Implementation and testing of reactive transport processes for a coupled (groundwater/surface water) physically based model
Bibliographic record
Abstract
In the context of the European Water Framework Directive (WFD, 2000/60/EC), which aims to achieve a good ecological and chemical status for all natural aquatic environments, tools to help understand and quantify pesticide transfers in agricultural watersheds are necessary. Physically based, spatially distributed models can be particularly useful for representing in detail processes and interactions between the soil surface and subsurface. The present study aims to add reactive transport to one such coupled surface water/groundwater model, CATHY, in order to represent pesticide transfers. Contaminant reactions implemented in CATHY for this study are adsorption (linear and general Freundlich isotherm) and degradation (first order kinetics). The advection part of the model is solved according to the finite volume method, while the diffusive part is solved with the finite element method. Reactions were computed on nodes, after diffusion, using a sequential noniterative approach. This version of CATHY was tested on experimental data from MASHYNS, a hillslope physical model (2 m long, 1 m deep, and 0.5 m wide). On this experimental hillslope, all input and output fluxes are controlled, for water as well as for contaminants, and state variables are monitored. It is fully instrumented with tensiometers, and the soil texture is well characterized and homogeneous. The new CATHY model is progressively tested and validated: first, on water flow under steady and non-steady state conditions, then on nonreactive solute transport with various initial conditions, and finally on pesticide (reactive) transport. Such a dataset makes it easy to define the parameter set for this completely controlled and homogeneous porous medium and to focus on the model's capacity to properly represent the physical behaviour of reactive pollutants in variably saturated media. This is a first step before moving to real and more complicated hillslopes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".