Determining the Influence of Land Use Change and Soil Heterogeneities on Discharge, Sediment and Phosphorus
Bibliographic record
Abstract
Southern Quebec’s Missisquoi Bay, a freshwater body in the northeastern portion of Lake Champlain is threatened by algal blooms arising from excess nutrient inputs contributed by agricultural watersheds which have their outlets in the bay. A version of the Soil and Water Assessment Tool (SWAT) model, calibrated in a previous study to estimate annual runoff, sediment and total phosphorus (TP) fluxes from the Castor subwatershed into the Pike River watershed, which, in turn, flows into the Missisquoi Bay, used static landscapes and single land uses to arrive at its predictions. However, in reality, farmers do rotate crops. Therefore, the present study’s objective was to quantify the impact of soil heterogeneities on land use change patterns in the Castor subwatershed from 1999 to 2011. Data from a 24-point soil survey within the Castor subwatershed were partitioned and regionalized into 5, 10, 15, 20 and 24 heterogeneous regions or configurations. Using the standard soils map (with mean properties) employed in several prior studies in the subwatershed, a sixth configuration termed “Reference,” was also developed. All 6 configurations were factorially combined with either 1999 or 2011 land use data to yield 12 different versions of the SWAT model and quantify the heterogeneities and uncertainty of soil properties on land use change. For hydrology, it was discovered that there were no marked differences in the predictions, which was attributable to the use the SCS-CN subroutine which masks the physical properties of soil parameters within the same hydrologic group. We evaluated all the models for two periods i.e. 1991-1999 and 2000-2007. All the 1999 land use SWAT configurations underestimated runoff, sediment and TP whereas all the 2011 land use SWAT set ups gave higher and more accurate values. For both land use periods, the 5 Region models both showed higher and more accurate estimates, than those set ups with a greater number of regions, but were similar in accuracy to the Reference model set-ups. Since the 5-region configurations showed the highest within-zone heterogeneity, it can be concluded that having many regions (many sampling points as regions) does not necessarily increase SWAT’s prediction accuracy.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".