Mapping and Modeling of Variable Source Areas in a Small Agricultural Watershed
Bibliographic record
Abstract
Modeling the spatiotemporal dynamics of Variable Source Areas (VSA) is challenging since VSAs depend on a number of factors such as soil properties, land use, water table, topography, geology and climatic conditions. In spite of these challenges, few encouraging attempts have been made to develop models for quantification and locating runoff generation areas based on VSA concepts. However, these approaches need to be validated with field tests for their feasibility and accuracy. This research is divided into four main sections. The first section discusses how an advanced, low cost, remotely controlled digital Wireless Sensor Network (WSN) system was developed to monitor and acquire climatic and hydrological data from a distantly located watershed. The developed WSN system was installed in a small agricultural watershed near Elora, Ontario and watershed observations of 45 rainfall events from September 2011 to July 2013 were collected. In the second section, significance of various climatic and hydrological factors affecting the spatiotemporal variability of runoff generating areas are explored. Analysis showed that the runoff generating areas were strongly influenced by the seasons and that rainfall amount was the most dominant factor affecting these areas, followed by initial soil moisture and rainfall intensity. The third section includes modification of an existing distributed CN-VSA method by incorporating seasonal variability of potential maximum soil moisture retention of the watershed. The simulations made with modified distributed CN-VSA predicted spatial extent of saturated areas more accurately in ways consistent with VSA hydrology. In the fourth section, an event based AGNPS model is reconceptualised based on VSA hydrology concept by incorporating the Topographic Wetness Index (TWI). This modeling approach demonstrates an easy method to predict the dynamics of VSAs by combining VSA hydrology with existing SCS-CN runoff equation. In this method, TWI was used in combination with land-use to define the CN values. The simulated results showed that in regions dominated by saturation excess runoff process, AGNPS-VSA model provides more realistic spatial distribution of runoff generating areas than the AGNPS model based on traditional SCS–CN method. This research will help to locate VSAs for applying targeted BMPs to control non-point source pollution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".