A Coupled Groundwater–Surface Water Modeling Framework for Simulating Transition Zone Processes
Bibliographic record
Abstract
There is an identified need for fully representing groundwater-surface water transition zone (i.e., the sediment zone that connects groundwater and surface water) processes in modeling fate and transport of contaminants to assist with management of contaminated sediments. Most existing groundwater and surface water fate and transport models are not dynamically linked and do not consider transition zone processes such as bioturbation and deposition and erosion of sediments. An interface module is developed herein to holistically simulate the fate and transport by coupling two commonly used models, Environmental Fluid Dynamics Code (EFDC) and SEAWAT, to simulate surface water and groundwater hydrodynamics, while providing an enhanced representation of the processes in the transition zone. Transition zone and surface water contaminant processes were represented through an enhanced version of the EFDC model, AQFATE. AQFATE also includes SEDZLJ, a state-of-the-science surface water sediment transport model. The modeling framework was tested on a published test problem and applied to evaluate field-scale two- and three-dimensional contaminant transport. The model accurately simulated concentrations of salinity from a published test case. For the field-scale applications, the model showed excellent mass balance closure for the transition zone and provided accurate simulations of all transition zone processes represented in the modeling framework. The model predictions for the two-dimensional field case were consistent with site-specific observations of contaminant migration. This modeling framework represents advancement in the simulation of transition zone processes and can help inform risk assessment at sites where contaminant sources from upland areas have the potential to impact sediments and surface water.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".