soil texture, land cover, and topography. Groundwater recharge peaked during spring (44 % of annual recharge) and winter (32 % of annual recharge). A novel and particularly useful result from this work was to show that the seasonality of recharge was driven by the regional temperature gradient, with decreasing temperatures from west to east, and that winter GWR presented a statistically significant increasing trend since 1961 due to increased precipitation and warming temperatures. Another original contribution of this work was to show that at the regional scale, water budget models, such as HydroBudget, can be easily calibrated with river flow measurements and baseflows, and therefore represent a good option with which to acquire knowledge about regional hydrological dynamics. Being accessible, they are a useful approach for scientists, modellers, and stakeholders alike to understand regional-scale groundwater renewal rates, especially if they can be easily adapted to specific study needs and environments.

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)0.001
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 score0.042
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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