SWAT-MODFLOW: Recent Applications and an Introduction to Version 3
Bibliographic record
Abstract
SWAT-MODFLOW, a surface/subsurface hydrologic flow model that couples the SWAT and MODFLOW modeling codes, is being used in many regions worldwide to address a variety of water supply and water management issues. A version of the code that employs the groundwater reactive transport code RT3D within MODFLOW also is being used to assess implications of nutrient management for groundwater and surface water. This presentation summarizes the recent uses of SWAT-MODFLOW and outlines updates to the modeling code. The current published SWAT-MODFLOW code (Version 2 on the SWAT website) recently has been applied to watersheds in Oregon (USA), Colorado (USA), Mississippi (USA), the Ogallala Aquifer Region (USA), Canada, Wales, and Iran. This presentation introduces Version 3, which includes the following new features: (1) linking groundwater pumping to irrigation, with pumping rate dictated either by prescribed MODFLOW pumping rates or by SWAT auto-irrigation routines; (2) linking groundwater drainage from MODFLOW’s drain package to SWAT subbasin channels; (3) the use of a groundwater delay term to represent groundwater travel time from the soil profile to the water table; and (4) an updated groundwater balance that includes recharge, drainage, groundwater discharge to streams, stream seepage to groundwater, groundwater storage change, groundwater inflow/outflow due to aquifer boundary conditions, and ET from shallow groundwater. Version 3 is accompanied by a revised tutorial that includes step-by-step instructions for including these new features into SWAT-MODFLOW applications. The tutorial also provides instructions for preparing RT3D input files for groundwater nutrient transport. The source code, executable, and tutorial for Version 3 are available on the SWAT website (https://swat.tamu.edu/software/swat-modflow/). Applications of Version 3 to watersheds in Denmark and Colorado (USA) are described briefly.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.045 |
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".