Using SWAT-LUD Model to Estimate the Influence of Water Exchange and Shallow Aquifer Denitrification on Water and Nitrate Flux
Bibliographic record
Abstract
Numerous studies have pointed out the importance of groundwater and surface water interaction (SW–GW) in a river system. However; those functions have rarely been considered in large scale hydrological models. The SWAT-LUD model has been developed based on the Soil and Water Assessment Tool (SWAT) model; and it integrates a new type of subbasin; which is called subbasin-LU (SL); to represent the floodplain area. New modules representing SW–GW exchanges and shallow aquifer denitrification are developed in the SWAT-LUD model. In this study; the SWAT-LUD model was applied to the middle floodplain area of the Garonne catchment in France. The results showed that the SWAT-LUD model could represent the SW–GW exchange and shallow aquifer denitrification appropriately. An annual 44.1 × 107 m3 of water flowed into the river from the study area; but the annual exchanged water volume was 6.4 × 107 m3; which represented just 1% of the river discharge. A total of 384 tons of N-NO3− (0.023 t·ha−1) was consumed by denitrification in the floodplain shallow aquifer annually. The nitrate concentration (N-NO3−) decrease in the channel was 0.12 mg·L−1; but in the shallow aquifer it reached 11.40 mg·L−1; 8.05 mg·L−1; and 5.41 mg·L−1 in LU1; LU2; and LU3; respectively. Our study reveals that; in the Garonne floodplain; denitrification plays a significant role in the attenuation of nitrate associated with groundwater; but the impacts of denitrification on nitrate associated with river water is much less significant.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".