Development of a SWAT extension module to simulate riparian wetland hydrologic processes at a watershed scale
Bibliographic record
Abstract
Abstract Using a mass balance algorithm, this study develops an extension module that can be embedded in the commonly used Soil and Water Assessment Tool (SWAT). This module makes it possible to assess effects of riparian wetlands on runoff and sediment yields at a watershed scale, which is very important for aquatic ecosystem management but rarely documented in the literature. In addition to delineating boundaries of a watershed and its subwatersheds, the module groups riparian wetlands within a subwatershed into an equivalent wetland for modelling purposes. Further, the module has functions to compute upland drainage area and other parameters (e.g. maximum volume) for the equivalent wetland based on digital elevation model, stream network, land use, soil and wetland distribution GIS datasets. SWAT is used to estimate and route runoff and sediment generated from upland drainage area. The lateral exchange processes between riparian wetlands and their hydraulically connected streams are simulated by the extension module. The developed module is empirically applied to the 53 km2 Upper Canagagigue Creek watershed located in Southern Ontario of Canada. The simulation results indicate that the module can make SWAT more reasonably predict flow and sediment loads at the outlet of the watershed and better represent the hydrologic processes within it. The simulation is sensitive to errors of wetland parameters and channel geometry. The approach of embedding the module into SWAT enables simulation of hydrologic processes in riparian wetlands, evaluation of wetland effects on regulating stream flow and sediment loading and assessment of various wetland restoration scenarios. Copyright © 2008 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".